25 August 2025

#Spring_AI

#Spring_AI

Key Concepts


S.No Topic Sub-Topics
1Introduction to Spring AIWhat is Spring AI, AI integration goals, Supported LLMs, Architecture, Use cases
2Spring AI SetupProject structure, Dependencies, Gradle/Maven config, OpenAI API key, Testing setup
3Spring AI Core ConceptsPrompt interface, ModelInterface, Response handling, Error strategy, Token limits
4Prompt EngineeringInstructions, System prompts, User prompts, Few-shot prompts, Prompt validation
5OpenAI Chat CompletionChatModel, CompletionModel, Temperature, MaxTokens, Streaming responses
6Multiple LLM ProvidersOpenAI, Azure OpenAI, HuggingFace, Google Gemini, Model switching
7Text Generation APILLM text API, Creative settings, Style tone, Prompt variables, Generating summaries
8Embedding APIEmbeddingModel, Vector embeddings, Dimensions, Similarity search, Applications
9Vector Stores IntroductionWhat is vector DB, Pinecone, Milvus, Redis Search, ChromaDB
10Spring AI with PostgreSQL Vectorpgvector extension, Table schema, Insert embeddings, Similarity queries, Ranking
11RAG Architecture BasicsRetrieval flow, Context building, Query embedding, Response synthesis, Use cases
12Spring AI RAG ImplementationRetriever interface, Embedding store, Prompt injection, Chunking, Recall optimization
13Document LoadingText loader, PDF loader, Web crawler, Markdown loader, HTML parsing
14Chunking StrategyToken limits, Recursive chunking, Semantic chunking, Metadata tagging, Overlap strategy
15Tools IntegrationFunction calling, Tools API, Schema definition, JSON mode, Automatic reasoning
16Image Generation ModelsDALL-E, Stable Diffusion, Prompt quality, Size configs, Safety filters
17Speech-to-TextWhisper API, Audio formats, Transcription settings, Accuracy boosting, Noise cleaning
18Text-to-SpeechVoice models, SSML usage, Voice styles, Streaming audio, Response types
19Knowledge Graph IntegrationNeo4j, Graph embeddings, Relation queries, Context retrieval, Semantic graph
20Multi-Model OrchestrationLLM chaining, Model selection logic, Cost optimization, Latency control, Retry logic
21AI AgentsAutonomous agents, Task planner, Tools execution, Memory store, State management
22Memory and HistoryConversation history, Memory storage, Retrieval scoring, Long context, Session state
23Security in AI ApplicationsAPI key security, Prompt injection risks, Data sanitization, Logging, Rate limiting
24Model Fine-TuningBase models, Fine-tuning dataset, Hyperparameters, Training loop, Evaluation
25LLM ObservabilityMetrics tracking, Prompt logging, Cost tracking, Tracing, Eval framework
26Enterprise RAGKnowledge base integration, Access control, Hybrid search, Document versions, QA pairs
27Spring AI in MicroservicesAPI Gateway, Circuit breaker, Async calls, Scaling models, Deployment strategy
28Docker & DeploymentDockerfile config, Environment variables, Secrets, K8s deployment, Horizontal scaling
29Hands-on ProjectRAG chatbot, PDF knowledge base, Vector DB, Streaming chat, UI integration
30Interview PreparationTop questions, Architecture diagram, Best practices, Performance tuning, Final revision

Interview question

Basic Level

  1. What is Spring AI, and how does it differ from traditional Spring Boot applications?
  2. What are the core components of Spring AI?
  3. Which AI models are supported by Spring AI out of the box?
  4. How do you add Spring AI dependencies to a Spring Boot project?
  5. What is the purpose of the ChatClient in Spring AI?
  6. How do you configure API keys for model providers in Spring AI?
  7. What is a PromptTemplate in Spring AI?
  8. How do you pass variables into a prompt template?
  9. What is the role of a model provider in Spring AI?
  10. Name some model providers compatible with Spring AI.
  11. How do you integrate OpenAI with Spring AI?
  12. What is the function of application.yml in Spring AI configuration?
  13. How do you define a basic chat request in Spring AI?
  14. What is an embedding model used for?
  15. How can you handle model responses in a Spring AI controller?
  16. Explain the role of dependency injection in Spring AI.
  17. How is logging handled in Spring AI applications?
  18. What is prompt chaining?
  19. How do you handle API key security in Spring AI applications?
  20. What are the advantages of using Spring AI for enterprise applications?
  21. How is Spring AI related to LangChain?
  22. Can Spring AI run locally without a cloud model?
  23. What is a simple use case of Spring AI in a chatbot?
  24. How do you test a simple Spring AI prompt flow?
  25. What are common error types encountered during Spring AI model calls?

Intermediate Level

  1. How do you configure multiple AI models in one Spring AI application?
  2. What is the ChatClient.Builder used for?
  3. How does the StreamingResponse feature work in Spring AI?
  4. How do you enable reactive programming in Spring AI?
  5. What are dynamic prompts, and when should you use them?
  6. How do you persist conversation history in Spring AI?
  7. Explain how to integrate RedisChatMemory with Spring AI.
  8. What are the benefits of using memory in chat-based applications?
  9. How do you generate embeddings using Spring AI?
  10. Explain the Retriever pattern in Spring AI.
  11. What are vector stores, and how are they used?
  12. How do you integrate Pinecone or FAISS with Spring AI?
  13. What are the steps to build a RAG (Retrieval-Augmented Generation) pipeline?
  14. How do you chunk documents for better retrieval performance?
  15. Explain the purpose of the EmbeddingStore interface.
  16. How do you configure the model temperature in Spring AI?
  17. What is top-k and top-p sampling in model configuration?
  18. How do you expose an AI-powered REST endpoint in Spring AI?
  19. How does Spring AI manage state between chat sessions?
  20. What is the difference between ChatMemory and Retriever?
  21. How do you handle rate-limiting in Spring AI?
  22. What kind of data format does the model response return?
  23. How do you integrate Azure OpenAI with Spring AI?
  24. How do you handle exceptions in the ChatClient pipeline?
  25. What are the logging and observability best practices in Spring AI?

Advanced Level

  1. How do you implement a custom embedding model?
  2. Explain the difference between OpenAI embeddings and Hugging Face embeddings.
  3. How does the Retriever fetch the most relevant chunks?
  4. What are the best practices for optimizing prompt templates?
  5. How do you design an RAG workflow with context injection?
  6. How do you store and reuse embeddings efficiently?
  7. What is the purpose of using FAISS with Spring AI?
  8. How does Spring AI interact with external APIs during generation?
  9. Explain how streaming responses improve real-time user experience.
  10. How do you use the Function Calling feature in Spring AI?
  11. What is schema definition in Spring AI tools?
  12. How can you integrate a custom AI provider into Spring AI?
  13. Explain how to secure model endpoints using OAuth2 or JWT.
  14. How do you build a context-aware chatbot using memory and retrievers?
  15. What is the difference between retrieval and augmentation in RAG?
  16. How can you improve latency in large model calls?
  17. Explain the role of the ChatModel interface in Spring AI.
  18. How can you create a custom retriever for proprietary data?
  19. How does Spring AI manage token limits in requests?
  20. How do you benchmark model performance in Spring AI?
  21. How do you add observability with Micrometer and Prometheus?
  22. How do you debug failed model responses?
  23. How can you build AI pipelines that use multiple models?
  24. What?s the advantage of using asynchronous calls with Spring AI?
  25. How can you implement multi-turn conversations using ChatMemory?

Expert Level

  1. Explain the internal architecture of the Spring AI ChatClient.
  2. How do you design scalable Spring AI microservices?
  3. What are advanced techniques for fine-tuning model responses?
  4. How do you integrate Spring AI with Kafka for event-driven AI processing?
  5. How can you implement distributed memory for chat state management?
  6. Explain how to customize prompt generation dynamically at runtime.
  7. What is the lifecycle of an embedding request in Spring AI?
  8. How do you build a plug-and-play vector retriever component?
  9. Explain how LangChain components can be wrapped using Spring AI.
  10. How do you implement caching for model responses?
  11. What strategies exist to mitigate AI hallucinations in responses?
  12. How do you build an AI-powered assistant using RAG + Function Calling?
  13. What?s the best way to structure embeddings for multi-domain data?
  14. How do you integrate AI pipelines with CI/CD workflows?
  15. How can you optimize token usage programmatically?
  16. Explain the trade-offs between local and hosted model execution.
  17. How do you handle model versioning and backward compatibility?
  18. What?s the process to extend Spring AI with a new model provider?
  19. How do you design fault-tolerant AI systems using Spring AI?
  20. What are advanced observability patterns (e.g., tracing across AI flows)?
  21. How do you containerize and deploy Spring AI apps on Kubernetes?
  22. How do you implement AI governance and compliance monitoring?
  23. Explain strategies for managing user personalization in GenAI systems.
  24. How can you integrate Spring AI with external knowledge graphs?
  25. What are upcoming features and community roadmaps for Spring AI?

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