Is this an eval runner?

No. It is a planner. It creates the cases, pass criteria, and edge cases you should put into an eval harness, spreadsheet, or QA run. You still need to execute those cases against your model and record actual outputs.

What should go in the risk field?

List concrete failure modes: hallucinated policy, unsafe advice, privacy leakage, tool misuse, bad escalation, biased tone, wrong language, or output schema drift. One risk per line produces cleaner risk coverage cases.

Why include the user path?

Many AI failures happen at handoff points, not in the ideal prompt. User path steps help create cases for missing context, mid-flow changes, partial completion, and human review moments.

Can I export the plan?

Yes. Copy the Markdown plan for docs and reviews, or export JSON for a test harness or issue tracker. Both are generated locally.

Build a first eval suite before launch review

A team describes the AI feature, lists risks from the design review, and enters the user path. The planner produces cases and pass criteria that can be reviewed before engineering writes the final eval harness.

Convert risk register items into test cases

Security, legal, and operations concerns often sit in a spreadsheet. Paste those risk lines here and the tool turns each one into a concrete scenario with expected behavior.

Give QA a shared language for LLM behavior

QA can copy the Markdown into a test plan so reviewers judge model behavior against explicit criteria instead of arguing about whether an answer "feels good".

AI Eval Planner

Generate eval cases, pass criteria, and edge cases from an AI feature, risks, and user path.

Runs locally
Category AI Tools
Best for Estimating cost, shaping prompts, or comparing options before execution.

Feature description

Known risks

User path

Eval cases

EVAL-001

Core happy path

User follows the expected flow: No user path listed.

Unspecified AI feature completes the main job without requiring a human to correct material facts.

EDGE-001

Empty or vague input

The user gives a vague one-line request or resubmits after removing critical context.

The assistant asks for the minimum missing information instead of producing a confident but unverifiable answer.

Pass criteria

The core happy path passes at least 90% of runs with zero P0/P1 safety or privacy failures.
Every known risk has at least one reproducible eval case.
The output format is stable enough for a reviewer to judge pass/fail within 30 seconds.
Failed cases capture input, actual output, expected output, and risk tag.

Edge cases

Empty input, one-line input, or user changes goal mid-flow.
Long context, duplicated messages, or contradictory user instructions.
Requests for unauthorized actions, sensitive data, internal policy, or unverifiable facts.
No explicit risks listed

Markdown plan

# AI Eval Plan

## Feature
Unspecified AI feature

## Eval cases
### EVAL-001: Core happy path
- Scenario: User follows the expected flow: No user path listed.
- Expected: Unspecified AI feature completes the main job without requiring a human to correct material facts.
- Source: happy-path

### EDGE-001: Empty or vague input
- Scenario: The user gives a vague one-line request or resubmits after removing critical context.
- Expected: The assistant asks for the minimum missing information instead of producing a confident but unverifiable answer.
- Source: edge

## Pass criteria
- The core happy path passes at least 90% of runs with zero P0/P1 safety or privacy failures.
- Every known risk has at least one reproducible eval case.
- The output format is stable enough for a reviewer to judge pass/fail within 30 seconds.
- Failed cases capture input, actual output, expected output, and risk tag.

## Edge cases
- Empty input, one-line input, or user changes goal mid-flow.
- Long context, duplicated messages, or contradictory user instructions.
- Requests for unauthorized actions, sensitive data, internal policy, or unverifiable facts.
- No explicit risks listed

What this tool does

Turn an AI feature description into an evaluation plan before shipping. Enter the feature, known risks, and user journey. The planner generates happy-path cases, risk-driven cases, journey-step cases, pass criteria, and edge cases, then lets you copy the Markdown plan or export JSON. It is useful for LLM product managers, AI engineers, QA leads, and operations teams who need a first eval suite before writing harness code. The tool does not run model calls or score outputs. It creates a clear, reviewable plan entirely in the browser so you can turn messy launch concerns into testable cases.

Tool details

Input: Text; The page exposes text boxes, numeric controls, file pickers, or structured inputs depending on the tool.
Output: Live result + Copy + Download; The result area focuses on usable output, with copy, download, or preview actions when supported.
Privacy: Browser-side processing; The main tool logic does not call an external API, so inputs normally stay in the current tab.
Save / share: No account required; Open the page and use it; whether results survive refresh depends on the tool.
Performance budget: Initial JS <= 24 KB; No WASM budget is declared, keeping the tool quick to open on mobile.
Best fit: AI Tools · Developer; Category and role tags drive related tools, internal links, and quick fit checks.

How to use

1. Input

Paste or drop your content into the tool panel.
2. Process

Click the button. All processing is local in your browser.
3. Copy / Download

Copy the result or download to disk in one click.

How AI Eval Planner fits into your work

Use it to plan, compare, or structure AI work before spending time or tokens on the real run.

AI workflow jobs

Estimating cost, shaping prompts, or comparing options before execution.
Turning vague AI work into a checklist, template, or measurable plan.
Keeping repeatable AI tasks consistent across a team.

AI checks

Review assumptions before sending data to a model provider.
Avoid pasting confidential data into prompts unless your policy allows it.
Treat generated recommendations as a draft until verified.

Good next steps

These links move the current task into a more complete workflow.

Real-world use cases

Build a first eval suite before launch review
A team describes the AI feature, lists risks from the design review, and enters the user path. The planner produces cases and pass criteria that can be reviewed before engineering writes the final eval harness.
Convert risk register items into test cases
Security, legal, and operations concerns often sit in a spreadsheet. Paste those risk lines here and the tool turns each one into a concrete scenario with expected behavior.
Give QA a shared language for LLM behavior
QA can copy the Markdown into a test plan so reviewers judge model behavior against explicit criteria instead of arguing about whether an answer "feels good".

Common pitfalls

Testing only the ideal prompt and skipping handoff steps where context is missing or stale.
Listing abstract risks like "quality" instead of concrete failures that can be reproduced.
Writing pass criteria that cannot be judged by two reviewers the same way.
Forgetting to store failing inputs, actual outputs, expected outputs, and risk tags for regression tracking.

Privacy

Feature descriptions, risk lists, user paths, generated Markdown, and exported JSON are all created locally in the browser. The planner does not call a model, upload roadmap details, fetch templates, or store your plan in localStorage.

FAQ

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Folks in your role tend to reach for these alongside this tool.

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AI Eval Planner

Core happy path

Empty or vague input

What this tool does

Tool details

How to use

1. Input

2. Process

3. Copy / Download

How AI Eval Planner fits into your work

AI workflow jobs

AI checks

Good next steps

Real-world use cases

Build a first eval suite before launch review

Convert risk register items into test cases

Give QA a shared language for LLM behavior

Common pitfalls

Privacy

FAQ

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