AI & agents / THE PRACTICAL CHECKLIST
LLM reviews
Compare language models with a controlled task set, clear success criteria, and a record of the configuration being evaluated.
What to look for
Distinguish the model from the application, prompt, retrieval source, and tools around it. When those conditions differ, describe the comparison as a workflow comparison rather than a model-only result.
Make the review useful
Use the same authorized inputs where possible and decide in advance how answers will be checked. Repeat important tasks, record meaningful variability, and avoid reporting only the best sample from repeated attempts.
Keep the limits in view
A public benchmark or broad reputation may suggest where to begin, but your requirements still need their own assessment. Do not present a small trial as a statistically representative estimate of general capability.
Is a benchmark score enough to choose an LLM?
It can inform your shortlist, but it does not establish performance on your exact task, data, permissions, or acceptable failure conditions.
ChatGPT, Claude & LLM reviews: a fair comparison framework
Evaluate AI tools on your own tasks, with controlled prompts, documented versions, and checkable results.
Read the 6-minute guide ↗KEEP EXPLORING
More in ai & agents.
AI reviews
Evaluate an AI tool on a defined task with checkable output. Separate writing fluency from correctness and workflow fit.
ChatGPT reviews
Evaluate the ChatGPT experience you actually use: a defined task, a documented configuration, and results checked against the source.
Anthropic & Claude reviews
Separate company-level questions, the Claude product experience, and the underlying model configuration when evaluating Anthropic-related tools.