| 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? |
