Evaluating LLM Output: Hallucination, Grounding, and Measurement
You cannot govern what you cannot measure. A practical framework for evaluating LLM outputs — distinguishing hallucination from grounding failure, and building evals that survive model changes.
The first question every enterprise asks about an LLM feature is "how do we know it's right?" Without an answer, the project stalls at pilot. Evaluation is the bridge from demo to production.
Define what "wrong" means
| Failure mode | What happened | Fix |
|---|---|---|
| Grounding failure | Answer contradicts the source | Tighten prompt / grounding |
| Retrieval failure | Right source never fetched | Improve retrieval/rerank |
| Reasoning failure | Correct sources, wrong conclusion | Better model / decomposition |
| Fabrication | Invented fact, no source | Force "I don't know" path |
Build a real evaluation set
Collect actual user questions with verified answers and the supporting source. A few hundred well-chosen cases beat thousands of synthetic ones. Version this set; it is the contract your system must keep passing as prompts and models change.
Combine automated and human evaluation
Use programmatic checks for faithfulness (does the answer follow from the source?) and relevance, including LLM-as-judge scoring where appropriate. But keep a human review loop for high-stakes outputs — automated judges have their own biases and blind spots.
Make evaluation continuous
Run the eval set on every prompt change, model upgrade, and retrieval tweak. A model "upgrade" can silently regress your specific use case. Regression testing for LLMs is exactly as important as it is for code.
The bottom line
Measurement turns AI from a leap of faith into an engineering discipline. Define the failure modes, build a versioned eval set, automate the checks, and keep a human in the loop where it matters.
Want to see it in action?
Try the live Document Intelligence demo.