Showing posts with label AI. Show all posts
Showing posts with label AI. Show all posts
26 August 2026
#LangChain4j
Key Concepts
| S.No | Topic | Sub-Topics |
|---|---|---|
| 1 | LangChain4j | LangChain4j, Architecture, Features, Installation, Maven/Gradle Setup, Java 21 |
| 2 | LLM Fundamentals | Chat Models, Completion Models, Embedding Models, Language Models, Model Providers |
| 3 | Supported LLM Providers | OpenAI, Azure OpenAI, Google Gemini, Anthropic Claude, Ollama, Hugging Face |
| 4 | ChatLanguageModel | Model Configuration, API Keys, Temperature, Max Tokens, Streaming, Timeouts |
| 5 | AI Services |
@AiService
, Interface-Based AI, Dependency Injection, Configuration
|
| 6 | Prompt Engineering | Prompt Templates, Variables, System Messages, User Messages, Few-Shot Prompting |
| 7 | Chat Memory | MessageWindowChatMemory, TokenWindowChatMemory, Persistent Memory, Session Management |
| 8 | Structured Outputs | JSON Responses, POJOs, Enums, Records, Validation, Parsing |
| 9 | Embeddings | Embedding Models, Vector Generation, Similarity Search, Embedding Store |
| 10 | Vector Databases | pgvector, ChromaDB, Milvus, Qdrant, Pinecone Integration |
| 11 | Document Processing | PDF, DOCX, TXT, HTML, Markdown, Apache Tika Integration |
| 12 | RAG Fundamentals | Retrieval Pipeline, Chunking, Metadata, Context Injection |
| 13 | RAG Implementation | Embedding Store, Retriever, Prompt Augmentation, Citation Support |
| 14 | Tool Calling |
@Tool
, Function Calling, External APIs, Database Access, Custom Tools
|
| 15 | MCP Integration | MCP Client, MCP Tools, MCP Resources, MCP Prompt Integration |
| 16 | Streaming Responses | Token Streaming, StreamingChatModel, Server-Sent Events (SSE), WebSocket |
| 17 | AI Moderation | Input Validation, Output Validation, Content Moderation, Guardrails |
| 18 | Spring Boot Integration | Spring AI vs LangChain4j, REST APIs, Configuration, Dependency Injection |
| 19 | Conversational AI | Multi-turn Chat, Context Management, Session Handling, Personalization |
| 20 | Agents | Agent Concepts, Planning, Tool Selection, Reflection, Autonomous Execution |
| 21 | Multi-Agent Systems | Agent Collaboration, Delegation, Workflow Coordination |
| 22 | Observability | Logging, Metrics, Tracing, LangSmith, OpenTelemetry |
| 23 | Testing | Unit Testing, Mock Models, Integration Testing, Prompt Testing |
| 24 | Security | API Security, Authentication, Prompt Injection Defense, Secret Management |
| 25 | Performance Optimization | Caching, Retry Policies, Parallel Calls, Token Optimization |
| 26 | Deployment | Docker, Kubernetes, Azure, AWS, CI/CD |
| 27 | Enterprise Applications | AI Copilot, Knowledge Base, Customer Support, Banking Assistant |
| 28 | Real-Time Projects | Enterprise RAG, SQL Assistant, Document Chatbot, AI Code Assistant |
| 29 | Advanced Topics | Custom Components, Multi-Modal AI, Hybrid Search, Advanced RAG |
| 30 | Interview Preparation | Architecture, APIs, Design Patterns, Best Practices, Interview Questions |
Interview question
| What is LangChain4j? |
| Why is LangChain4j used in Java applications? |
| What are the major components of LangChain4j? |
| How is LangChain4j different from LangChain? |
| What are the main use cases of LangChain4j? |
| How does LangChain4j integrate with Spring Boot? |
| What LLM providers are supported by LangChain4j? |
| How does LangChain4j abstract different LLM providers? |
| What is ChatLanguageModel in LangChain4j? |
| What is StreamingChatLanguageModel? |
| What is the difference between ChatLanguageModel and StreamingChatLanguageModel? |
| How do you configure an OpenAI model in LangChain4j? |
| How do you integrate Azure OpenAI with LangChain4j? |
| How do you integrate Ollama with LangChain4j? |
| How do you configure model temperature in LangChain4j? |
| What are max tokens and token limits? |
| How do you configure timeout and retry for an LLM? |
| How do you handle LLM API failures? |
| How do you switch between different LLM providers? |
| How do you manage API keys securely in LangChain4j? |
| What is a prompt in LangChain4j? |
| What is the difference between system, user, and AI messages? |
| How do you create a system message? |
| How do you create a user message? |
| How do you create an AI message? |
| What is a prompt template? |
| How do you create dynamic prompts? |
| How do you pass variables into prompts? |
| How do you implement few-shot prompting? |
| How do you prevent prompt injection? |
| How do you optimize prompts for production applications? |
| What is an AI Service in LangChain4j? |
| What is the purpose of @AiService? |
| How do you create an AI Service? |
| How does LangChain4j generate an implementation for an AI Service interface? |
| How do you define prompts inside an AI Service? |
| How do you pass method parameters to an AI Service? |
| How do you return structured objects from an AI Service? |
| How do you configure chat memory in an AI Service? |
| How do you integrate tools with an AI Service? |
| How do you integrate a content retriever with an AI Service? |
| What is ChatMemory? |
| Why is chat memory required? |
| What is MessageWindowChatMemory? |
| What is TokenWindowChatMemory? |
| What is the difference between MessageWindowChatMemory and TokenWindowChatMemory? |
| How does LangChain4j manage conversation history? |
| How do you implement persistent chat memory? |
| How do you store chat memory in a database? |
| How do you manage memory for multiple users? |
| How do you manage session-specific memory? |
| How do you prevent chat memory from exceeding the context window? |
| What is long-term memory in an AI application? |
| How do you implement long-term memory with LangChain4j? |
| What are embeddings? |
| Why are embeddings required for RAG? |
| What is EmbeddingModel? |
| How does LangChain4j generate embeddings? |
| What is embedding dimensionality? |
| How does embedding similarity work? |
| What is cosine similarity? |
| How do you select an embedding model? |
| How do you generate embeddings in batches? |
| How do you optimize embedding generation cost? |
| What is a VectorStore or EmbeddingStore? |
| Why is a vector database required? |
| Which vector databases are supported by LangChain4j? |
| How do you use PGVector with LangChain4j? |
| How do you integrate Pinecone with LangChain4j? |
| How do you integrate Milvus with LangChain4j? |
| How do you use an in-memory embedding store? |
| How do you persist embeddings? |
| What metadata can be stored with embeddings? |
| How do you filter vector search results using metadata? |
| What is RAG? |
| Why is RAG important for enterprise applications? |
| What is the RAG pipeline in LangChain4j? |
| What are the ingestion and retrieval phases of RAG? |
| How do you load documents in LangChain4j? |
| What is a Document in LangChain4j? |
| What is DocumentLoader? |
| How do you load PDF documents? |
| How do you load documents from URLs? |
| How do you attach metadata to documents? |
| What is a DocumentParser? |
| What is a DocumentSplitter? |
| Why is document splitting required? |
| How do you choose an appropriate chunk size? |
| What is chunk overlap? |
| How does chunk overlap affect retrieval? |
| How do you preserve document metadata during chunking? |
| How do you handle large PDF documents in RAG? |
| What is ContentRetriever? |
| What is EmbeddingStoreContentRetriever? |
| How does a content retriever work? |
| How do you configure top-K retrieval? |
| What is a minimum similarity score? |
| How do you implement metadata filtering in retrieval? |
| How do you implement custom retrieval logic? |
| What is hybrid search? |
| How do you combine keyword and vector search? |
| What is reranking? |
| Why is reranking useful in RAG? |
| How do you reduce irrelevant context in RAG? |
| What is query transformation? |
| What is multi-query retrieval? |
| How do you handle ambiguous user queries in RAG? |
| How do you prevent hallucinations in RAG? |
| How do you implement source citations in RAG responses? |
| How do you evaluate RAG retrieval quality? |
| What is a Tool in LangChain4j? |
| What is the @Tool annotation? |
| How do you create a custom tool? |
| How does an LLM decide which tool to call? |
| What information should be included in a tool description? |
| How do you pass parameters to a tool? |
| How do you return tool results? |
| How do you handle tool execution errors? |
| How do you register multiple tools? |
| How do you implement dynamic tools? |
| How do you restrict tools available to an AI Service? |
| How do you secure tool execution? |
| How do you prevent unauthorized tool calls? |
| What is function calling? |
| How does tool calling differ from normal text generation? |
| How do you implement database tools? |
| How do you implement REST API tools? |
| How do you implement business logic as an AI tool? |
| What is an AI Agent? |
| How is an AI Agent different from a chatbot? |
| How do agents use tools? |
| How does an agent perform multi-step tasks? |
| What is agent state? |
| How do you manage agent memory? |
| How do you implement agent planning? |
| How do you implement sequential agent workflows? |
| How do you implement conditional agent workflows? |
| How do you implement parallel agent execution? |
| How do you implement human-in-the-loop workflows? |
| What are common agent failure modes? |
| How do you prevent infinite agent loops? |
| How do you limit agent tool calls? |
| What is MCP? |
| How does LangChain4j support MCP? |
| What is an MCP Client? |
| What is an MCP Server? |
| How does MCP tool discovery work? |
| How do you connect LangChain4j to an MCP server? |
| How do MCP resources differ from MCP tools? |
| How do you secure MCP tool access? |
| What are guardrails? |
| Why are guardrails important in enterprise AI? |
| How do you implement input validation? |
| How do you validate LLM output? |
| How do you protect against prompt injection? |
| How do you prevent sensitive data leakage? |
| How do you implement PII protection? |
| What are structured outputs? |
| How do you map LLM responses to Java POJOs? |
| How do you enforce JSON output from an LLM? |
| How do you validate structured AI responses? |
| How does streaming work in LangChain4j? |
| How do you stream tokens to a Spring Boot REST API? |
| How do you implement asynchronous AI processing? |
| How do you handle backpressure in streaming applications? |
| How do you integrate LangChain4j with Spring Boot? |
| How do you configure LangChain4j beans? |
| How do you manage LangChain4j configuration using application.yml? |
| How do you expose LangChain4j functionality through REST APIs? |
| How do you integrate LangChain4j with PostgreSQL? |
| How do you implement enterprise multi-tenant RAG? |
| How do you isolate vector data between tenants? |
| How do you implement document-level authorization in RAG? |
| How do you monitor token usage? |
| How do you optimize LLM latency? |
| How do you reduce LLM API costs? |
| How do you implement caching for LLM responses? |
| How do you handle rate limits from LLM providers? |
| How do you implement retry and fallback strategies? |
| How do you test LangChain4j applications? |
| How do you mock an LLM during unit testing? |
| How do you test RAG pipelines? |
| How do you evaluate agent performance? |
| How do you test tool calling? |
| How do you troubleshoot incorrect RAG responses? |
| How do you troubleshoot poor retrieval quality? |
| How do you troubleshoot excessive token usage? |
| How do you design a production-ready LangChain4j architecture? |
| How would you design an enterprise RAG system using LangChain4j? |
| How would you design a LangChain4j AI Agent with multiple tools? |
| How would you build a secure and scalable LangChain4j application? |
| What are the most important LangChain4j design patterns for production? |
Related Topics
#LlamaIndex
Key Concepts
| S.No | Topic | Sub-Topics |
|---|---|---|
| 1 | LlamaIndex | Architecture, Core Concepts, Components, Workflows, Data Flow, Installation, Project Structure |
| 2 | LlamaIndex Setup | Python Environment, Package Installation, Configuration, API Keys, Settings, Logging, Dependencies |
| 3 | Documents | Document Object, Metadata, IDs, Text Extraction, Transformation, Custom Metadata, Document Management |
| 4 | Readers | SimpleDirectoryReader, PDF Reader, CSV Reader, JSON Reader, HTML Reader, Database Reader, Custom Reader |
| 5 | Nodes | TextNode, Node IDs, Metadata, Relationships, Parent-Child Nodes, Custom Nodes, Node Parsing |
| 6 | Node Parsing | SentenceSplitter, TokenTextSplitter, Semantic Splitting, Hierarchical Parsing, Metadata Extraction, Chunk Overlap, Custom Parsers |
| 7 | Metadata | Document Metadata, Node Metadata, Metadata Extraction, Metadata Filtering, Automatic Metadata, Metadata Templates, Metadata Propagation |
| 8 | Embeddings | Embedding Models, OpenAI Embeddings, Hugging Face Embeddings, Local Embeddings, Batch Embeddings, Similarity, Configuration |
| 9 | Vector Stores | VectorStore, Chroma, Pinecone, Qdrant, Milvus, FAISS, PGVector |
| 10 | Storage | StorageContext, Document Store, Index Store, Vector Store, Persistence, Loading Indexes, Remote Storage |
| 11 | Indexes | VectorStoreIndex, SummaryIndex, TreeIndex, KeywordTableIndex, KnowledgeGraphIndex, Property Indexes, Index Selection |
| 12 | VectorStoreIndex | Index Construction, Node Insertion, Embeddings, Persistence, Loading, Filtering, Retrieval Configuration |
| 13 | Retrievers | Vector Retriever, Keyword Retriever, BM25, Hybrid Retrieval, Metadata Filters, Similarity Top-K, Custom Retrievers |
| 14 | Query Engine | QueryEngine, Query Pipeline, Retrieval, Response Synthesis, Similarity Threshold, Streaming, Async Queries |
| 15 | Response Synthesis | Compact, Refine, Tree Summarize, Simple Summarize, Citation Responses, Structured Responses, Custom Synthesis |
| 16 | RAG Fundamentals | Ingestion, Chunking, Embedding, Indexing, Retrieval, Generation, End-to-End RAG |
| 17 | Advanced RAG | Hybrid Search, Query Rewriting, Reranking, Metadata Filtering, Contextual Retrieval, Recursive Retrieval, Fusion Retrieval |
| 18 | Reranking | Cross-Encoder, Cohere Reranker, Sentence Transformers, Top-K Retrieval, Score Thresholds, Reciprocal Rank Fusion, Custom Rerankers |
| 19 | Query Transformation | Query Expansion, Query Rewriting, Sub-Question Generation, HyDE, Multi-Query Retrieval, Routing, Query Decomposition |
| 20 | Chat Engines | Chat Engine, Conversational Context, Memory, Context Management, Streaming Chat, Async Chat, Custom Chat Behavior |
| 21 | Memory | Chat Memory, Short-Term Memory, Long-Term Memory, Token Limits, Memory Blocks, Retrieval-Based Memory, Persistent Memory |
| 22 | Agents | Agent Architecture, Tools, Tool Calling, ReAct Agents, Function Calling, Agent Memory, Multi-Step Reasoning |
| 23 | Workflows | Workflow Concepts, Events, Steps, State, Async Workflows, Branching, Parallel Execution, Error Handling |
| 24 | Tools | FunctionTool, QueryEngineTool, Custom Tools, API Tools, Database Tools, Tool Metadata, Tool Selection |
| 25 | LLM Integration | OpenAI, Anthropic, Gemini, Ollama, Hugging Face, Local LLMs, Custom LLM Integration |
| 26 | Structured Outputs | Pydantic Models, Structured Prediction, JSON Output, Schema Validation, Extraction, Response Parsing, Typed Outputs |
| 27 | Data Extraction | LLM Extraction, Structured Extraction, Metadata Extraction, Table Extraction, Entity Extraction, Relation Extraction, Validation |
| 28 | Evaluation | Retrieval Evaluation, Response Evaluation, Faithfulness, Relevance, Correctness, Context Evaluation, RAG Benchmarking |
| 29 | Production RAG | Caching, Observability, Tracing, Logging, Security, Latency Optimization, Cost Optimization |
| 30 | Production Deployment | FastAPI, Docker, Kubernetes, Vector DB Deployment, Scaling, Monitoring, CI/CD |
| 31 | Advanced Architecture | Multi-Index RAG, Agentic RAG, Multimodal RAG, Knowledge Graphs, Hybrid Architecture, Distributed Ingestion, Enterprise Architecture |
| 32 | Capstone Project | Enterprise RAG, PDF Ingestion, Intelligent Chunking, Vector Database, Hybrid Retrieval, Reranking, Conversational Memory |
Interview question
| What is LlamaIndex and what problem does it solve? |
| Why is LlamaIndex commonly used for RAG applications? |
| What are the core components of LlamaIndex? |
| What is the architecture of LlamaIndex? |
| How does LlamaIndex differ from LangChain? |
| What are Documents in LlamaIndex? |
| What are Nodes in LlamaIndex? |
| What is the difference between a Document and a Node? |
| What is SimpleDirectoryReader? |
| How do you load PDF files using LlamaIndex? |
| How do you load CSV files using LlamaIndex? |
| How do you load JSON files using LlamaIndex? |
| How do you load HTML documents using LlamaIndex? |
| How can you create a custom data reader in LlamaIndex? |
| What is NodeParser in LlamaIndex? |
| What is SentenceSplitter? |
| How does SentenceSplitter split documents? |
| What is chunk size in LlamaIndex? |
| What is chunk overlap? |
| How do you choose an appropriate chunk size? |
| What is a TextNode? |
| How does metadata work with Nodes? |
| What are node relationships? |
| What is parent-child node relationship? |
| Why is metadata important in RAG applications? |
| How do you add custom metadata to documents? |
| What are embeddings in LlamaIndex? |
| Why are embeddings required for semantic search? |
| How do you configure an embedding model in LlamaIndex? |
| How do you use OpenAI embeddings with LlamaIndex? |
| How can you use Hugging Face embeddings with LlamaIndex? |
| How can you use local embedding models with LlamaIndex? |
| What is VectorStoreIndex? |
| How do you create a VectorStoreIndex? |
| What happens internally when a VectorStoreIndex is created? |
| What is StorageContext? |
| What is the purpose of a document store? |
| What is the purpose of an index store? |
| What is a vector store? |
| Which vector databases can be integrated with LlamaIndex? |
| How do you integrate Chroma with LlamaIndex? |
| How do you integrate Pinecone with LlamaIndex? |
| How do you integrate Qdrant with LlamaIndex? |
| How do you integrate Milvus with LlamaIndex? |
| How do you integrate PostgreSQL/PGVector with LlamaIndex? |
| What is FAISS and how can it be used with LlamaIndex? |
| What is persistence in LlamaIndex? |
| How do you persist an index to disk? |
| How do you load a persisted index? |
| What is a Retriever in LlamaIndex? |
| What is VectorIndexRetriever? |
| How does similarity search work in LlamaIndex? |
| What is top-k retrieval? |
| How do you configure similarity_top_k? |
| What is metadata filtering? |
| How do metadata filters improve retrieval? |
| What is hybrid search? |
| How do you implement hybrid retrieval in LlamaIndex? |
| What is BM25 retrieval? |
| What is a QueryEngine? |
| How do you create a QueryEngine? |
| What happens internally when a query is sent to a QueryEngine? |
| What is ResponseSynthesizer? |
| What are the different response synthesis strategies? |
| What is the Compact response synthesis mode? |
| What is the Refine response synthesis mode? |
| What is Tree Summarize response synthesis? |
| How do you generate citation-based responses? |
| What is RAG in LlamaIndex? |
| How do you build a basic RAG pipeline using LlamaIndex? |
| What are the major stages of a LlamaIndex RAG pipeline? |
| How does LlamaIndex handle document ingestion? |
| How does LlamaIndex perform retrieval? |
| How does LlamaIndex use an LLM after retrieval? |
| What are common RAG failure modes in LlamaIndex? |
| How do you improve retrieval accuracy in LlamaIndex? |
| What is reranking in LlamaIndex? |
| Why is reranking useful in RAG? |
| How do you integrate a reranker with LlamaIndex? |
| What is Reciprocal Rank Fusion? |
| What is query transformation? |
| What is query rewriting? |
| What is query expansion? |
| What is HyDE in LlamaIndex? |
| What is sub-question query decomposition? |
| What is SubQuestionQueryEngine? |
| What is RouterQueryEngine? |
| How does query routing work in LlamaIndex? |
| What is RecursiveRetriever? |
| What is AutoMergingRetriever? |
| What is a ChatEngine? |
| What is the difference between QueryEngine and ChatEngine? |
| How does conversational memory work in LlamaIndex? |
| What is ChatMemory? |
| How do you maintain conversation history in LlamaIndex? |
| What are Agents in LlamaIndex? |
| What is a ReAct agent? |
| How does tool calling work in LlamaIndex agents? |
| What is FunctionTool? |
| What is QueryEngineTool? |
| How can an agent use a RAG query engine as a tool? |
| What are Workflows in LlamaIndex? |
| How are events and steps used in Workflows? |
| How do you build an asynchronous workflow? |
| How do you handle errors in LlamaIndex workflows? |
| How do you evaluate a LlamaIndex RAG application? |
| What is faithfulness evaluation? |
| What is retrieval relevance evaluation? |
| How do you optimize LlamaIndex applications for latency and cost? |
| How do you deploy a LlamaIndex RAG application in production? |
Related Topics
#AI Core
Key Concepts
| S.No | Topic | Sub-Topics |
|---|---|---|
| 1 | AI Fundamentals | AI definition, AI types, Narrow AI, General AI, Generative AI, Predictive AI, AI applications |
| 2 | AI & ML Foundations | AI vs ML, Supervised learning, Unsupervised learning, Semi-supervised learning, Reinforcement learning, Training, Inference |
| 3 | Data Fundamentals | Structured data, Unstructured data, Data collection, Data preprocessing, Data quality, Features, Labels |
| 4 | Mathematics for AI | Linear algebra, Vectors, Matrices, Matrix operations, Probability, Statistics, Calculus basics |
| 5 | Probability & Statistics | Probability distributions, Conditional probability, Bayes theorem, Mean, Variance, Standard deviation, Correlation |
| 6 | Machine Learning Fundamentals | Regression, Classification, Clustering, Feature engineering, Training, Validation, Prediction |
| 7 | ML Algorithms | Linear regression, Logistic regression, Decision trees, Random forest, SVM, KNN, Naive Bayes |
| 8 | Ensemble Learning | Bagging, Boosting, Random forest, AdaBoost, Gradient boosting, XGBoost, LightGBM |
| 9 | Model Evaluation | Train-test split, Cross-validation, Accuracy, Precision, Recall, F1-score, ROC-AUC |
| 10 | Feature Engineering | Feature selection, Feature extraction, Encoding, Scaling, Normalization, Missing values, Outliers |
| 11 | Unsupervised Learning | K-Means, Hierarchical clustering, DBSCAN, PCA, Dimensionality reduction, Anomaly detection, Cluster evaluation |
| 12 | Deep Learning Fundamentals | Neural networks, Neurons, Layers, Weights, Bias, Activation functions, Forward propagation |
| 13 | Neural Network Training | Loss functions, Backpropagation, Gradient descent, Learning rate, Optimizers, Batch size, Epochs |
| 14 | Deep Learning Architectures | CNN, RNN, LSTM, GRU, Autoencoder, Transformer, Attention |
| 15 | Computer Vision | Image classification, Object detection, Image segmentation, OCR, Image embeddings, Vision transformers, Image generation |
| 16 | NLP Fundamentals | Text preprocessing, Tokenization, Stemming, Lemmatization, N-grams, Text classification, Named Entity Recognition |
| 17 | Embeddings | Word embeddings, Sentence embeddings, Document embeddings, Vector representation, Cosine similarity, Semantic search, Embedding models |
| 18 | Generative AI | Generative models, LLMs, Text generation, Image generation, Audio generation, Video generation, Multimodal AI |
| 19 | LLM Fundamentals | Transformer, Tokens, Context window, Attention, Parameters, Pre-training, Inference |
| 20 | Prompt Engineering | Zero-shot, Few-shot, Role prompting, Prompt templates, Structured prompts, Prompt chaining, Output constraints |
| 21 | RAG | Document ingestion, Chunking, Embeddings, Vector database, Retrieval, Reranking, Context generation |
| 22 | Vector Databases | Vector indexing, Similarity search, ANN, Metadata filtering, Hybrid search, Reranking, Vector DB selection |
| 23 | AI Agents | Agent architecture, Planning, Tool calling, Function calling, Memory, ReAct, Multi-agent systems |
| 24 | AI Tools & MCP | Tool definitions, API tools, Database tools, Function execution, MCP concepts, MCP servers, MCP clients |
| 25 | Fine-Tuning | Instruction tuning, Dataset preparation, Supervised fine-tuning, LoRA, QLoRA, PEFT, Fine-tuning evaluation |
| 26 | AI Evaluation | Model evaluation, RAG evaluation, Faithfulness, Relevance, Groundedness, LLM-as-a-judge, Benchmarking |
| 27 | AI Security | Prompt injection, Jailbreaking, Data leakage, PII protection, Tool security, Output validation, Guardrails |
| 28 | AI MLOps / LLMOps | Model deployment, Model registry, Versioning, Monitoring, Logging, Drift detection, Experiment tracking |
| 29 | AI Performance & Cost | Model selection, Quantization, Caching, Batching, Token optimization, Latency optimization, Cost optimization |
| 30 | Production AI Architecture | AI application architecture, RAG architecture, Agent architecture, Data layer, Model layer, Security layer, Monitoring & deployment |
Interview question
| What is Artificial Intelligence (AI)? |
| What is Machine Learning (ML)? |
| What is Deep Learning? |
| What is Generative AI? |
| What is an LLM? |
| What is the difference between AI, ML, Deep Learning, and Generative AI? |
| What is ChatGPT? |
| What is an AI model? |
| What is the difference between a traditional AI model and an LLM? |
| What are the major components of a Generative AI system? |
| What is Natural Language Processing (NLP)? |
| What is Natural Language Understanding (NLU)? |
| What is Natural Language Generation (NLG)? |
| What is a Transformer architecture? |
| What is self-attention? |
| What is multi-head attention? |
| What is an encoder and decoder in a Transformer? |
| Why are Transformers widely used in LLMs? |
| What is tokenization? |
| What is a token in an LLM? |
| What is an embedding? |
| What is a vector embedding? |
| What is a context window? |
| What happens when an LLM exceeds its context window? |
| What is model training? |
| What is model inference? |
| What is pre-training? |
| What is fine-tuning? |
| What is instruction tuning? |
| What is RLHF? |
| What is supervised fine-tuning? |
| What is prompt engineering? |
| Why is prompt engineering important? |
| What are the main components of a good prompt? |
| What is a system prompt? |
| What is a user prompt? |
| What is a developer instruction? |
| What is zero-shot prompting? |
| What is one-shot prompting? |
| What is few-shot prompting? |
| What is role prompting? |
| What is instruction prompting? |
| What is contextual prompting? |
| What is prompt decomposition? |
| What is prompt chaining? |
| What is structured prompting? |
| What is a prompt template? |
| What is dynamic prompt generation? |
| What is chain-of-thought prompting? |
| What is reasoning prompting? |
| What is ReAct prompting? |
| What is self-consistency prompting? |
| What is self-reflection prompting? |
| What is tree-of-thought prompting? |
| What is least-to-most prompting? |
| What is retrieval-augmented prompting? |
| What is grounding in Generative AI? |
| What is hallucination in an LLM? |
| Why do LLMs hallucinate? |
| How can prompt engineering reduce hallucinations? |
| What is prompt injection? |
| What is indirect prompt injection? |
| How can prompt injection attacks be prevented? |
| What is jailbreak prompting? |
| What are AI guardrails? |
| What is output validation in Generative AI? |
| What is structured output? |
| How can an LLM generate valid JSON? |
| What is function calling? |
| What is tool calling in an LLM? |
| How does an AI model decide when to call a tool? |
| What is the difference between function calling and tool calling? |
| What is RAG? |
| Why is RAG used with LLMs? |
| What is the difference between RAG and fine-tuning? |
| What are the main components of a RAG pipeline? |
| What is document chunking? |
| How do you choose the right chunk size? |
| What is semantic search? |
| What is a vector database? |
| What is similarity search? |
| What is hybrid search? |
| What is reranking in RAG? |
| What is Agentic RAG? |
| What is an AI Agent? |
| What is the difference between an LLM application and an AI Agent? |
| What are the core components of an AI Agent? |
| What is agent memory? |
| What is short-term memory in an AI Agent? |
| What is long-term memory in an AI Agent? |
| What is agent planning? |
| What is an autonomous AI Agent? |
| What is a multi-agent system? |
| What is the role of prompts in AI Agents? |
| How do you design prompts for reliable AI Agents? |
| How do you evaluate an LLM response? |
| What metrics are used to evaluate Generative AI applications? |
| How do you evaluate RAG systems? |
| How do you reduce LLM latency and token costs? |
| How do you design production-ready LLM applications? |
| What are the major security challenges in Generative AI? |
| What is responsible AI? |
| How would you design an enterprise-grade Agentic AI solution? |
| How would you combine Prompt Engineering, RAG, Tools, Memory, and Agents in a real-world application? |
Related Topics
22 August 2026
#LangChain
Key Concepts
| S.No | Topic | Sub-Topics |
|---|---|---|
| 1 | LangChain | LangChain architecture, LLMs, ChatModels, Prompts, Messages, Chains, Runnables |
| 2 | Models & Providers | OpenAI, Anthropic, Google Gemini, Ollama, model configuration, temperature, token limits |
| 3 | Prompt Engineering | Prompt templates, ChatPromptTemplate, system messages, variables, few-shot prompts, output control, prompt composition |
| 4 | Messages & Chat | HumanMessage, AIMessage, SystemMessage, message history, message placeholders, multimodal messages, message filtering |
| 5 | Output Parsers | String output, JSON output, Pydantic parser, structured output, validation, error handling, schema design |
| 6 | Runnables | Runnable interface, RunnableSequence, RunnableParallel, RunnableLambda, RunnablePassthrough, RunnableBranch, RunnableConfig |
| 7 | LCEL | Pipe operator, chain composition, parallel execution, branching, streaming, batch execution, reusable chains |
| 8 | Chains | LLM chains, prompt + model chains, sequential chains, conditional chains, transformation chains, custom chains, chain debugging |
| 9 | Tool Calling | Tool definition, tool schemas, function calling, model binding, tool invocation, tool results, tool errors |
| 10 | Agents | Agent architecture, agent loop, tool selection, AgentExecutor, ReAct, custom agents, agent errors |
| 11 | Agentic Workflows | Planning, reasoning, tool execution, routing, decision making, retries, human-in-the-loop |
| 12 | Memory | Conversation memory, message history, RunnableWithMessageHistory, persistent memory, session management, memory limits, summarization |
| 13 | Document Loaders | PDF loader, web loader, text loader, CSV loader, JSON loader, directory loader, custom loaders |
| 14 | Document Processing | Documents, metadata, cleaning, normalization, splitting strategy, duplicate removal, document pipelines |
| 15 | Text Splitters | RecursiveCharacterTextSplitter, token splitting, semantic splitting, chunk size, overlap, custom splitting, chunk optimization |
| 16 | Embeddings | Embedding models, vector representation, similarity, OpenAI embeddings, local embeddings, batch embeddings, embedding evaluation |
| 17 | Vector Stores | Chroma, FAISS, Pinecone, Milvus, Weaviate, PGVector, vector-store abstraction |
| 18 | Retrieval | Similarity search, MMR, metadata filtering, top-k, retriever interface, contextual retrieval, custom retrievers |
| 19 | RAG Fundamentals | RAG architecture, indexing pipeline, retrieval pipeline, prompt construction, context injection, answer generation, citations |
| 20 | Advanced RAG | Multi-query retrieval, contextual compression, parent-child retrieval, hybrid search, reranking, query transformation, self-query retrieval |
| 21 | Retrieval Quality | Precision, recall, relevance, chunk evaluation, retrieval evaluation, hallucination detection, RAG benchmarking |
| 22 | Structured Data & SQL | SQLDatabase, SQL agents, database tools, natural-language queries, query validation, schema awareness, SQL security |
| 23 | LangChain + APIs | REST APIs, custom tools, external services, authentication, request handling, response parsing, API error handling |
| 24 | LangGraph Integration | Graph concepts, state, nodes, edges, conditional routing, persistence, human approval, LangChain vs LangGraph |
| 25 | Streaming | Token streaming, Runnable streaming, agent streaming, custom events, async streaming, UI integration, real-time responses |
| 26 | Async & Performance | Async invoke, parallel execution, batching, concurrency, caching, token optimization, latency optimization |
| 27 | Observability | LangSmith, tracing, runs, prompts, tool traces, debugging, evaluation datasets |
| 28 | Production RAG Application | Architecture, ingestion service, retrieval service, LLM service, API layer, security, deployment |
| 29 | Production Agents | Agent architecture, tool security, guardrails, retries, timeouts, state persistence, failure recovery |
| 30 | Expert Capstone | Production RAG, agentic RAG, multi-tool agent, LangGraph workflow, evaluation, monitoring, deployment |
Interview question
| What is LangChain? |
| Why was LangChain created? |
| What are the main components of LangChain? |
| What problems does LangChain solve? |
| What are the core abstractions in LangChain? |
| What is an LLM in LangChain? |
| What is a Chat Model in LangChain? |
| What is the difference between an LLM and a Chat Model? |
| What is a prompt in LangChain? |
| What is a PromptTemplate? |
| What is a ChatPromptTemplate? |
| What is a SystemMessage in LangChain? |
| What is a HumanMessage in LangChain? |
| What is an AIMessage in LangChain? |
| What is message history in LangChain? |
| What is output parsing in LangChain? |
| What is a StrOutputParser? |
| What is structured output in LangChain? |
| How do you generate JSON output using LangChain? |
| How do you validate LLM output in LangChain? |
| What is an Embedding Model? |
| Why are embeddings used in LangChain? |
| What is a vector embedding? |
| What is semantic similarity? |
| What is a Vector Store? |
| What vector stores are supported by LangChain? |
| What is Chroma in LangChain? |
| What is FAISS? |
| What is Pinecone? |
| What is Milvus? |
| What is Weaviate? |
| What is the difference between a vector database and a relational database? |
| How does LangChain perform similarity search? |
| What is similarity search with score? |
| What is Maximum Marginal Relevance? |
| Why is MMR useful in RAG applications? |
| What is a retriever in LangChain? |
| What is the difference between a retriever and a vector store? |
| How do you convert a vector store into a retriever? |
| What is a MultiQueryRetriever? |
| What is a Document in LangChain? |
| What are the main fields of a LangChain Document? |
| What is document metadata? |
| Why is metadata important in RAG? |
| What is a Document Loader? |
| What document loaders are available in LangChain? |
| How do you load a PDF using LangChain? |
| How do you load a text file using LangChain? |
| How do you load CSV files using LangChain? |
| How do you load JSON files using LangChain? |
| How do you load web pages using LangChain? |
| How do you load documents from a directory? |
| What is DirectoryLoader? |
| What is PyPDFLoader? |
| What is WebBaseLoader? |
| What is UnstructuredLoader? |
| What is a RecursiveCharacterTextSplitter? |
| Why is recursive text splitting preferred? |
| What is chunk size? |
| What is chunk overlap? |
| What is text splitting in LangChain? |
| Why do documents need to be split into chunks? |
| How do you choose an appropriate chunk size? |
| How does chunk overlap affect retrieval? |
| What happens if the chunk size is too small? |
| What happens if the chunk size is too large? |
| What is token-based text splitting? |
| What is character-based text splitting? |
| What is recursive splitting? |
| What is MarkdownHeaderTextSplitter? |
| What is HTMLHeaderTextSplitter? |
| How do you split code using LangChain? |
| How do you preserve metadata during document splitting? |
| How can you optimize document chunking for RAG? |
| What is parent-child document retrieval? |
| What is contextual chunking? |
| What is semantic chunking? |
| What is the difference between fixed-size and semantic chunking? |
| How does chunking affect embedding quality? |
| How does chunking affect retrieval accuracy? |
| What is RAG? |
| How does Retrieval-Augmented Generation work? |
| What are the main stages of a LangChain RAG pipeline? |
| What is the difference between indexing and retrieval? |
| How do you build a basic RAG application using LangChain? |
| What is a RetrievalQA chain? |
| What replaced older RetrievalQA patterns in modern LangChain? |
| What is create_retrieval_chain? |
| What is create_stuff_documents_chain? |
| What is the StuffDocumentsChain approach? |
| What is MapReduce document processing? |
| What is Refine document processing? |
| What is the difference between Stuff, MapReduce, and Refine? |
| How does LangChain handle retrieved documents? |
| How do you pass retrieved documents to an LLM? |
| How do you add citations to a RAG response? |
| How do you prevent hallucinations in RAG? |
| What are common RAG failure modes? |
| How do you improve RAG retrieval quality? |
| How do you evaluate a LangChain RAG application? |
| What is LCEL? |
| What does LCEL stand for? |
| Why was LCEL introduced? |
| What is a Runnable in LangChain? |
| What is RunnableSequence? |
| What is RunnableParallel? |
| What is RunnablePassthrough? |
| What is RunnableLambda? |
| What is RunnableBranch? |
| What does the pipe operator do in LCEL? |
| How do you compose multiple Runnable components? |
| What is the difference between Chain and Runnable? |
| Why is Runnable preferred in modern LangChain? |
| How do you invoke a Runnable? |
| What is the difference between invoke and run? |
| What is batch execution in LangChain? |
| What is stream execution in LangChain? |
| What is astream in LangChain? |
| How does async execution work in LangChain? |
| How do you handle parallel execution with LCEL? |
| What is an Agent in LangChain? |
| What is an AgentExecutor? |
| How does a LangChain agent work? |
| What is tool calling? |
| What is a Tool in LangChain? |
| How do you create a custom tool? |
| What is the @tool decorator? |
| How does an agent select a tool? |
| What is tool input schema? |
| How do you validate tool arguments? |
| What is the difference between a tool and a function? |
| What is function calling? |
| What is the difference between function calling and tool calling? |
| What is ReAct in LangChain? |
| How does the ReAct agent work? |
| What are the advantages of agents over chains? |
| What are the disadvantages of agents? |
| When should you use an agent instead of a chain? |
| How do you restrict an agent from using unauthorized tools? |
| How do you handle tool execution errors? |
| What is memory in LangChain? |
| Why is conversation memory required? |
| What is conversation history? |
| What is RunnableWithMessageHistory? |
| How do you maintain chat history in LangChain? |
| What is the difference between short-term and long-term memory? |
| How can conversation history exceed the context window? |
| How do you summarize conversation history? |
| How can vector stores be used as long-term memory? |
| How do you persist conversation history? |
| What is LangSmith? |
| Why is LangSmith used with LangChain? |
| What is tracing in LangSmith? |
| How do you trace a LangChain application? |
| What is observability in LLM applications? |
| How do you debug a LangChain chain? |
| How do you monitor token usage? |
| How do you monitor latency in LangChain? |
| How do you evaluate LLM responses? |
| What is an evaluation dataset? |
| What are common RAG evaluation metrics? |
| What is retrieval relevance? |
| What is answer correctness? |
| What is faithfulness in RAG evaluation? |
| How do you compare two prompts using LangSmith? |
| How do you integrate OpenAI with LangChain? |
| How do you integrate Anthropic with LangChain? |
| How do you integrate Google Gemini with LangChain? |
| How do you integrate Ollama with LangChain? |
| How do you use local LLMs with LangChain? |
| How do you configure API keys in LangChain? |
| How do you manage secrets securely in a LangChain application? |
| How do you configure temperature? |
| What is max_tokens? |
| How do model parameters affect LangChain applications? |
| How do you implement streaming responses in LangChain? |
| How do you stream tokens from an LLM? |
| How do you handle callbacks in LangChain? |
| What are callbacks used for? |
| How do you implement logging in LangChain? |
| How do you handle exceptions in LangChain? |
| How do you implement retries? |
| What is with_retry in LangChain? |
| How do you implement fallbacks? |
| What is with_fallbacks in LangChain? |
| How do you build a conversational RAG system? |
| How do you combine chat history with RAG? |
| What is history-aware retrieval? |
| What is create_history_aware_retriever? |
| How do you rewrite follow-up questions before retrieval? |
| How do you handle ambiguous user queries in RAG? |
| How do you implement metadata filtering in retrieval? |
| How do you retrieve documents based on user-specific permissions? |
| How do you implement hybrid search with LangChain? |
| What is keyword search? |
| What is BM25 retrieval? |
| How do you combine BM25 and vector search? |
| What is ensemble retrieval? |
| What is contextual compression retrieval? |
| What is ContextualCompressionRetriever? |
| What is a reranker? |
| Why is reranking useful in RAG? |
| How do you implement reranking in LangChain? |
| What is ParentDocumentRetriever? |
| When should you use ParentDocumentRetriever? |
| How do you secure a LangChain application? |
| What is prompt injection? |
| How can LangChain applications defend against prompt injection? |
| What is indirect prompt injection? |
| How do you prevent sensitive data leakage? |
| How do you restrict agent tool permissions? |
| How do you validate external tool outputs? |
| How do you implement guardrails around LangChain? |
| How do you prevent an agent from executing dangerous operations? |
| How do you implement human approval for agent actions? |
| How do you optimize LangChain application performance? |
| How do you reduce LLM token usage? |
| How do you reduce RAG latency? |
| How do you cache LLM responses? |
| What is caching in LangChain? |
| How do you batch multiple LLM requests? |
| How do you parallelize independent LangChain operations? |
| How do you optimize vector search? |
| How do you optimize embedding generation? |
| How do you design LangChain for production? |
| What are common LangChain production challenges? |
| How do you version prompts in production? |
| How do you test LangChain chains? |
| How do you unit test a LangChain application? |
| How do you mock an LLM during testing? |
| How do you test retrieval independently? |
| How do you test agent tool selection? |
| How do you handle LLM provider outages? |
| How do you design multi-model fallback architecture? |
| How do you deploy LangChain applications? |
| What is the difference between LangChain and LangGraph? |
| When should LangGraph be used instead of LangChain agents? |
| How does LangChain integrate with LangGraph? |
| What is a stateful agent workflow? |
| What is an AI workflow in LangChain? |
| What is the difference between deterministic workflows and agents? |
| How do you build multi-step workflows with LangChain? |
| How do you implement conditional execution? |
| How do you implement parallel branches? |
| How do you combine retrieval, tools, and LLM calls? |
| What is the difference between LangChain legacy APIs and modern LangChain APIs? |
| What are deprecated LangChain chains? |
| Why is LCEL important for modern LangChain development? |
| How do you migrate legacy chains to Runnable-based pipelines? |
| How do you migrate old agent implementations to modern agents? |
| How do LangChain packages separate integrations? |
| What is langchain-core? |
| What is langchain-community? |
| What is the purpose of provider-specific LangChain packages? |
| How do you keep LangChain dependencies maintainable in production? |
| Design a production-ready RAG application using LangChain. |
| Design a PDF question-answering system using LangChain. |
| Design a multi-document RAG system using LangChain. |
| Design a chatbot with persistent conversation history. |
| Design an enterprise document search system using LangChain. |
| Design a resume screening application using LangChain. |
| Design an agent that uses database and web-search tools. |
| Design a customer-support agent using LangChain. |
| Design a secure enterprise RAG system with document-level access control. |
| Design a scalable LangChain architecture for millions of documents. |
Related Topics
09 January 2026
#RAG
Key Concepts
| S.No | Topic | Sub-Topics |
|---|---|---|
| 1 | RAG | What is RAG, Why RAG, RAG vs LLM-only, RAG use cases, RAG limitations |
| 2 | LLM Fundamentals for RAG | Transformer basics, Context window, Tokens, Prompt-response flow, Hallucinations |
| 3 | Text Embeddings | What are embeddings, Vector representation, Embedding models, Dimensionality, Similarity meaning |
| 4 | Embedding Models | OpenAI embeddings, SentenceTransformers, Multilingual embeddings, Trade-offs, Model selection |
| 5 | Vector Databases Basics | Vector DB concept, ANN search, Indexing basics, Metadata storage, Vector lifecycle |
| 6 | Vector DB Tools | FAISS, Pinecone, Weaviate, Milvus, ChromaDB |
| 7 | Distance Metrics | Cosine similarity, Dot product, Euclidean distance, Trade-offs, Metric selection |
| 8 | Chunking Strategies | Fixed chunking, Semantic chunking, Chunk size, Overlap, Parent-child chunks |
| 9 | Document Ingestion | PDF ingestion, Text files, HTML ingestion, Cleaning text, Normalization |
| 10 | Indexing Pipeline | Embedding generation, Batch indexing, Metadata tagging, Versioning, Index updates |
| 11 | Retrieval Basics | Top-k retrieval, Similarity threshold, Recall vs precision, Retrieval latency, Query flow |
| 12 | Hybrid Search | Dense search, Sparse search, Keyword search, BM25, Hybrid ranking |
| 13 | Metadata Filtering | Structured filters, Access control, User-based filtering, Time filters, Security filters |
| 14 | Prompt Engineering for RAG | Prompt templates, Context injection, Instructions, Citations, Answer formatting |
| 15 | Naive RAG Architecture | Single retriever, Single prompt, Context stuffing, Limitations, Failure cases |
| 16 | Advanced RAG Architecture | Multi-retriever, Reranking, Compression, Query rewriting, Modular design |
| 17 | Reranking Techniques | Cross-encoders, Relevance scoring, Latency trade-off, Top-n rerank, Quality boost |
| 18 | Context Optimization | Token limits, Context pruning, Compression, Redundancy removal, Ordering chunks |
| 19 | Multi-hop Retrieval | Complex queries, Query decomposition, Iterative retrieval, Chain-of-thought, Examples |
| 20 | Agentic RAG | LLM agents, Tool calling, Planner-executor, Memory, Autonomous retrieval |
| 21 | Structured Data RAG | SQL integration, CSV data, APIs, Knowledge graphs, Hybrid retrieval |
| 22 | RAG with LangChain | Retrievers, Chains, Vector stores, Memory, RAG pipelines |
| 23 | RAG with LlamaIndex | Indexes, Query engines, Node parsing, Storage context, Tools |
| 24 | Evaluation of RAG | Retrieval metrics, Answer quality, Faithfulness, Relevance, Latency |
| 25 | RAGAS Framework | Faithfulness score, Context recall, Answer relevance, Ground truth, Automation |
| 26 | Security in RAG | Prompt injection, Data leakage, RBAC, PII handling, Secure retrieval |
| 27 | Scalability & Performance | Index sharding, Caching, Async retrieval, Load balancing, Cost control |
| 28 | Production Deployment | API design, Model hosting, Vector DB hosting, Monitoring, Logging |
| 29 | Monitoring & Feedback | User feedback, Drift detection, Retrieval errors, Continuous improvement, Alerts |
| 30 | Enterprise RAG Use Cases | Chatbots, Search engines, Knowledge assistants, Analytics, Decision support |
Interview question
Related Topics
19 December 2025
# Agentic / Autonomous Agents
Key Concepts
| S.No | Topic | Sub-Topics |
|---|---|---|
| 1 | Introduction to Autonomous Agents | Definition, Types, Applications, Benefits, Industry trends |
| 2 | Agentic AI Overview | Definition, Difference from traditional AI, Capabilities, Use cases, Examples |
| 3 | Multi-Agent Systems | Definition, Coordination, Communication, Cooperation, Competition |
| 4 | Agent Architectures | Reactive agents, Deliberative agents, Hybrid agents, Layered architectures, Examples |
| 5 | Environment Modeling | State representation, Dynamics, Reward functions, Sensors, Actuators |
| 6 | Perception in Autonomous Agents | Data acquisition, Feature extraction, Object detection, Sensor fusion, Challenges |
| 7 | Decision Making & Planning | Search algorithms, Planning strategies, Utility functions, Heuristics, Optimization |
| 8 | Reinforcement Learning for Agents | Q-learning, Policy gradients, Reward shaping, Exploration vs exploitation, Applications |
| 9 | Goal-Oriented Behavior | Goal representation, Hierarchical planning, Task decomposition, Prioritization, Monitoring |
| 10 | Autonomous Navigation | Path planning, Obstacle avoidance, SLAM, Localization, Motion control |
| 11 | Communication & Coordination | Message passing, Protocols, Distributed planning, Consensus, Collaboration |
| 12 | Learning & Adaptation | Online learning, Transfer learning, Continual learning, Self-improvement, Feedback loops |
| 13 | Simulation Environments | Gazebo, Unity ML-Agents, OpenAI Gym, Custom simulators, Evaluation |
| 14 | Human-Agent Interaction | User interface, Feedback, Trust, Explainability, Collaboration |
| 15 | Task Automation & Robotics | Robotic process automation, Physical robots, Task scheduling, Workflow integration, Examples |
| 16 | Safety & Reliability | Fault tolerance, Error recovery, Risk assessment, Robustness, Monitoring |
| 17 | Ethics & Responsible AI | Decision accountability, Bias mitigation, Fairness, Transparency, Regulatory compliance |
| 18 | Energy & Resource Management | Efficiency optimization, Power management, Resource allocation, Scalability, Constraints |
| 19 | Swarm Intelligence | Flocking behavior, Distributed control, Self-organization, Collective decision making, Applications |
| 20 | Planning under Uncertainty | Probabilistic planning, POMDPs, Risk analysis, Decision making, Examples |
| 21 | Autonomous Agents for NLP | Conversational agents, Chatbots, Task automation, Information retrieval, Language understanding |
| 22 | Autonomous Agents for Vision | Perception, Object recognition, Scene understanding, Navigation, Robotics applications |
| 23 | Autonomous Agents in Finance | Trading agents, Portfolio management, Risk assessment, Fraud detection, Strategy automation |
| 24 | Autonomous Agents in Healthcare | Diagnosis, Treatment planning, Patient monitoring, Robotics, Drug discovery |
| 25 | Tools & Frameworks | LangChain, AutoGPT, Ray, Unity ML-Agents, OpenAI Gym |
| 26 | Evaluation Metrics | Task success rate, Efficiency, Accuracy, Robustness, Adaptability |
| 27 | Integration with Cloud Platforms | AWS, Azure, GCP, Deployment, Scaling, Monitoring |
| 28 | Emerging Trends | Generative agents, Self-improving AI, Multi-modal agents, Autonomous decision making, Research directions |
| 29 | Challenges & Limitations | Computational cost, Safety, Scalability, Generalization, Ethical concerns |
| 30 | Career Path & Opportunities | AI researcher, Robotics engineer, Autonomous systems developer, Skill development, Industry demand |
Interview question
Related Topics
| LangGraph |
| AutoGen |
| CrewAI |
13 September 2025
#GenAI
Key Concepts
| S.No | Topic | Sub-Topics |
|---|---|---|
| 1 | Introduction to Generative AI | History of AI, Overview of Generative AI, Types of Generative Models, Key Use Cases, AI vs Human Creativity |
| 2 | AI Fundamentals | ML Basics, DL Overview, Neural Networks, Supervised vs Unsupervised Learning, Activation Functions |
| 3 | Generative Models Overview | Definition, Types, Applications, Strengths & Limitations, Popular Frameworks |
| 4 | Probabilistic Models | Bayesian Networks, Markov Chains, Hidden Markov Models, Conditional Probability, Inference Techniques |
| 5 | Variational Autoencoders (VAE) | Architecture, Encoder & Decoder, Latent Space, Loss Functions, Applications |
| 6 | Generative Adversarial Networks (GANs) | Generator & Discriminator, Training Process, Loss Functions, Popular Variants (DCGAN, CycleGAN), Applications |
| 7 | Diffusion Models | Concept of Diffusion, Forward & Reverse Processes, Noise Schedules, Denoising, Use Cases in Images |
| 8 | Transformers for GenAI | Attention Mechanism, Encoder-Decoder Architecture, Self-Attention, Positional Encoding, Use in Text/Image Generation |
| 9 | Large Language Models (LLMs) | Definition, Examples (GPT, BERT), Training Data, Tokenization, Limitations & Bias |
| 10 | Text Generation | Prompt Engineering, Beam Search, Temperature & Top-k Sampling, Fine-tuning Models, Text Summarization |
| 11 | Image Generation | GAN-based Image Generation, Diffusion Models, Text-to-Image, Inpainting, Super-Resolution |
| 12 | Audio & Music Generation | WaveNet, MusicVAE, Text-to-Speech, Voice Cloning, Sound Effects Generation |
| 13 | Video Generation | Video Synthesis, Frame Interpolation, Video-to-Video Translation, DeepFake Techniques, Ethical Considerations |
| 14 | Reinforcement Learning for GenAI | RL Basics, Policy & Value Networks, Reward Functions, RLHF (Human Feedback), Applications in Text & Games |
| 15 | Data Preparation & Augmentation | Dataset Collection, Cleaning, Normalization, Synthetic Data Generation, Data Labeling |
| 16 | Fine-Tuning & Transfer Learning | Pre-trained Models, Domain Adaptation, Hyperparameter Tuning, Prompt Tuning, LoRA & PEFT |
| 17 | Evaluation Metrics | Perplexity, BLEU, FID, IS, Human Evaluation, Metrics for Text, Image, Audio |
| 18 | Ethics in GenAI | Bias & Fairness, DeepFakes, Misinformation, Copyright Issues, Responsible AI Guidelines |
| 19 | GenAI Frameworks & Tools | TensorFlow, PyTorch, HuggingFace Transformers, Diffusers, OpenAI API |
| 20 | Prompt Engineering | Effective Prompts, Chain-of-Thought, Zero-shot vs Few-shot, Context Management, Prompt Templates |
| 21 | GenAI in NLP Applications | Chatbots, Summarization, Translation, Sentiment Analysis, Question Answering |
| 22 | GenAI in Vision Applications | Art Generation, Style Transfer, Image Enhancement, Object Synthesis, Medical Imaging |
| 23 | GenAI in Audio Applications | Music Composition, Voice Cloning, Speech-to-Text, Soundscapes, Podcast Generation |
| 24 | GenAI in Gaming & Simulation | NPC Dialogue, Procedural Content Generation, Story Generation, Game Level Design, AI Opponents |
| 25 | Deployment of GenAI Models | Cloud Services, API Deployment, Model Serving, Scalability, Monitoring |
| 26 | Optimization & Efficiency | Quantization, Pruning, Knowledge Distillation, Low-resource Training, Latency Reduction |
| 27 | GenAI for Business | Marketing Content, Personalized Recommendations, Automated Reports, Customer Support, Generative Analytics |
| 28 | Security & Privacy | Data Privacy, Model Stealing, Adversarial Attacks, Secure Model Sharing, Differential Privacy |
| 29 | Future Trends in GenAI | Multi-modal AI, Self-supervised Learning, General AI, AI Agents, AI in Healthcare & Research |
| 30 | Capstone Project | Choose a GenAI Domain, Collect Data, Train Model, Evaluate, Deploy, Document & Present |
Interview question
Related Topics
#LLM
Key Concepts
| S.No | Topic | Sub-Topics |
|---|---|---|
| 1 | Introduction to LLM Frameworks | Definition, Importance, Applications, Types of LLMs, Industry trends |
| 2 | Overview of Large Language Models | GPT, BERT, LLaMA, PaLM, Key concepts |
| 3 | Transformers Architecture | Attention mechanism, Encoder-decoder, Self-attention, Multi-head attention, Positional encoding |
| 4 | Tokenization Techniques | WordPiece, Byte-Pair Encoding, SentencePiece, Tokenization libraries, Preprocessing |
| 5 | Embedding Representations | Word embeddings, Contextual embeddings, Positional embeddings, Dimensionality, Fine-tuning |
| 6 | Pretrained Models & Frameworks | Hugging Face, OpenAI GPT, Cohere, Meta LLaMA, Integration |
| 7 | Fine-tuning LLMs | Supervised fine-tuning, Parameter-efficient tuning, LoRA, PEFT, Evaluation |
| 8 | Prompt Engineering | Prompt design, Zero-shot, Few-shot, Chain-of-thought, Best practices |
| 9 | LLM Training Pipelines | Data preprocessing, Dataset curation, Training loop, Checkpointing, Monitoring |
| 10 | Inference Optimization | Quantization, Pruning, Mixed precision, Batch inference, Latency optimization |
| 11 | Evaluation Metrics | Perplexity, BLEU, ROUGE, Accuracy, Human evaluation |
| 12 | LLM Frameworks Comparison | Hugging Face, OpenAI, Cohere, Meta LLaMA, LangChain integration |
| 13 | Integration with APIs | REST API, SDKs, Streaming, Rate limiting, Authentication |
| 14 | Vector Databases & LLMs | Pinecone, Weaviate, Milvus, FAISS, Embedding storage |
| 15 | LangChain Framework | Chains, Agents, Memory, Tools, Integrations |
| 16 | RAG (Retrieval-Augmented Generation) | Definition, Pipelines, Vector search, Integration with LLMs, Applications |
| 17 | LLM for NLP Tasks | Text classification, Summarization, NER, QA systems, Sentiment analysis |
| 18 | LLM for Code Generation | Code understanding, Generation, Auto-completion, Evaluation, Tools |
| 19 | Multi-modal LLMs | Text-to-image, Text-to-speech, Vision-language models, Applications, Frameworks |
| 20 | LLM Deployment Strategies | Cloud deployment, On-premise deployment, Edge deployment, Monitoring, Scaling |
| 21 | LLM Security & Privacy | Data privacy, Model watermarking, Access control, Compliance, Threats |
| 22 | Prompt Tuning & Instruction Tuning | Soft prompts, Instruction datasets, Fine-tuning strategies, Evaluation, Best practices |
| 23 | RLHF (Reinforcement Learning with Human Feedback) | Concept, Training pipeline, Reward model, Applications, Challenges |
| 24 | Open-source LLM Frameworks | Hugging Face, LLaMA, Falcon, MPT, Integration tools |
| 25 | LLM in Chatbots & Virtual Assistants | Conversation design, Context handling, Multi-turn dialogue, Personalization, Evaluation |
| 26 | Monitoring LLMs in Production | Logging, Metrics, Drift detection, Alerting, Performance tracking |
| 27 | Cost Optimization in LLM Usage | Compute optimization, Model selection, Batch inference, Quantization, Cloud cost management |
| 28 | Ethics & Bias in LLMs | Bias detection, Fairness, Mitigation strategies, Responsible AI, Regulatory compliance |
| 29 | Future Trends in LLM Frameworks | Multilingual models, Model scaling, Efficiency improvements, AGI research, Emerging frameworks |
| 30 | Career Path & LLM Opportunities | LLM engineer, Researcher, AI consultant, Skill development, Industry roles |
Interview question
Related Topics
| LangChain |
| LlamaIndex |
| Haystack Agents |
Subscribe to:
Posts (Atom)