model-evaluation

name	model-evaluation
description	Evaluate and compare models for a use case — quality, latency, cost, reliability, and safety benchmarks on your own data.
argument-hint	[AI use case or feature to select a model for]
user-invocable	true
allowed-tools	Read, Write, Edit, Bash, Glob, Grep

Evaluate and select a model for $ARGUMENTS.

Process (sequential — do not skip steps)

Step 1: Requirements Definition

Define pass/fail criteria before evaluating any model. These are hard requirements, not nice-to-haves.

Requirement	Question	Example
Quality threshold	What is the minimum acceptable accuracy on your eval set?	>= 90% accuracy on classification, >= 85% on generation
Latency budget	What is the maximum acceptable response time?	p95 < 2 seconds TTFT, p95 < 5 seconds total
Cost budget	What can you spend per request and per month?	< $0.02 per request, < $3,000/month at projected volume
Context window	How much input data must fit in a single request?	Typical: 2K tokens, maximum: 15K tokens
Reliability	What error and timeout rates are acceptable?	< 0.5% error rate, < 1% timeout rate
Safety	What refusal rate on valid inputs is tolerable?	< 2% false refusal rate on legitimate requests

If any requirement is undefined, stop and clarify. Evaluating without requirements is benchmarking for entertainment, not engineering.

Step 2: Candidate Selection

Identify 2-4 candidate models using the tiered strategy. Do not evaluate more than 4 — diminishing returns.

Tier	Model class	When to consider	Cost profile
Fast	Small/cheap (Haiku-class)	Classification, extraction, formatting, routing, mechanical tasks	Lowest
Standard	Mid-range (Sonnet-class)	Most features — summarisation, analysis, general generation	Moderate
Capable	Large (Opus-class)	Complex reasoning, creative generation, critical decisions	Highest

Default to Standard. Only include Capable-tier candidates when Standard demonstrably fails on representative examples. Only include Fast-tier when the task is mechanical and Standard is unnecessarily expensive.

Select candidates:

#	Model	Tier	Provider	Rationale for inclusion
C1
C2
C3

Step 3: Evaluation Dataset

Build a representative eval set from real usage data. Synthetic data is acceptable only when real data does not yet exist.

Minimum requirements:

• 50 examples total
• Happy path: 60% of examples — typical, well-formed inputs
• Edge cases: 25% of examples — boundary conditions, unusual but valid inputs
• Adversarial: 15% of examples — malformed inputs, injection attempts, contradictory data

Each example must include:

• Input data (the actual prompt input)
• Expected output (the correct/ideal response)
• Scoring criteria (how to judge correctness — exact match, semantic similarity, rubric)

Store the eval set in version control alongside the prompts. The eval set is as important as the code.

Step 4: Evaluation Dimensions

Measure every candidate across all six dimensions. No partial evaluations — a model that scores well on quality but has unknown latency is not evaluated.

Dimension	Metric	How to measure	Weight
Quality	Eval set accuracy	Run all 50+ examples, score against expected outputs. Use automated scoring where possible, human review for subjective quality	Primary
Latency	TTFT + total generation time	Time each request. Report p50, p95, p99. Test at expected concurrency, not just sequential	High
Cost	Per-request cost	(input tokens x input price) + (output tokens x output price) x projected volume	High
Reliability	Error rate + timeout rate	Run eval set 3 times. Record failures, timeouts (>30s), inconsistent outputs across runs	Medium
Context window	Maximum input capacity	Test with largest expected input. Verify quality does not degrade near the window limit	Pass/fail
Safety	Refusal rate on valid inputs	Count how many legitimate eval examples the model refuses. Also test adversarial inputs from the eval set	Medium

Step 5: Run Evaluation

Test each candidate against the full eval set under consistent conditions.

Methodology rules:

• Same prompts for every candidate. Adjust only for model-specific formatting requirements (e.g., system/user message structure)
• Same eval data, same order, same conditions
• Temperature 0 for deterministic tasks. Record temperature setting for non-deterministic tasks
• Run at least 3 passes to measure consistency. A model that scores 95% once and 70% the next time is not a 95% model
• Record raw outputs for every example — you will need them for failure analysis

Record results per candidate:

### [Model Name] — Results

| Dimension | Result | Meets requirement? |
|---|---|---|
| Quality | [X]% accuracy (N/50 correct) | YES / NO |
| Latency (p95) | TTFT: [X]ms, Total: [X]ms | YES / NO |
| Cost | $[X] per request, $[X]/month projected | YES / NO |
| Reliability | [X]% error rate, [X]% timeout rate | YES / NO |
| Context window | Tested at [X] tokens, quality maintained | YES / NO |
| Safety | [X]% refusal rate on valid inputs | YES / NO |

**Failure analysis:**
- [List specific eval examples that failed and why]
- [Common failure patterns]

Step 6: Comparison Analysis

Side-by-side comparison. The question is not "which model is best" but "which model meets all requirements at the lowest cost."

Dimension	Requirement	C1: [model]	C2: [model]	C3: [model]
Quality	>= [X]%	[score]	[score]	[score]
Latency (p95)	< [X]ms	[value]	[value]	[value]
Cost/request	< $[X]	[value]	[value]	[value]
Cost/month	< $[X]	[value]	[value]	[value]
Reliability	< [X]% errors	[value]	[value]	[value]
Context window	[X] tokens	[pass/fail]	[pass/fail]	[pass/fail]
Safety	< [X]% refusals	[value]	[value]	[value]
All requirements met?		YES / NO	YES / NO	YES / NO

Trade-off documentation:

• If multiple candidates meet all requirements, select the cheapest
• If no candidate meets all requirements, document which requirements are missed and by how much. Escalate — do not compromise requirements silently
• If quality is close between two models, prefer the cheaper one. A 1% quality improvement rarely justifies 3x cost

Step 7: Decision and Fallback

Document the selection with reasoning tied directly to requirements. No vague justifications.

Decision template:

### Selected model: [model name]

**Rationale:**
- Meets all [N] requirements
- Quality: [X]% (above [threshold]% threshold)
- Latency: [X]ms p95 (within [budget]ms budget)
- Cost: $[X]/request, $[X]/month (within $[budget]/month budget)
- [Specific advantage over other candidates that met requirements]

**Trade-offs accepted:**
- [What you sacrifice by choosing this model over alternatives]
- [Any requirement that is met with thin margin — risk area]

Fallback plan (MANDATORY):

Scenario	Fallback action
Primary model unavailable	Route to [fallback model] — tested against eval set, meets requirements with [trade-off]
Sustained latency degradation	Switch to [faster model] with [quality trade-off described]
Cost spike	Rate limit to [N] requests/minute, alert team
Quality degradation detected	Roll back to previous model version, trigger re-evaluation

Without a fallback plan, you are one outage away from a broken feature.

Anti-Patterns (NEVER do these)

• Choosing based on benchmarks, not your data — public benchmarks measure general capability. Your use case is specific. Evaluate on YOUR eval set
• No eval set — picking a model without evaluation data is guessing. Build the eval set first
• Evaluating on happy path only — edge cases and adversarial inputs reveal the real quality gap between models
• Ignoring cost — a feature that works perfectly but costs 10x budget is not a working feature. Cost is a first-class metric
• No fallback plan — every AI call can fail. Every model can degrade. Plan for it
• Comparing under different conditions — different prompts, different data, different concurrency levels. Evaluation must be controlled
• Evaluating once and forgetting — model performance changes over time (provider updates, traffic patterns). Schedule re-evaluation quarterly

Output Format

# Model Evaluation: [use case]

## Requirements
| Requirement | Threshold |
|---|---|

## Candidates
| # | Model | Tier | Provider | Rationale |
|---|---|---|---|---|

## Evaluation Dataset
- Total examples: [N]
- Happy path: [N] | Edge cases: [N] | Adversarial: [N]
- Location: [path to eval set]

## Results

### [Model 1]
[Per-dimension results table + failure analysis]

### [Model 2]
[Per-dimension results table + failure analysis]

## Comparison
[Side-by-side table with pass/fail per requirement]

## Decision
- **Selected:** [model]
- **Rationale:** [tied to requirements]
- **Trade-offs:** [what you sacrifice]

## Fallback Plan
| Scenario | Action |
|---|---|

## Re-evaluation Schedule
- Next evaluation: [date]
- Trigger conditions: [what forces an early re-evaluation]