The reading room · Digna Legi
Beyond vibe checks: A PM’s complete guide to evals
/100
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Why read this
Evals define what “good” means for variable AI outputs and isolate whether system changes help or damage behavior.
AI brief · Checked against source text
The main idea
The central claim is that AI product teams over-focus on models and prompts while neglecting evaluations, which the author presents as the mechanism for isolating whether a system change improves or damages behavior. Evals work by defining what “good” means for an AI product across variable, qualitative outputs, then measuring specific steps rather than trusting manual checks or launch-time feedback.
Some background helpful. Basic familiarity with LLMs, prompts, APIs, and AI product workflows.
Go a little deeper
AI testing is not just pass or fail
The author’s useful distinction is that LLM systems are not deterministic software components. The same prompt can yield different acceptable or unacceptable answers, so evaluation has to handle relevance, coherence, interpretation, and safety rather than only binary correctness. That changes the PM’s job from checking whether code runs to defining the conditions under which varied behavior is still acceptable.
Different eval types buy different signals
The excerpt avoids treating evals as one technique. Human evals are closest to users but sparse, ambiguous, and expensive. Code-based checks are cheap and fast but weak for subjective work. LLM-as-judge approaches scale natural-language grading and can explain judgments, but need calibration and enough volume because their outputs are probabilistic.
Good evals are written instruments
For LLM-based judging, the eval itself is a prompt that must be designed. The author breaks it into role, context, goal, and terminology or label. The key mechanism is specificity: the judge must know what it is examining, what application data to grade, what success means, and how ambiguous terms such as toxicity should be interpreted in this product context.
A case from the article
The trip planner failure
The travel-agent scenario shows why manual inspection is brittle. A team builds a natural-language trip planner, connects it to flight, hotel, and mapping tools, and tests common requests by hand. It looks fine until launch, when users complain that the agent booked San Diego instead of San Francisco. The example illustrates how agentic systems can fail at specific interpretation or action steps that vibe checks miss.
How the case is made
The case is made through practitioner observation, analogy to software and driving tests, and a worked trip-planning agent scenario.
Where the idea has limits
The excerpt argues for evals as necessary discipline, but its detailed implementation evidence is incomplete here; it introduces, rather than fully demonstrates, the promised concrete eval example.
A question to take away · from Digna Legi
Where in your AI system are you currently judging quality by feel because no one has defined measurable failure yet?
What the original adds
The original excerpt also distinguishes human, code-based, and LLM-based evals, then names four components of an LLM judge prompt: role, context, goal, and terminology or labels.
Why return to this
Use this when a convincing demo is doing too much of the work in a launch decision. The eval examples help turn a vague quality judgment into explicit criteria you can check after a prompt or model changes.
PMs deciding whether an AI product is improving.
The publisher labels this a subscriber-only edition. The available text supports the note, but does not establish complete access to the guide.
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Digna legi. Worth reading.