How to Build a Golden Dataset for Repeatable AI Evaluations
A golden dataset is a curated, versioned set of inputs with reviewed expected outputs or explicit pass criteria, used as the fixed reference you run before every…
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#LLM evaluation
Explore the guides, ideas, and practical explanations in this topic.
5 published articlesHow to Evaluate and Reduce Hallucinations in LLM Workflows
Hallucinations are best handled as two linked tasks: measuring them with a labelled evaluation set, and reducing them with grounded retrieval plus an output guardrail. This guide…
How to Evaluate Hallucinations in a Local Model with Your Own Test Set
A hallucination evaluation for a local model needs three things: questions whose answers you have verified, a cold run with no tools or retrieval, and a grading…
Prompt Engineering: A Practical Guide to Instructions, Examples, and Evaluation
Prompt engineering is the practice of writing, testing, and revising the instructions given to a language model so its output reliably serves a defined purpose. This guide…
How to Write a Prompt with Clear Success Criteria
A prompt with clear success criteria states the outcome you want, the constraints that matter, the evidence the model may use, and what the final answer must…