AgentSkillsCN

systematic-ai-eval-design

设计并实施高保真度的评估集（Evals），以衡量AI模型的表现。在将AI功能从原型测试、基于“直觉体验”的验证阶段，逐步过渡到生产就绪的系统时，或在尝试优化模型在特定专业任务上的表现时，可运用这一方法。

SKILL.md
--- frontmatter
name: systematic-ai-eval-design
description: Design and implement high-fidelity evaluation sets (Evals) to measure AI model performance. Use this when transitioning an AI feature from prototype to "vibe-based" testing to a production-ready system, or when trying to optimize a model's performance on a specific professional task.

In the era of AI, the Eval is the new Product Requirement Document (PRD). To improve a model, you must first define exactly what success looks like through systematic, high-fidelity benchmarks that allow researchers and developers to run experiments and measure progress.

The Core Principle: Evals as PRDs

If the model is the product, the Eval is the requirement. A good Eval allows you to move away from subjective "vibe checks" and toward a systematic way of measuring how AI automates your core value chain.

Workflow: Building a High-Fidelity Eval

1. Identify the Core Value Chain

Focus on the specific task that provides the most economic value.

  • •Narrow the scope: Instead of "legal advice," focus on "redlining a Series A term sheet."
  • •Define the persona: Identify the exact professional level required to judge the task (e.g., a McKinsey analyst, a Senior Software Engineer, or a Radiologist).

2. Define the "Gold Standard" Rubric

Create a multidimensional scoring system that translates expert intuition into machine-readable criteria.

  • •Identify Critical Failures: List "non-negotiables" (e.g., "The model must not hallucinate a clause that wasn't in the original document").
  • •Weight the Criteria: Assign points for specific achievements (e.g., +2 for identifying a missing indemnification clause, +1 for tone consistency).
  • •Create Verifiers: For technical tasks, use unit tests or hard logic. For creative/reasoning tasks, use qualitative rubrics that a high-skilled human (or a stronger "judge model") can apply.

3. Source Expert Labor for "Post-Training" Data

High-quality models require high-quality feedback.

  • •Avoid Crowdsourcing: Do not use low-skilled labor for complex professional tasks. The model will only be as good as the person training it.
  • •Focus on the Top 10%: Recruit experts who are currently "underemployed" (e.g., top-tier engineers at slow-moving companies) to provide the initial "ground truth" data.

4. Implement AI Feedback (RLAIF)

Once you have a human-defined rubric, use it to automate the feedback loop.

  • •Use the rubric to reward the model for "good" trajectories and penalize "bad" ones.
  • •Scale the evaluation by having a "Judge LLM" apply the human-written rubric to thousands of model outputs.

Examples

Example 1: Legal Contract Analysis

  • •Context: A legal-tech startup building an AI contract reviewer.
  • •Input: 50 complex NDAs with hidden "toxic" clauses.
  • •Eval Application:
    1. •Expert Task: Hire a lawyer to redline these documents perfectly.
    2. •Rubric: 1) Did it catch the 3 hidden clauses? 2) Is the tone professional? 3) Is the explanation legally sound?
    3. •Measurement: The model is scored 0-10 on each document based on the lawyer's "ground truth."
  • •Output: A percentage score showing model accuracy, used to decide if the model is ready for deployment.

Example 2: Coding Assistant for Proprietary API

  • •Context: An internal tool helping engineers use a company's private libraries.
  • •Input: 100 coding prompts based on internal documentation.
  • •Eval Application:
    1. •Technical Verifier: Write a unit test for each prompt that the code must pass.
    2. •Expert Rubric: A senior engineer reviews the code for "idiomatic" use of the private API.
    3. •RLHF: The model generates 3 versions of the code; the engineer selects the most "correct" version to reinforce that style.
  • •Output: A leaderboard of different model versions (e.g., GPT-4o vs. a fine-tuned Llama) to see which follows internal standards best.

Common Pitfalls to Avoid

  • •Saturating the Eval: If the model hits 100% accuracy, the Eval is too easy. You must constantly "raise the ceiling" by adding more difficult, long-horizon reasoning tasks (e.g., moving from "write a function" to "build a feature across three files").
  • •Vibe-Based Decisions: Never ship an AI update because "it feels better." Without a numeric Eval score, you risk regressing on edge cases you aren't currently looking at.
  • •Ignoring the "Leading Indicators": In fast-moving markets, the best Eval is the one that measures the capabilities your wealthiest customers are currently desperate for, not just academic benchmarks like MMLU.
  • •Reward Hacking: Be careful with rubrics; if you reward "length of explanation," the model will become wordy and unhelpful. Ensure rubrics reward the outcome, not the formatting.