The reading room · Digna Legi
AI Evals: A Hands-On Guide for Product Teams
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Evidence-reviewed score based on available publisher text. The evidence is sampled with gaps, so completeness and all examples cannot be fully judged.
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How scoring works →This brief · about 3 min with detail
Why read this
AI evals measure acceptable output in probabilistic workflows, using context-specific correctness, recurring error patterns, and baselines.
AI brief · Checked against source text
The main idea
AI evals are feedback loops for probabilistic systems: they measure how often an AI workflow produces acceptable output, not whether code deterministically passes once. The author argues product teams must define what “good” means for their own context, discover recurring error types through review, choose measurement methods matched to those errors, and use baseline comparisons to improve prompts, models, and workflows.
Some background helpful. Basic familiarity with LLM products, prompts, and product discovery.
Go a little deeper
Correctness is a product decision
The key move is not choosing an eval vendor or metric first; it is deciding what counts as correct in a specific use case. A concise answer may be wrong for a learner who needs explanation, while a verbose answer may be wrong for an expert. Built-in measures like helpfulness or conciseness can start discussion, but the article treats domain fit as the product team’s responsibility.
Error analysis precedes automation
The author’s process starts with watching outputs fail before designing measurements. Manual review exposes error categories: fabricated job titles, merged quotes, misclassified questions, leading suggestions, or already-answered prompts. This sequencing matters because fixing everything immediately can waste effort on minor flaws before the team understands which failures are frequent, harmful, or important to customers.
Measurement should fit the error
The taxonomy is useful because it ties eval type to the shape of the mistake. Exact quotes can be checked with deterministic string search. Ambiguous semantic claims may need a second model judging a simplified yes/no task. Golden datasets work best when inputs and outputs are small with one right answer. Customer behavior can also become feedback when direct ratings are too thin.
Baselines turn tweaks into experiments
Once errors can be counted, prompt changes and model changes stop being vibes-based. The article recommends running fixed inputs through the current workflow, scoring the outputs, then comparing variants across all evals. A change that improves one category can damage another, so the baseline is not just a benchmark; it protects against local improvements that degrade the overall product.
A case from the article
Fact-checking interview vignettes
For Lovable interview summaries, the author reviewed one transcript deeply, identified key moments, checked every quote, and found two recurring failures: fabricated details and invented merged quotes. That led to two evals: a fact-checker for claims and a hallucination guard that searched for quoted strings in the original transcript. The point is that one careful review can reveal reusable checks for the remaining workload.
How the case is made
The case is made through practical observation, worked product examples, and a taxonomy of eval types tied to specific errors.
Where the idea has limits
The guidance is scoped to product teams evaluating AI workflows or products; it does not fully cover calibrating LLM judges, which the author explicitly says is deferred.
A question to take away · from Digna Legi
Where are you currently trusting an AI output without knowing the error categories it tends to produce?
What the original adds
The source includes detailed setup advice for baselines, experiment documentation, harness modules, and several concrete error-to-eval mappings beyond this brief.
About this brief
AI-written, then separately checked for source support, useful detail and clarity. The author’s claims and our editorial question are kept separate. The original remains the author’s work. How we select and summarise →
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Digna legi. Worth reading.