Executive Summary

  • You cannot improve what you do not measure. Releasing an agent without a statistical hallucination baseline is corporate negligence.
  • Deploying humans to read thousands of tests is too slow. The industry standard is 'LLM-as-a-Judge'.
  • Continuous CI/CD evaluation pipelines catch regressions when shifting from GPT-4o to a new model version.
Ragas Answer Relevance
> 0.92Production Ready

The required mathematical threshold for a RAG response to be considered highly relevant and unhallucinated.

1. The Evaluation Triad

Frameworks like Ragas evaluate models across three mathematical dimensions: Faithfulness (Is the answer entirely derived from the context?), Answer Relevance (Does it actually answer the user's question?), and Context Precision (Did the vector database pull the right files?).

Impact of Context Window Size on Hallucination
10k Tokens (Laser Focused)2
50k Tokens (Moderate Noise)8
128k Tokens (Lost in the Middle)19

2. Creating the Golden Dataset

Before engineering begins, domain experts must curate a 'Golden Dataset' of 100 trick questions, edge cases, and standard queries paired with the perfect human-written answer. The AI is scored against this dataset nightly.

3. CI/CD for Prompts

Prompts are code. When a developer changes the system prompt, an automated GitHub action runs the agent against the Golden Dataset. If the Answer Relevance score drops by 2%, the PR is rejected.