Search the problem you are solving—not the buzzword.

Judge a task by variation, verifiability, stakes, data boundaries, and fallback before choosing a model or building a workflow.

Replace a vague AI feature request with a one-page brief that names the user, input, acceptable output, evidence, boundaries, and fallback.

Build a deliberately small test set with ordinary, difficult, ambiguous, and unsafe cases so a promising demo becomes evidence.

Separate instructions, source material, examples, history, tools, and output rules to see what the model actually receives.

Define required fields, evidence, tone, uncertainty, and rejection conditions before optimizing prompt wording.

Trace a bad answer to missing input, conflicting instructions, weak evidence, capability limits, or a broken handoff.

Compare knowledge volatility, source volume, citation needs, access rules, and maintenance cost before adding retrieval.

Audit whether each important claim is supported by the cited passage, uses the right version, and survives a freshness check.

Place each action on a five-level permission ladder, from read-only suggestions to explicitly approved irreversible work.

Write the stop signal, saved state, owner, rollback action, and safe retry rule before automation reaches production.

Weight quality, latency, tool use, privacy, recovery, and operating constraints using cases from the workflow you will ship.

Count input, output, retries, tools, waiting, review, and failure recovery instead of comparing a single token price.

Route every field through allow, transform, isolate, or exclude so a useful workflow does not quietly become an uncontrolled data transfer.

Sample real task slices, write reviewable references, calibrate graders, and version the set so model changes produce trustworthy comparisons.

Choose review gates from impact, reversibility, and uncertainty, then give reviewers the evidence and actions needed to make a real decision.

Watch task mix, inputs, dependencies, outcomes, and human overrides against a named baseline so every alert leads to an inspectable response.