aiconfig-targeting

SKILL.md

AI Config Targeting

Configure targeting rules for AI Configs to control which variations serve to different contexts. Works the same for both completion and agent mode.

Prerequisites

LaunchDarkly account with AI Configs enabled

API access token with write permissions

Project key and environment key

Existing AI Config with variations (use aiconfig-create skill)

API Key Detection

Check environment variables - LAUNCHDARKLY_API_KEY, LAUNCHDARKLY_API_TOKEN, LD_API_KEY

Check MCP config - Claude: ~/.claude/config.json -> mcpServers.launchdarkly.env.LAUNCHDARKLY_API_KEY

Prompt user - Only if detection fails

Core Concepts
Evaluation Order

Targeting rules evaluate in this order (same as feature flags):

Individual targets - Specific context keys (highest priority)

Segment rules - Pre-defined segments

Custom rules - Attribute-based conditions (evaluated in order)

Default rule - Fallthrough for all others

Off variation - When targeting is disabled

Semantic Patch API

AI Config targeting uses semantic patch instructions:

PATCH /api/v2/projects/{projectKey}/ai-configs/{configKey}/targeting
Content-Type: application/json; domain-model=launchdarkly.semanticpatch

Key Concepts

variationId: UUIDs, not keys. Always fetch targeting first to get IDs.

Weights: Thousandths (50000 = 50%, 100000 = 100%)

Clause logic: Multiple clauses = AND, multiple values = OR

Null attributes: Rules with null/missing attributes are skipped

Workflow

Step 1: Get Targeting (with Variation IDs)

curl -X GET "https://app.launchdarkly.com/api/v2/projects/{projectKey}/ai-configs/{configKey}/targeting" \
  -H "Authorization: {api_token}" \
  -H "LD-API-Version: beta"

Response includes variations array with _id (UUID) for each variation.

Step 2: Edit the Default Rule

Edit the default rule to serve the variation you created.

Important: The turnTargetingOn instruction does not work for AI Configs. Use updateFallthroughVariationOrRollout instead.

# First, get variation IDs from Step 1 response
# Then set fallthrough to the enabled variation (e.g., "Default" variation)
curl -X PATCH "https://app.launchdarkly.com/api/v2/projects/{projectKey}/ai-configs/{configKey}/targeting" \
  -H "Authorization: {api_token}" \
  -H "Content-Type: application/json; domain-model=launchdarkly.semanticpatch" \
  -H "LD-API-Version: beta" \
  -d '{
    "environmentKey": "production",
    "instructions": [{
      "kind": "updateFallthroughVariationOrRollout",
      "variationId": "your-enabled-variation-uuid"
    }]
  }'

Step 3: Add Targeting Rules

Attribute-based rule:

curl -X PATCH "https://app.launchdarkly.com/api/v2/projects/{projectKey}/ai-configs/{configKey}/targeting" \
  -H "Authorization: {api_token}" \
  -H "Content-Type: application/json; domain-model=launchdarkly.semanticpatch" \
  -H "LD-API-Version: beta" \
  -d '{
    "environmentKey": "production",
    "instructions": [{
      "kind": "addRule",
      "clauses": [{
        "contextKind": "user",
        "attribute": "selectedModel",
        "op": "contains",
        "values": ["sonnet"],
        "negate": false
      }],
      "variation": 0
    }]
  }'

Percentage rollout:

curl -X PATCH "..." \
  -d '{
    "environmentKey": "production",
    "instructions": [{
      "kind": "addRule",
      "clauses": [{
        "contextKind": "user",
        "attribute": "tier",
        "op": "in",
        "values": ["premium"],
        "negate": false
      }],
      "percentageRolloutConfig": {
        "contextKind": "user",
        "bucketBy": "key",
        "variations": [
          {"variation": 0, "weight": 60000},
          {"variation": 1, "weight": 40000}
        ]
      }
    }]
  }'

Set fallthrough (default rule):

curl -X PATCH "..." \
  -d '{
    "environmentKey": "production",
    "instructions": [{
      "kind": "updateFallthroughVariationOrRollout",
      "variationId": "fallback-variation-uuid"
    }]
  }'

Python Implementation

import requests
import os
from typing import Dict, List, Optional

class AIConfigTargeting:
    """Manager for AI Config targeting rules"""

    def __init__(self, api_token: str, project_key: str):
        self.api_token = api_token
        self.project_key = project_key
        self.base_url = "https://app.launchdarkly.com/api/v2"

    def get_targeting(self, config_key: str) -> Optional[Dict]:
        """Get current targeting with variation IDs."""
        url = f"{self.base_url}/projects/{self.project_key}/ai-configs/{config_key}/targeting"

        response = requests.get(url, headers={
            "Authorization": self.api_token,
            "LD-API-Version": "beta"
        })

        if response.status_code == 200:
            return response.json()
        print(f"[ERROR] {response.status_code}: {response.text}")
        return None

    def get_variation_id(self, config_key: str, variation_key: str) -> Optional[str]:
        """Look up variation UUID from key or name."""
        targeting = self.get_targeting(config_key)
        if targeting:
            for var in targeting.get("variations", []):
                if var.get("key") == variation_key or var.get("name") == variation_key:
                    return var.get("_id")
        return None

    def update_targeting(self, config_key: str, environment: str,
                         instructions: List[Dict], comment: str = "") -> Optional[Dict]:
        """Send semantic patch instructions."""
        url = f"{self.base_url}/projects/{self.project_key}/ai-configs/{config_key}/targeting"

        payload = {"environmentKey": environment, "instructions": instructions}
        if comment:
            payload["comment"] = comment

        response = requests.patch(url, headers={
            "Authorization": self.api_token,
            "Content-Type": "application/json; domain-model=launchdarkly.semanticpatch",
            "LD-API-Version": "beta"
        }, json=payload)

        if response.status_code == 200:
            return response.json()
        print(f"[ERROR] {response.status_code}: {response.text}")
        return None

    def enable_config(self, config_key: str, environment: str,
                      variation_key: str = "default") -> bool:
        """
        Enable an AI Config by setting fallthrough to an enabled variation.

        Note: turnTargetingOn doesn't work for AI Configs. Instead, set the
        fallthrough from the disabled variation (index 0) to an enabled one.
        """
        variation_id = self.get_variation_id(config_key, variation_key)
        if not variation_id:
            print(f"[ERROR] Variation '{variation_key}' not found")
            return False
        return self.set_fallthrough(config_key, environment, variation_id)

    def add_rule(self, config_key: str, environment: str,
                 clauses: List[Dict], variation: int,
                 description: str = "") -> bool:
        """Add targeting rule serving a specific variation index."""
        instruction = {
            "kind": "addRule",
            "clauses": clauses,
            "variation": variation
        }
        if description:
            instruction["description"] = description

        result = self.update_targeting(config_key, environment,
            [instruction], f"Add rule: {description}")
        if result:
            print(f"[OK] Rule added")
            return True
        return False

    def add_rollout_rule(self, config_key: str, environment: str,
                         clauses: List[Dict],
                         weights: List[Dict],
                         bucket_by: str = "key") -> bool:
        """
        Add percentage rollout rule.

        weights: [{"variation": 0, "weight": 50000}, {"variation": 1, "weight": 50000}]
        """
        result = self.update_targeting(config_key, environment, [{
            "kind": "addRule",
            "clauses": clauses,
            "percentageRolloutConfig": {
                "contextKind": "user",
                "bucketBy": bucket_by,
                "variations": weights
            }
        }], "Add percentage rollout")
        if result:
            print(f"[OK] Rollout rule added")
            return True
        return False

    def set_fallthrough(self, config_key: str, environment: str,
                        variation_id: str) -> bool:
        """Set default (fallthrough) variation by UUID."""
        result = self.update_targeting(config_key, environment, [{
            "kind": "updateFallthroughVariationOrRollout",
            "variationId": variation_id
        }], "Set fallthrough")
        if result:
            print(f"[OK] Fallthrough set")
            return True
        return False

    def target_individuals(self, config_key: str, environment: str,
                          context_keys: List[str], variation: int,
                          context_kind: str = "user") -> bool:
        """Target specific context keys."""
        result = self.update_targeting(config_key, environment, [{
            "kind": "addTargets",
            "variation": variation,
            "contextKind": context_kind,
            "values": context_keys
        }], f"Target {len(context_keys)} individuals")
        if result:
            print(f"[OK] Individual targets added")
            return True
        return False

    def target_segment(self, config_key: str, environment: str,
                      segment_keys: List[str], variation: int) -> bool:
        """Target a segment."""
        result = self.update_targeting(config_key, environment, [{
            "kind": "addRule",
            "clauses": [{
                "attribute": "segmentMatch",
                "contextKind": "",  # Leave blank for segments
                "op": "segmentMatch",
                "values": segment_keys,
                "negate": False
            }],
            "variation": variation
        }], f"Target segments: {segment_keys}")
        if result:
            print(f"[OK] Segment targeting added")
            return True
        return False

    def clear_rules(self, config_key: str, environment: str) -> bool:
        """Remove all targeting rules."""
        result = self.update_targeting(config_key, environment,
            [{"kind": "replaceRules", "rules": []}], "Clear all rules")
        if result:
            print(f"[OK] All rules cleared")
            return True
        return False

Instruction Reference

Note: turnTargetingOn and turnTargetingOff do not work for AI Configs. AI Configs have targeting enabled by default. To "enable" a config, set the fallthrough to an enabled variation using updateFallthroughVariationOrRollout.

Rules

Kind
Description

addRule
Add rule with clauses and variation/rollout

removeRule
Remove by ruleId

replaceRules
Replace all rules

reorderRules
Change evaluation order

updateRuleVariationOrRollout
Update what a rule serves

Fallthrough

Kind
Description

updateFallthroughVariationOrRollout
Set default variation or rollout

Individual Targets

Kind
Description

addTargets
Target specific context keys

removeTargets
Remove specific targets

replaceTargets
Replace all targets

Operators Reference

Operator
Description
Example

in
Value in list
["premium", "enterprise"]

contains
String contains
["sonnet"]

startsWith
String prefix
["user-"]

endsWith
String suffix
[".edu"]

matches
Regex match
["^user-\\d+$"]

greaterThan / lessThan
Numeric comparison
[100]

before / after
Date comparison
["2024-12-31T00:00:00Z"]

semVerEqual / semVerGreaterThan
Version comparison
["2.0.0"]

segmentMatch
Segment membership
["beta-testers"]

Clause Structure

{
  "contextKind": "user",
  "attribute": "email",
  "op": "endsWith",
  "values": [".edu"],
  "negate": false
}

Multiple clauses = AND (all must match)

Multiple values = OR (any can match)

negate: true inverts the operator

Rollout Types

Manual Percentage Rollout

{
  "percentageRolloutConfig": {
    "contextKind": "user",
    "bucketBy": "key",
    "variations": [
      {"variation": 0, "weight": 50000},
      {"variation": 1, "weight": 50000}
    ]
  }
}

Progressive Rollout

{
  "progressiveRolloutConfig": {
    "contextKind": "user",
    "controlVariation": 1,
    "endVariation": 0,
    "steps": [
      {"rolloutWeight": 1000, "duration": {"quantity": 4, "unit": "hour"}},
      {"rolloutWeight": 5000, "duration": {"quantity": 4, "unit": "hour"}},
      {"rolloutWeight": 10000, "duration": {"quantity": 4, "unit": "hour"}}
    ]
  }
}

Guarded Rollout

{
  "guardedRolloutConfig": {
    "randomizationUnit": "user",
    "stages": [
      {"rolloutWeight": 1000, "monitoringWindowMilliseconds": 17280000},
      {"rolloutWeight": 5000, "monitoringWindowMilliseconds": 17280000}
    ],
    "metrics": [{
      "metricKey": "error-rate",
      "onRegression": {"rollback": true},
      "regressionThreshold": 0.01
    }]
  }
}

Common Patterns

Model Routing by Attribute

# Route based on selectedModel context attribute
targeting.add_rule(
    config_key="model-selector",
    environment="production",
    clauses=[{
        "contextKind": "user",
        "attribute": "selectedModel",
        "op": "contains",
        "values": ["sonnet"],
        "negate": False
    }],
    variation=0,  # Sonnet variation index
    description="Route sonnet requests"
)

Tier-Based Variation

targeting.add_rule(
    config_key="chat-assistant",
    environment="production",
    clauses=[{
        "contextKind": "user",
        "attribute": "tier",
        "op": "in",
        "values": ["premium", "enterprise"],
        "negate": False
    }],
    variation=0  # Premium model variation
)

Segment Targeting

targeting.target_segment(
    config_key="chat-assistant",
    environment="production",
    segment_keys=["beta-testers"],
    variation=1  # Experimental variation
)

Error Handling

Status
Cause
Solution

400
Invalid semantic patch
Check instruction format, ops must be lowercase

403
Insufficient permissions
Check API token

404
Config not found
Verify projectKey and configKey

422
Invalid variation
Use index (0, 1, 2...) or UUID from targeting response

Next Steps

After configuring targeting:

Provide config URL:
https://app.launchdarkly.com/projects/{projectKey}/ai-configs/{configKey}

Monitor performance with aiconfig-ai-metrics

Attach judges with aiconfig-online-evals

Set up guarded rollouts for automatic regression detection

Related Skills

aiconfig-create - Create AI Configs with variations

aiconfig-variations - Manage variations

aiconfig-online-evals - Attach judges

aiconfig-segments - Create segments for targeting

References

Target with AI Configs

Targeting Rules

JSON Targeting

Guarded Rollouts