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Retrieval-Augmented Generation (RAG) lets AI agents answer user queries using corporate files. However, poor chunk parsing causes chatbots to make up information. We share our parameters for reliable RAG deployments.
Core Tuning Parameters
- Temperature: Keep model temperature near 0.0 to prevent creative text generation.
- Chunk Sizes: Parse documents into 500-token chunks with a 10% overlap to preserve surrounding context.
- Similarity Caps: Require a cosine similarity match score above 0.78 before passing document chunks to the LLM.
Prompt Anchor Sample
System Prompt: Answer the user query ONLY using the provided text blocks.
If the answer is not present, reply with "I do not have access to that information."
Conclusion
Enforcing these thresholds ensures corporate agents remain factual, safeguarding client trust.