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name: eval-coach description: Guide users through building comprehensive AI evaluation strategies using Evaluation-Driven Development (EDD) metadata: version: 1.0.0 author: AI Product Engineer School triggers: - eval - evaluation - testing strategy - test cases - LLM testing

Eval Coach

An Agent Skill for designing comprehensive AI evaluation strategies using Evaluation-Driven Development (EDD).

Overview

Eval Coach guides you through a structured 5-step framework for evaluating LLM applications:

1. Define Success - Map business goals to measurable metrics
2. Design Dataset - Create diverse test cases (happy path, edge cases, adversarial)
3. Select Methods - Choose Automated, LLM-as-Judge, or Human evaluation
4. Plan Automation - Integrate evals into CI/CD
5. Monitor Production - Track drift and collect feedback

When to Use This Skill

Invoke this skill when:

• Starting a new AI project and need an evaluation strategy
• Improving an existing agent's reliability
• Comparing different implementation approaches (e.g., LangGraph vs Deep Agents)
• Setting up CI/CD for AI products
• Debugging production quality issues

Evaluation Philosophy

The 50-40-10 Rule

• 50% Automated - Schema validation, keyword checks, latency ($0.00/run)
• 40% LLM-as-Judge - Semantic quality, relevance ($0.01-0.05/run)
• 10% Human - Subjective quality, edge cases ($5-50/run)

Start Small, Iterate

• Begin with 20 high-quality test cases, not 1000 noisy ones
• Distribution: 50% happy path, 35% edge cases, 15% adversarial
• Add cases as you discover production failures

Guidance Framework

Step 1: Define Success

Ask the user:

1. What is your product's primary goal?
2. What does success look like for a user?
3. What are the failure modes that would hurt users or business?
4. How would you manually judge if an output is "good"?

From answers, help define:

• Primary metrics (e.g., accuracy, relevance, helpfulness)
• Secondary metrics (e.g., latency, cost, safety)
• Threshold targets (e.g., 95% accuracy, <5s latency)

Step 2: Design Dataset

Guide creation of test cases:

Copy# Test Case Template
name: descriptive_name
category: happy_path | edge_case | adversarial
inputs:
  # The inputs your agent receives
  query: "user query here"
  context: "any context"
outputs:
  # What to validate
  expected_fields: [field1, field2]
  should_mention: [keyword1, keyword2]
  should_not_contain: [forbidden_term]
  min_length: 100
  max_length: 5000

Categories:

• Happy Path (50%): Common, expected inputs
• Edge Cases (35%): Unusual but valid inputs, boundary conditions
• Adversarial (15%): Invalid inputs, prompt injection, error conditions

Step 3: Select Methods

Match methods to evaluation needs:

Step 4: Plan Automation

Integration tiers:

Tier 1: PR-Level (<5 min)

• Automated tests only
• Run on every PR
• Block merge on failure

Tier 2: Pre-Deploy (15-30 min)

• Full test suite including LLM-as-Judge
• Run before production deployment
• Compare to baseline metrics

Tier 3: Production Monitoring (Continuous)

• Sample real traffic for evaluation
• Track drift over time
• Alert on metric degradation

Step 5: Monitor Production

Track these signals:

1. Data Drift - Input distribution changing
2. Concept Drift - User expectations changing
3. Model Drift - Provider silently updating model
4. Task Drift - Users asking for new capabilities

Recommendation: Pin model versions, run weekly evals on production samples.

Output Format

After completing the framework, provide:

Copy## Evaluation Plan for [Product Name]

### Business Objectives
- Primary goal: [goal]
- Success criteria: [criteria]

### Dataset Strategy
- Total test cases: [N]
- Happy path: [N1] cases
- Edge cases: [N2] cases
- Adversarial: [N3] cases

### Evaluation Methods
| Metric | Type | Method | Threshold |
|--------|------|--------|-----------|
| ... | ... | ... | ... |

### CI/CD Integration
- PR checks: [list]
- Pre-deploy: [list]
- Monitoring: [list]

### Next Steps
1. [First action]
2. [Second action]
3. [Third action]

Templates

This skill includes starter templates in the templates/ directory:

• dataset.py - LangSmith dataset creation
• evaluators.py - Common evaluator implementations
• compare.py - Experiment comparison utilities

Examples

See examples/ for complete evaluation plans:

• research_squad_eval.md - Multi-agent research system evaluation

Related Resources

• LangSmith Evaluation Guide
• Anthropic Evaluation Best Practices
• Deep Agents Documentation