01
Define the decision
Turn “is it good enough?” into a named owner, user outcome, failure budget, and launch threshold.
Evaluation & release workflow
Ship LLM features with evidence—not vibes.
A downloadable evaluation system for RAG and chat features: datasets, a regression runner, launch gates, and human-review workflows.
No dashboard · No customer-data upload · Model-provider neutral
A release decision should be reviewable.
Prompt, model, retrieval, and tool changes can alter the customer experience. LaunchGate gives a small team a practical way to measure the change before it reaches users.
02
Test real risks
Build a versioned case set around normal tasks, known failures, boundaries, and tool or retrieval breakdowns.
03
Gate the release
Review correctness, critical failures, latency, cost, and human-review load before shipping, holding, or rolling back.
From examples to an explicit release gate.
Representative cases
→
Observable expectations
→
Reproducible results
→
Ship / hold decision
Lightweight by design.
Start locally with files your team controls. The included Python runner does not call an LLM; it evaluates the results you generate in your own approved environment.
- Evaluation specification and release checklist
- Failure taxonomy and reviewer rubric
- JSONL examples and version registry
- Dependency-free Python gate and unit tests
- GitHub Actions starter workflow
- Privacy and incident checklist
Start with the level you need.
The kit is self-serve and priced in Korean won. Custom implementation, if offered, is a separate fixed-scope engagement.
Individual
₩49,000
For one builder who needs a practical release baseline.
- Templates and synthetic examples
- Python gate and tests
- Personal/internal-use license
Team
₩199,000
For a product or agency team building a shared release workflow.
- Everything in Individual
- CI workflow starter
- Named-organization internal-use license
Checkout links will open after seller verification.
In the meantime, take the free 3-minute release check, or download the printable scorecard and five-case sample. This page will be updated with live checkout links once verification is complete.
Questions before you download.
Does it include a model or API credits?
No. LaunchGate is provider-neutral. You use your own approved accounts and data.
Does it guarantee safety or compliance?
No. It helps establish an engineering release process; product, legal, security, and operational decisions remain yours.
Can it work with existing evaluation tools?
Yes. The templates and thresholds are portable. The included runner is intentionally simple.
What examples should we use?
Synthetic, approved, or properly redacted cases only. Do not place secrets or unnecessary personal data in test fixtures.
Make the next AI release a decision you can explain.
Start with the scorecard and synthetic sample. Live checkout will be added after seller verification.