Build AI skills the way real AI teams work.
Design prompts, version them, compare alternatives, test on datasets, define structured outputs and tools, build agent workflows, evaluate quality, document findings, and turn experiments into portfolio-ready AI applications.
Saved, rollback-ready prompt versions.
Playground runs and comparisons.
Reusable test inputs with references.
AI app and workflow prototypes.
Reusable function/tool definitions.
Findings, reflections and research notes.
Professional workflow
Separate reusable instructions from variables and task inputs.
Use a dataset rather than trusting one good-looking response.
Run A/B or pairwise reviews and record why one version wins.
Use structured output schemas and tool contracts for predictable application behavior.
Record risks, metrics, limitations, test results and deployment assumptions.
Quick start
Starter blueprints
Prompt composer
Prompt quality
Rendered prompt preview
Experiment output
Run inspector
Run history
Backend-ready request
Use a server-side endpoint so provider secrets stay off the client.
Prompt payload
Prompt Library & Version History
Publish reusable prompts as versions, compare changes and roll back when needed.
Saved versions
Selected version
Side-by-side Prompt Comparison
Compare prompt variants before publishing. Paste prompts or load recent versions.
Create evaluation case
Evaluation methods
Required words, JSON validity, length and format rules.
Relevance, correctness, clarity, safety and completeness.
Compare two versions and record the preferred output.
Current eval dataset
| # | Input | Reference | Category | Risk | Weight |
|---|
Batch experiment
Run your current prompt against every eval case. In local mode Ethan AI Lab scores prompt/test alignment heuristically; with a backend it can store real outputs for review.
Experiment summary
Case results
| Input | Score | Status | Notes |
|---|
Experiment history
Dataset Manager
Build reusable test sets for prompt regression checks and AI quality experiments.
Dataset health
Cases
| Input | Reference | Category | Risk |
|---|
JSON Schema Studio
Output Validator
Validation report
Function / Tool Builder
Design checklist
Describe one action, not a whole workflow.
Use explicit parameter types and required fields.
Validate arguments before executing real actions.
Only expose tools an agent actually needs.
Tool catalog
Agent Blueprint Studio
Agent readiness
Agent blueprint
Saved agents
AI App / Workflow Canvas
Build stages
Define user and measurable need.
Prompt + tools + interface.
Dataset + rubrics + regressions.
Privacy, failures and oversight.
Backend, monitoring and iteration.
Project blueprint
Saved projects
AI Lab Notebook
Good experiment notes
Record what changed, why you changed it, what you expected, what actually happened and what you will test next.
Entries
Responsible AI is part of professional AI engineering.
Good systems combine model capability with verification, privacy, bias testing, security, transparent limitations and appropriate human oversight.
Verify
Important factual claims need independent checking against reliable sources or reference data.
Protect privacy
Do not expose passwords, secrets, confidential records or unnecessary personal data to model providers.
Evaluate bias
Test representative cases and inspect whether outputs unfairly disadvantage people or groups.
Human oversight
High-impact decisions should have meaningful human review and clear escalation paths.
Secure tools
Validate tool arguments, restrict permissions and require confirmation for consequential actions.
Document limitations
State what the system can and cannot reliably do, and monitor failures after deployment.