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.

LLM Evaluation Lab

Problem

Approach

Technology

Engineering considerations

Testing RAG Applications: A Practical Guide

Evaluating LLM Applications in Production

LLM Evaluation Lab

Problem

Approach

Technology

Engineering considerations

Testing RAG Applications: A Practical Guide

Evaluating LLM Applications in Production

Problem

Approach

Technology

Engineering considerations

Related writing

Testing RAG Applications: A Practical Guide

Evaluating LLM Applications in Production

Problem

Approach

Technology

Engineering considerations

Related writing

Testing RAG Applications: A Practical Guide

Evaluating LLM Applications in Production