Spring AI Interview Questions – Practice Questions with Answers and Scoring
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Top Spring AI Interview Questions for Freshers and Experienced Developers
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45 Questions2 PagesEasy · Medium · HardPage 1 of 2
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1
What is Spring AI and how does it integrate with the Spring ecosystem?
easyspring-aioverview
Answer
Spring AI provides abstractions to integrate AI models into Spring apps.
Key concept: Unified API for LLM providers.
Example: Using ChatClient in Spring Boot.
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2
Explain the role of ChatClient in Spring AI.
easychatclientllm
Answer
ChatClient is used to interact with LLMs via prompts.
Key concept: Simplifies request/response handling.
Example: chatClient.prompt("Hello").call().content();
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3
How does Spring AI handle multiple LLM providers?
mediumprovidersarchitecture
Answer
It uses provider-specific implementations under a common interface.
Key concept: Pluggable architecture.
Example: OpenAI, Azure, HuggingFace support.
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4
What is a prompt template in Spring AI?
mediumprompttemplate
Answer
A reusable template for dynamic prompt generation.
Key concept: Parameterized prompts.
Example: 'Hello {name}' with variable binding.
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5
How does Spring AI support embedding generation?
mediumembeddingsvector
Answer
Through EmbeddingClient abstraction.
Key concept: Converts text into vectors.
Used for semantic search.
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6
Explain how vector stores are integrated in Spring AI.
mediumvectorstoreretrieval
Answer
Spring AI supports vector databases like Pinecone or Redis.
Key concept: Store embeddings for retrieval.
Example: similarity search queries.
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7
What is Retrieval-Augmented Generation (RAG) in Spring AI?
mediumragretrieval
Answer
Combines LLM with external data retrieval.
Key concept: Improves accuracy using context.
Example: search DB + generate answer.
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8
How do you implement RAG using Spring AI?
hardragimplementation
Answer
Store embeddings, retrieve relevant docs, pass to LLM.
Key concept: Context injection.
Example: vectorStore.similaritySearch().
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9
What is the role of Message abstraction in Spring AI?
mediummessagechat
Answer
Represents structured communication with LLM.
Key concept: Supports roles like user/system.
Example: SystemMessage, UserMessage.
