No task-level evals exist before launch
An AI system is approaching launch without evaluations tied to the actual tasks users need it to perform.
- Where you see this
AI feature launchesmodel provider comparisonsRAG prototypesinternal copilots
- Not necessarily a problem when
- the launch is explicitly a private discovery prototype with no user trust surface
- Often mistaken for
- the demo works
- Time horizon
- immediate
- Best placed to act
AI feature ownerproduct ownertech lead
The signal
What you would actually notice
AI behavior that looks good in examples can fail the workflows users actually trust it to perform.
Field observation
The team can show demos, benchmark scores, or example outputs, but not a task-grounded eval set with pass/fail expectations.
Also observed
- We tested representative prompts.
- The model is much better on the benchmark.
- We can add evals after launch.
Primary reading
What it usually indicates
Most likely underlying patterns when this signal shows up. Not a diagnosis, a starting hypothesis.
Usually indicates
Most likely underlying patterns when this signal shows up.
- weak evaluation discipline
- benchmark mirage
- synthetic evaluation dependence
- unclear product success criteria
Stakes
Why it matters
AI behavior that looks good in examples can fail the workflows users actually trust it to perform.
Inspection
What to check next
Deliberate steps to confirm or disconfirm the primary reading above. Not a checklist. An order of inspection.
- task inventory
- eval harness
- launch criteria
- production failure taxonomy
- human review plan
Diagnostic questions
Questions to ask the team, or yourself, before concluding anything.
- What tasks must the system perform reliably?
- Which tasks are represented in evaluation?
- What failure cases would block launch?
- Who owns eval freshness after launch?
Progression
Under the signal
Where this pattern tends to come from, what's holding it up, and where it goes if nothing changes.
Leading indicators
What tends to show up first.
- the eval conversation centers on models rather than tasks
- quality examples are hand-picked
- support and product teams have not contributed failure cases
Common root causes
What is usually sitting under the signal.
- benchmark-driven model selection
- pressure to launch demos quickly
- unclear product task framing
- no eval owner
Likely consequences
What happens if nothing changes.
- production failures missed by demos
- benchmark mirage
- support surprise
- low trust in AI behavior
Look-alikes
Not what it looks like
Patterns that can be mistaken for this signal, and 'fix' attempts that make it worse.
- the demo works
- the model scores are strong
- we will monitor after launch
Anti-patterns when responding
Responses that feel sensible and usually make the underlying pattern worse.
- using vendor benchmarks as launch evidence
- testing only happy-path prompts
- treating screenshots as evaluation
Context
Context and ownership
Where this signal surfaces, who sees it first, who can actually act, and how much runway there usually is before escalation.
Where it shows up
- AI feature launches
- model provider comparisons
- RAG prototypes
- internal copilots
Who sees it first
Before it escalates.
- ML engineer
- QA lead
- product manager
Who can move on it
Not always the same as who notices it.
- AI feature owner
- product owner
- tech lead
immediate
How much runway there usually is before the signal hardens into the underlying pattern.
AI impact
AI effects on this signal
How AI-assisted and AI-driven workflows tend to amplify or hide this signal.
AI amplifies
Ways AI tooling tends to make this signal louder or more common.
- AI can generate impressive demos and many plausible examples without grounding them in user tasks.
AI masks
Ways AI tooling tends to hide this signal, so it keeps growing under the surface.
- AI-generated eval examples can make coverage look broader than it is.
Relationships
Connected signals
Related failure modes, decisions behind the signal, response playbooks, and neighboring red flags.