Know when your AI's answers get worse.
We build evaluations that catch poor answers before release and show when quality changes in production.
| Test case | Before | Candidate |
|---|---|---|
| Known question | Cites evidence | Cites evidence |
| Missing evidence | Guesses | Asks for context |
| Restricted record | Blocks access | Blocks access |
Same cases. Same criteria. A visible change in behavior.
What we put in place
We turn real user questions and failed answers into repeatable checks for your AI product. Your team can compare a prompt, model or data change before release and see where answers get worse. We also watch live results and add new failures to the checks as the product changes.
For AI products with inconsistent answers, repeated prompt changes or quality problems that uptime monitoring cannot explain.
- Build evaluation cases
- Compare before release
- Monitor live quality
Cases from real work
We collect user questions, failed runs and known edge cases, then turn them into a set of checks the team can rerun.
Scoring with clear criteria
We use direct checks for clear rules and compare human and model scores when an answer needs judgment.
Checks before release
We compare prompt, model and data search changes against the same baseline and agree what must pass before release.
Monitoring after release
We sample live results, investigate quality drops and add new failures to the repeatable checks.
Example workflow
See what a prompt change affects
For a team updating an AI feature that already serves customers.
Capture a baseline
Keep representative tasks, expected behavior and results from the current version.
Compare the change
Run the proposed prompt or model against the same cases and scoring rules.
Make a release decision
Review regressions alongside response time and cost before rolling out the change.
What we verify: A better average does not hide a failure on an important customer task.
What should each check tell you?
What should each check tell you?
Quality checks complement the monitoring already keeping your application online.
Is the service responding?
- Use
- Operational monitoring
- What it reveals
- Availability, errors and response time
Did this change harm known cases?
- Use
- A regression evaluation set
- What it reveals
- Changes against an agreed baseline
Is the answer useful to a person?
- Use
- Task-specific scoring and review
- What it reveals
- Whether the result meets the user's need
What failures are appearing now?
- Use
- Production sampling
- What it reveals
- Cases the existing evaluation set missed
| The question | Use | What it reveals |
|---|---|---|
| Is the service responding? | Operational monitoring | Availability, errors and response time |
| Did this change harm known cases? | A regression evaluation set | Changes against an agreed baseline |
| Is the answer useful to a person? | Task-specific scoring and review | Whether the result meets the user's need |
| What failures are appearing now? | Production sampling | Cases the existing evaluation set missed |
How we work together
Review actual failures
We collect actual questions, answers and failed runs, then agree what a good result looks like for each task.
Build a baseline
We build a set of cases and check that its scores match how your team judges the answers.
Connect release checks
We connect those cases to your release process so a harmful change is visible before it reaches users.
Monitor and refresh
We review live results with you and update the cases as users, data and models change.
Common questions
How many evaluation cases do we need?
How many evaluation cases do we need?
Enough to cover the important tasks and known failures. We start with representative cases, then expand the set when new failure patterns appear rather than aiming for an arbitrary count.
Can a model judge another model's answers?
Can a model judge another model's answers?
It can help, but its scores need checking against human judgment. We use direct rules for objective requirements and calibrate model-based scoring for the parts that need interpretation.
Can evaluations explain a quality drop without a code change?
Can evaluations explain a quality drop without a code change?
They can help locate it. We compare model versions, data, prompts, retrieval and configuration against a recorded baseline, then investigate the changed behavior.
How are LLM evaluations different from uptime monitoring?
How are LLM evaluations different from uptime monitoring?
Uptime tells you whether the service responds. Evaluations ask whether the response is acceptable for the task. A system can be available while giving poor answers.
Need checks before your next AI release?
Bring a few real examples. We'll help you turn them into useful quality checks.
or reach us at hello@phaseahead.ai