Using AI · Growth Library
Benchmark AI on the Task Before You Trust the Workflow
Test a small representative sample before turning assistance into a process
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Choose a small sample containing easy, ordinary, and awkward cases. Define what counts as acceptable before running the model. Compare AI output with the source or a trusted human result, record failure patterns, and decide which cases can be assisted, which need review, and which should stay human-first.
Check-in: Do I know the cases where AI is reliable enough to help and the cases where its polished answer should not be trusted?
Evidence: Reviewed · source · Dell'Acqua et al. — Navigating the Jagged Technological Frontier
Review note: Reviewed against Dell'Acqua et al. on the jagged technological frontier. The evidence supports task-level evaluation and warns against assuming capability transfers to adjacent tasks. This protocol operationalizes that boundary through representative cases and acceptance criteria.
The useful question is rarely “Is this model smart?” It is “Does this model help on this exact step, with these inputs, under these failure costs?” The difference saves a great deal of automation theatre.
1. Build a representative sample
Include ordinary cases and the awkward cases that usually create rework. If you test only clean examples, you learn how the demo behaves, not how the workflow behaves.
2. Define acceptance before seeing output
Write the criteria first. Otherwise a fluent answer can quietly lower the standard after the fact.
3. Compare and classify failures
Mark errors by type: missing fact, invented fact, wrong calculation, bad judgment, format drift, privacy problem, or another failure that matters in your context.
4. Route work by risk
Use the result to separate safe assistance from mandatory review and human-only cases. Re-test after major model, prompt, data, or workflow changes.
Questions to consider
Why test the task instead of judging the model in general?
Because performance can change sharply between tasks that look similar. A model that is useful for one step in a workflow may be unreliable on the next step.
What should I score?
Use criteria tied to the real task: factual correctness, completeness, required format, safety, policy compliance, calculation accuracy, or another observable acceptance condition.
Sources
Evidence state: practical. Sources are provided so the underlying material can be inspected directly.
Maintenance record
Review history
Current status: Reviewed. An editorial or evidence review is recorded and no later event changes that conclusion.
2026-09-15 · reviewed · Brali editorial agent Source: review-registry-using-ai-starters.json
Make the jagged-frontier result executable as a low-risk workflow evaluation protocol rather than a generic claim about AI productivity.
Browse the review ledger · Challenge or update this hack
Implementation observations can trigger a review, but they cannot change an evidence conclusion by themselves. Commercial relationships do not control review status or outcomes.
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Article versions
Brali keeps substantive article history visible. Later reviews may refresh wording, sources, examples, or boundaries without silently replacing the record.
- Version 2026-09-15: first recorded publication in the migrated Brali corpus.
- Evidence review 2026-09-15: reviewed by Brali editorial agent; evidence state is
reviewed.
Canonical source record · Evidence state: reviewed.