Evaluate an AI feature against real tasks
Evaluate the feature against real user jobs, realistic failure patterns, and operational constraints so the team learns whether the system actually helps, not just whether it performs well on curated examples.
- Situation
- An AI feature needs evaluation beyond synthetic benchmarks or generic demos.
- Goal
- Replace proxy confidence with task-grounded evidence about usefulness, correctness, risk, and failure behavior.
- Do not use when
- the feature is still too undefined to know what task it serves
- Primary owner
- evaluation owner
- Roles involved
AI engineerproduct ownerevaluation ownerdomain expertsrepresentative users or user proxiesrisk or quality partner when needed
Context
The situation
Deciding whether to reach for this playbook: when it fits, and when it doesn't.
Use when
Conditions where this playbook is the right tool.
- A team is deciding whether an AI feature is ready to ship or scale
- Benchmarks look good but user value is uncertain
- Real-world behavior matters more than lab performance
- The system affects workflow quality, accuracy, or decision-making
Stakes
Why this matters
What this playbook protects against, and why skipping or half-running it tends to be expensive.
AI features often fail in the gap between benchmark success and actual work. Real evaluation reveals whether the feature helps on the messy edges: ambiguous prompts, incomplete evidence, user misuse, changing context, and operational pressure.
Quality bar
What good looks like
The observable qualities of a team or system that is actually doing this well. Not just going through the motions.
Signs of the playbook done well
- Evaluation is tied to concrete user tasks and success criteria
- The team knows the feature’s strong cases, weak cases, and unsafe cases
- Measurement includes usefulness, failure shape, and review burden
- Go/no-go decisions are legible and evidence-based
- Benchmark results are contextualized instead of treated as truth
Preparation
Before you start
What you need available and true before running the procedure. Skipping this is the most common reason playbooks fail.
Inputs
Material you'll want to gather first.
- Feature scope
- Target user personas and tasks
- Representative prompts or inputs
- Ground truth or review criteria where possible
- Benchmark results if available
- Human workflow expectations
Prerequisites
Conditions that should be true for this to work.
- The team knows what user task the feature claims to improve
- There is access to realistic task examples
- Someone owns the evaluation design rather than treating it as a side job
Procedure
The procedure
Each step carries its purpose (why it exists), its actions (what you do), and its outputs (what you produce). Read the purpose. It's what keeps the step from degenerating into checklist theatre.
Define the real task and failure cost
Make evaluation answer the right question.
Actions
- Write the task in user terms, not model terms
- Define what good, acceptable, weak, and harmful performance look like
- State what kinds of failure matter most
Outputs
- Task evaluation definition
Assemble representative task cases
Prevent evaluation from overfitting to convenience examples.
Actions
- Collect real or realistic examples spanning common, tricky, and edge cases
- Include ambiguity, incomplete context, and failure-prone scenarios
- Tag the cases by risk, domain, and expected behavior
Outputs
- Task case set
Measure outcome, not just output
Check whether the feature improves the workflow.
Actions
- Evaluate answer quality, user effort reduction, review burden, and decision confidence
- Measure where human correction is needed and how often
- Compare against baseline workflow, not only against itself
Outputs
- Task outcome scorecard
Analyze failure shape
Understand how the feature goes wrong, not just how often.
Actions
- Cluster errors into categories like omission, hallucination, overconfidence, bad retrieval, or unsafe shortcut
- Identify failure cases that users are unlikely to catch
- Separate tolerable errors from unacceptable ones
Outputs
- Failure taxonomy
Make an evidence-based shipping decision
Tie launch decisions to task reality.
Actions
- State where the feature is ready, limited, or unsafe
- Define mitigations such as stronger review, narrower scope, or better retrieval
- Record what evidence would justify broader rollout later
Outputs
- Shipping recommendation
Judgment
Judgment calls and pitfalls
The places where execution actually diverges: decisions that need thought, questions worth asking, and mistakes that recur regardless of good intent.
Decision points
Moments where judgment and trade-offs matter more than procedure.
- What user task are we truly evaluating?
- What failure is unacceptable even at low frequency?
- Does the feature reduce user effort enough to justify its new risks?
- Where should this feature be limited or gated?
Questions worth asking
Prompts to use on yourself, the team, or an AI assistant while running the procedure.
- What real user job does this feature improve?
- Which failures matter most because users will not catch them?
- How does this compare to the current workflow, not just to a benchmark?
Common mistakes
Patterns that surface across teams running this playbook.
- Evaluating generic helpfulness instead of a concrete job
- Overfitting evaluation to easy examples
- Using benchmark lifts as a substitute for workflow evidence
- Ignoring the cost of human review or correction
Warning signs you are doing it wrong
Signals that the playbook is being executed but not landing.
- The team can quote eval scores but not real task outcomes
- The feature performs well in demos and poorly in messy use
- Failure discussions focus on rate but not on detectability or harm
- Shipping decisions happen before failure shape is understood
Outcomes
Outcomes and signals
What should exist after the playbook runs, how you'll know it worked, and what to watch for over time.
Artifacts to produce
Durable outputs the playbook should leave behind.
- Task evaluation definition
- Task case set
- Task outcome scorecard
- Failure taxonomy
- Shipping recommendation
Success signals
Observable changes that mean the playbook landed.
- The team understands where the feature truly helps
- Unsafe or low-value use cases are identified before broad rollout
- Task-grounded evaluation changes design or launch decisions
- Benchmark discussion becomes secondary to workflow evidence
Follow-up actions
Moves that keep the playbook's effects compounding after it finishes.
- Refresh the case set as real usage broadens
- Connect repeated failure classes to model, retrieval, or UX changes
- Track whether live behavior matches pre-launch task evaluation
Metrics or signals to watch
Longer-horizon indicators that the underlying problem is receding.
- Task success rate
- Human correction rate
- Review or validation time per output
- Unacceptable failure frequency
- User trust or usefulness ratings by task
AI impact
AI effects on this playbook
How AI-assisted and AI-driven workflows help execution, and the ways they can make it worse.
AI can help with
Where AI tooling genuinely reduces the cost of running this playbook well.
- Drafting case taxonomies and evaluation sheets
- Clustering failure patterns from evaluation runs
- Summarizing differences between benchmark and task-grounded outcomes
AI can make worse by
Distortions AI introduces that make the underlying problem harder to see.
- Generating synthetic cases that are too clean
- Masking weak task fit behind persuasive evaluation summaries
- Inflating apparent rigor with more metrics that still miss user reality
Relationships
Connected playbooks
Failure modes this playbook tends to address, decisions behind the situation, red flags that motivate running it, and neighboring playbooks.