Playbook: Non-technical Workflows & Checklists for Safe, Repeatable AI Experiments

Welcome — run safer, more useful AI experiments

This playbook helps non-technical staff run controlled AI experiments that produce reliable findings, reduce hidden risks, and create handoff-ready artifacts for engineers, data teams, or product owners. Use these steps to avoid wasted effort, surface meaningful issues early, and turn curiosity into usable results.

Why this matters

Ad hoc AI usage often creates inconsistent quality, unclear assumptions, and safety or compliance blind spots. A short, consistent workflow protects your team and makes experiment results trustworthy and actionable.

Quick overview of the workflow

1. Prepare test data and cover cases
2. Run controlled experiments
3. Validate outputs and document known limitations
4. Handoff to engineering with clear artifacts and escalation triggers

Step 1 — Prepare test data & cover cases

Goal: create a small, representative test set that exercises expected behavior, edge cases, and known failure modes.

• Define success: write one or two sentences describing what a successful output looks like for this experiment.
• Collect examples: 10–50 real or synthetic examples that represent typical inputs.
• Include edge cases: at least 3–5 items that might break the model (ambiguous input, unusual format, missing fields, domain-specific jargon).
• Label minimally: for each example, record the input, the desired/expected output (or acceptance criteria), and any special notes (e.g., regulatory sensitivity or PII).
• Data hygiene: remove or flag personally identifiable information unless you have explicit permission to use it. Note provenance: where the data came from and who approved its use.

Step 2 — Run controlled experiments

Goal: produce repeatable outputs and capture configuration so others can reproduce the run.

• Fix variables: record the model/service name, prompt, temperature/setting, tools/plugins, and any pre/post-processing steps.
• Run consistently: execute the same inputs under the same settings and capture the raw outputs. Run each input at least twice to check nondeterminism.
• Capture metadata: date/time, user, runtime environment (tool, browser, notebook), and any relevant API/version info.
• Keep everything: save prompts, system messages, tool outputs, and ephemeral system responses in a folder or experiment log so engineers can reproduce your steps.

Step 3 — Validate outputs & document known limitations

Goal: assess whether results meet your acceptance criteria and identify failure patterns that matter.

• Quick rubric (for each example): Acceptable / Needs Review / Unacceptable. Note the reason for review or rejection.
• Common checks:
  • Accuracy: factual correctness for the domain
  • Completeness: did the output address the required elements?
  • Safety & compliance: any sensitive content, PII leakage, or policy conflicts?
  • Bias & fairness: unexpected or harmful stereotyping?
  • Format: is the output in the required structure (CSV, JSON, short text, etc.)?
• Summarize failure modes: group failures into patterns (e.g., hallucinations on legal terms, poor handling of abbreviations) and estimate frequency.
• Estimate confidence: give an overall confidence level (High / Medium / Low) for using this approach in production without engineering changes.

Step 4 — Handoff to engineering with artifacts

Goal: provide a concise, reproducible package engineers can act on or escalate as needed.

Produce the following handoff artifacts:

• Experiment summary (one page): experiment name, goal, success definition, key findings, confidence, recommended next steps.
• Repro package (zip or repo): test data (with labels), exact prompts, model/settings, raw outputs, and a short runbook describing how to reproduce.
• Failure-mode list: clear examples with inputs, outputs, and why they fail the rubric.
• Privacy & risk notes: any data sensitivity, compliance concerns, or regulatory flags.
• Screenshots or sample transcripts that highlight representative good and bad outputs.

When to escalate immediately

• Outputs expose PII, PHI, or other regulated data.
• Model generates potentially libelous, discriminatory, or safety-critical instructions.
• Performance is unpredictably poor (widely inconsistent across runs).
• Experiment suggests a production or security vulnerability (e.g., prompt injection or data leakage).

Quick printable checklist

1. Defined success criteria and acceptance rubric
2. Collected representative test set + edge cases
3. Recorded model, prompt, and settings
4. Saved raw outputs and run metadata
5. Completed validation rubric for each example
6. Created handoff artifacts (summary, repro package, failure modes)
7. Flagged escalation if any critical risk found

Examples of simple validation tests

• Prompt injection test: include an instruction in input that tries to override task boundaries; observe whether output follows or resists it.
• Edge format test: submit malformed or incomplete input and confirm failure mode is safe and predictable.
• Repro test: run the same input twice under recorded settings—are results acceptably consistent?

Practical tips

• Keep experiments small and time-boxed (a few hours to a single day).
• Prefer clarity over cleverness in prompts—clear, explicit instructions are easier to reproduce and evaluate.
• Label assumptions you made (e.g., "assume date format is YYYY-MM-DD") so engineers can decide whether to encode or change them.
• If unsure about data sensitivity, pause and consult your privacy or legal team before using real data.

Next steps and adaptations

Use this playbook as a shared team template. Over time, capture successful experiment artifacts and common rubrics into a local toolkit or domain collection that teams can copy and extend.

Artifacts to reuse

• Experiment template (summary, rubric, runbook)
• Repro package structure (folders for inputs, outputs, prompts, metadata)
• Standard validation rubric and escalation matrix

End of playbook. Use this page to run experiments thoughtfully, reduce wasted effort, and create handoffs that lead to real improvements.