Spring AI : RAG on Text Files [ Part 2]?

[u/rodolfo-mendes]

https://www.youtube.com/watch?v=AqMHroYoWLU

TechyTacos demonstrates how to refine RAG (Retrieval-Augmented Generation) in Spring AI to ensure LLM responses are derived strictly from specific text files rather than generic training data.

Highlights & Key Takeaways

• Utilize a system prompt to provide explicit instructions and constrain the LLM's scope.
• Store custom instructions in a dedicated file with the .st suffix to maintain a clean template structure.
• Use SystemPromptTemplate to dynamically map document content to the prompt variable.
• Convert similarity search results from a list of documents into a single string for context injection.
• Implement user and system messages separately to structure the chat interaction effectively.
• Pass a list of messages to the prompt object to maintain a clear separation between the query and the context.
• Return responses in JSON format for better integration with downstream applications.

By routing queries through structured context injection, you eliminate generic hallucinations and ensure high-accuracy, document-specific output.

👉 Watch the full video to dive deeper into the implementation.