Building an Eval Framework: How to Monitor and Reduce LLM Hallucinations
goyalshaliniuk · x · 2026-08-08
The author shares practical engineering methods for continuously monitoring and evaluating LLM hallucinations.
Build Evaluation Datasets
Include known facts, edge cases, unanswerable questions, adversarial prompts, and real user queries.
Key Metrics to Track
- Accuracy
- Groundedness
- Citation correctness
- Abstention quality
- Unsupported claims
Verification Workflow
For high-stakes applications, add a post-generation verification step: check claims, compare against sources, and flag unsupported statements. A separate model or rule-based validator can be used as a verifier.
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