Configs Variations

Config Variations

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

Prerequisites

This skill requires the remotely hosted LaunchDarkly MCP server to be configured in your environment.

Primary MCP tool:

clone-ai-config-variation – clone a baseline variation with selective overrides (recommended for experimentation)

Alternative MCP tools (for more control):

get-ai-config – review existing variations before adding new ones
create-ai-config-variation – create new variations from scratch

Optional MCP tools:

update-ai-config-variation – refine a variation after creation
delete-ai-config-variation – remove variations that didn’t work out

Core Principles

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

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 more detailed prompt
Reduce latency	Faster model, lower `max_tokens`
Increase accuracy	Different model family (Claude vs GPT-4)

Step 3: Create Variations (Recommended: Clone with Overrides)

Use clone-ai-config-variation to duplicate the baseline and override only what you’re testing. The tool reads the source variation, merges your overrides, and creates the new variation. Everything you don’t pass is inherited from the source automatically.

Required fields:

sourceVariationKey – the baseline to clone from
key and name – identifiers for the new variation (e.g., gpt4o-mini-cost-test)

Override ONLY the fields you are testing. Leave all other fields unset – do not pass them even if you know their current values. The clone tool inherits them from the source. This enforces the one-variable-at-a-time principle:

Testing a cheaper model? Pass only modelConfigKey and modelName. Do NOT pass instructions, messages, or parameters.
Testing different instructions? Pass only instructions. Do NOT pass modelConfigKey or modelName.
Testing a parameter? Pass only parameters. Do NOT pass model or prompt fields.

The response returns both the source and created variation, so you can immediately verify the diff.

Step 3 (Alternative): Create from Scratch

If you need full control, use get-ai-config first to review the current state, then create-ai-config-variation with all fields specified manually. Always fetch before creating so you understand the existing config’s mode, model, and parameters.

Step 4: Verify

If you used clone-ai-config-variation, the response includes both source and created variations for immediate comparison. Otherwise, use get-ai-config to confirm.

Report results:

Variations created with correct models and parameters
Only the intended variable differs between variations
Flag any issues

Note on API responses: After calling a creation or clone tool, treat a successful response as confirmation that the operation succeeded. The API response may not echo back every field you sent (e.g., model fields may show defaults). Do not retry or assume failure based on response field values alone – verify with get-ai-config if needed.

modelConfigKey Format

Required for models to display in the UI. Format: {Provider}.{model-id}:

OpenAI.gpt-4o, OpenAI.gpt-4o-mini
Anthropic.claude-sonnet-4-5, Anthropic.claude-3-5-sonnet

Safety: Protect the Baseline

When the user wants to try a different model, prompt, or parameters, always create a new variation alongside the baseline. Never modify or delete the existing baseline variation. This applies even if the user says “replace” or “switch” – the correct action is to create a new variation and let targeting/rollouts control traffic, not to edit the original.

Use clone-ai-config-variation or create-ai-config-variation to add the new variation
Do NOT use update-ai-config-variation on the baseline to change its model or instructions
Do NOT use delete-ai-config-variation on the baseline
Explain to the user that keeping the baseline enables comparison and safe rollback

What NOT to Do

Don’t test too many things at once – change one variable per variation
Don’t pass unchanged fields when cloning – let the tool inherit them from the source
Don’t forget modelConfigKey (variations without it show as “NO MODEL” in the UI)
Don’t make decisions on small sample sizes
Don’t modify or remove the baseline variation – create new variations alongside it
Don’t use update-ai-config-variation to “replace” a baseline – create a new variation instead

More resources

To learn more about creating and managing variations, read Create and manage config variations (opens in a new tab).

configs-create – Create the initial config
configs-update – Refine based on learnings