Python · LLM · Evaluation
LLM Evaluation Lab
LLM Evaluation Lab is a hands-on workspace for building evaluation suites for LLM-based features — treating prompts and model behaviour as something to be tested, not just tried.
Problem
LLM features are often judged by spot-checking a few outputs. Without reproducible evaluations, it is hard to tell whether a prompt change, model upgrade, or retrieval tweak made things better or worse.
Approach
- Curate small, representative example sets that describe expected behaviour.
- Combine deterministic checks (format, constraints) with metric-based and model-graded evaluation.
- Run evaluations as repeatable test suites so results can be compared across changes.
Technology
- Python
- LLM APIs
- pytest
- Evaluation metrics
Engineering considerations
- Reproducibility: pinned model versions, fixed datasets, and recorded configuration.
- Separating what can be asserted deterministically from what needs graded judgement.
- Being explicit about the limitations and biases of model-graded evaluation.
Related writing
Testing RAG Applications: A Practical Guide
A retrieval-augmented generation system has two places to fail — retrieval and generation. A layered test strategy that isolates each, from unit tests to end-to-end evaluation.
2 min read
2 min read
Evaluating LLM Applications in Production
Offline test sets tell you whether a change is safe to ship; production signals tell you whether it keeps working. How to combine both into an evaluation practice for LLM features.
2 min read
2 min read