From Docs to Evals: Essential AI Skills for Modern Product Managers

Limitations of Traditional PRDs

Conventional product requirement documents (PRDs) enumerate fixed features and boundaries. Large language models (LLMs) are stochastic, generate dynamic outputs, and operate in open‑ended contexts, so a static text specification cannot capture all possible behaviours.

Evaluations as a Living PRD

Golden Conversation as Initial Specification

User: "Help me write a resume." Model: "Sure, please provide your experience and I’ll craft an attractive version."

This dialogue encodes tone, guidance, and scope. Teams reverse‑engineer concrete artefacts from it:

Design system prompts.

Orchestrate agent workflows.

Define quantitative evaluation criteria.

Error‑Driven Requirement Extraction

Requirements emerge from observed failures. A reproducible error‑analysis pipeline is:

Randomly sample 100 real user interaction logs (traces).

Manually label each trace as Pass or Fail .

Write critique notes describing why a trace failed.

Aggregate notes into a structured failure‑pattern table.

The failure‑pattern table becomes a concrete metric set that can be fed to an LLM‑based evaluator for automated quality checks.

LLM‑as‑a‑Judge for Automated Quality Definition

When manual testing is infeasible, an LLM is prompted to make binary judgments on targeted questions (e.g., “Is the response factually faithful?”). This forces the team to articulate precise quality thresholds and provides immediate Pass/Fail feedback on every model update.

Evaluation‑Driven Architecture

Retrieval‑Augmented Generation (RAG)

RAG pipelines are split into a retriever and a generator. Separate evals measure:

Retriever recall (Recall@K) – the proportion of relevant documents returned in the top‑K results.

Generator faithfulness and relevance – typically using metrics such as FAITHFULNESS (answer groundedness) and BLEU / ROUGE for relevance.

These metrics mirror the architectural decomposition, guiding where to improve.

Agent Systems

Complex multi‑step agents are instrumented with a failure matrix that records the step at which a trace fails. Finer granularity (e.g., per‑action Pass/Fail) yields clearer diagnostic signals and drives targeted refactoring of the agent workflow.

New PM Role: Evaluation Architect

Product managers in the AI era shift from writing static specifications to designing, maintaining, and evolving evaluation suites. Their responsibilities include:

Crafting golden‑conversation prototypes.

Defining systematic error‑analysis processes.

Building LLM‑as‑a‑Judge pipelines.

Aligning evaluation metrics with architectural components.

This role ensures that product quality is continuously verified and that the evaluation suite itself becomes the source of truth for product requirements.

Evaluations as the Language of AI Products

Traditional PRDs answer “what we want to build.” An evaluation‑centric PRD answers “what good looks like” for the model. Evaluations are therefore both specification and verification, providing a dynamic, runnable, and evolvable definition of product success.