AI Config Variations

You're using a skill that will guide you through testing and optimizing AI configurations through variations. Your job is to design experiments, create variations, and systematically find what works best.

Prerequisites

• Existing AI Config (use aiconfig-create first)
• LaunchDarkly API access token or MCP server
• Clear hypothesis about what to test

Core Principles

1. Test One Thing at a Time: Change model OR prompt OR parameters, not all at once
2. Have a Hypothesis: Know what you're trying to improve
3. Measure Results: Use metrics to compare variations
4. Verify via API: The agent fetches the config to confirm variations exist

API Key Detection

1. Check environment variables — LAUNCHDARKLY_API_KEY, LAUNCHDARKLY_API_TOKEN, LD_API_KEY
2. Check MCP config — If applicable
3. Prompt user — Only if detection fails

Workflow

Step 1: Identify What to Optimize

What's the problem? Cost, quality, speed, accuracy? How will you measure success?

Step 2: Design the Experiment

Goal	What to Vary
Reduce cost	Cheaper model (e.g., gpt-4o-mini)
Improve quality	Better model or prompt
Reduce latency	Faster model, lower max_tokens
Increase accuracy	Different model (Claude vs GPT-4)

Step 3: Create Variations

Follow API Quick Start:

• POST /projects/{projectKey}/ai-configs/{configKey}/variations
• Include modelConfigKey (required for UI)
• Keep everything else constant except what you're testing

Step 4: Set Up Targeting

Use aiconfig-targeting skill to control distribution (e.g., 50/50 split for A/B test).

Step 5: Verify

1. Fetch config:

   GET /projects/{projectKey}/ai-configs/{configKey}
   

2. Confirm variations exist with correct model and parameters

3. Report results:

   • ✓ Variations created
   • ✓ Models and parameters correct
   • ⚠️ Flag any issues

modelConfigKey

Required for models to show in UI. Format: {Provider}.{model-id} — e.g., OpenAI.gpt-4o, Anthropic.claude-sonnet-4-5.

What NOT to Do

• Don't test too many things at once
• Don't forget modelConfigKey
• Don't make decisions on small sample sizes

Related Skills

• aiconfig-create — Create the initial config
• aiconfig-targeting — Control who gets which variation
• aiconfig-update — Refine based on learnings

References

• API Quick Start