Pilot And Evaluation Design

Pilot and Evaluation Design

Design the smallest credible pilot that can decide whether an industry R&D solution should move forward.

The goal is not to prove the solution is perfect. The goal is to reduce uncertainty enough to make a responsible build, stop, or iterate decision.

When to use this skill

Use this skill when:

• a solution candidate exists and needs validation;
• the user needs a PoC, MVP, pilot, or experiment plan;
• the work must connect technical metrics to business value;
• offline evaluation, backtesting, human review, or A/B testing is needed;
• risks such as compliance, fairness, explainability, latency, or adoption must be measured.

For purely academic experiments, use experiment-design. For unclear business problems, use industry-problem-framing first.

Workflow

1. State the pilot decision

Define the decision the pilot must support:

After this pilot, we will decide whether to [scale / redesign / stop / collect more data] based on [criteria].

Avoid pilots that only produce a demo. A useful pilot changes a decision.

2. Define scope and deployment boundary

Specify:

• user group or process included;
• data window;
• product or workflow touchpoint;
• whether the pilot is offline, shadow-mode, assisted-human, or live;
• who reviews outputs;
• what actions are allowed;
• what is explicitly out of scope.

Prefer the least risky design that still tests the core mechanism.

Common pilot modes:

3. Define baselines

Always compare against:

• current process;
• simple heuristic;
• simple statistical or ML baseline when relevant;
• published or standard method if feasible;
• human-only or human-with-tool condition when decision support is involved.

If a baseline cannot be implemented, explain why and how this weakens conclusions.

4. Choose metrics by decision role

Use four metric groups:

1. Primary success metric: the metric that decides go/no-go.
2. Diagnostic metrics: explain why the system works or fails.
3. Business metrics: connect to cost, revenue, risk, time, satisfaction, or compliance.
4. Guardrail metrics: ensure the pilot does not create unacceptable harm.

Examples:

• fraud/risk: precision@review-capacity, recall at fixed false-positive rate, loss captured, investigator workload;
• document AI: extraction F1, field-level accuracy, human correction time, hallucination rate;
• recommendation: uplift, conversion, churn, opt-out, fairness by segment;
• LLM support: answer groundedness, escalation rate, handling time, human acceptance, policy violation rate.

When the pilot pairs a metric with qualitative signals (interviews, adoption feedback, human review), design that combination intentionally rather than running two strands and stapling them together. references/mixed-methods-integration.md covers when mixing is worth it (methodological rationale, novel integrated insight), which design type fits (exploratory/explanatory sequential, convergent parallel, embedded), and the antipatterns to avoid (sample contamination, lost opportunity, integration failure) — grounded in Storey et al. (2025), arXiv:2404.06011.

5. Prevent invalid conclusions

Check for:

• target leakage;
• future information in features;
• biased labels from existing policy;
• selection bias in reviewed cases;
• seasonality and campaign effects;
• changed business rules;
• non-random pilot assignment;
• metric gaming;
• human review inconsistency;
• data drift.

If any risk is material, add a control or caveat.

6. Add ablations and stress tests

Use ablations only when they answer a decision-relevant question.

Examples:

• remove graph features to test whether network structure matters;
• compare retrieval-only vs generation-with-retrieval for LLM systems;
• compare model score vs model score plus explanation for human adoption;
• test performance by customer segment, branch, channel, or time period;
• stress test out-of-distribution months or rare cases.

7. Set go/no-go criteria

Define thresholds before seeing results.

Go: improves primary metric by X while guardrails remain within Y.
Iterate: improves diagnostics but misses business threshold.
Stop: does not beat current process or violates guardrails.
Collect data: uncertainty dominated by label/data gaps.

Use confidence intervals or repeated time splits when sample size allows.

Output format

# Pilot and evaluation design: [solution]

## Pilot decision

## Pilot scope
- Mode:
- Users/process:
- Data window:
- Deployment boundary:
- Out of scope:

## Hypothesis
If we ..., then ..., because ..., compared with ...

## Baselines

## Metrics
| Metric | Type | Why it matters | Decision threshold |
|---|---|---|---|

## Evaluation protocol
- Data split/replay design:
- Human evaluation design:
- Statistical analysis:
- Leakage controls:
- Monitoring:

## Ablations and stress tests

## Risks and guardrails

## Go/no-go criteria

## Pilot deliverables

Quality bar

A good pilot design should be small, safe, decision-relevant, and honest about what it cannot prove.