AI Config Online Evaluations

Attach judges to AI Config variations for automatic quality scoring using LLM-as-a-judge methodology. Judges evaluate responses and return scores between 0.0 and 1.0.

Prerequisites

• LaunchDarkly account with AI Configs enabled

• API access token with write permissions

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

• For automatic metric recording and the consolidated judge-result API: Python AI SDK v0.18.0+ or Node.js AI SDK v0.17.0+

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

What Are Judges?

Judges are specialized AI Configs in judge mode that evaluate responses from other AI Configs. They use an LLM to score outputs and return structured results:

{ "score": 0.85, "reasoning": "Answered correctly with one minor omission" }

Built-in Judges

LaunchDarkly provides three pre-configured judges:

Judge Metric Key Measures

Accuracy $ld:ai:judge:accuracy

How correct and grounded the response is

Relevance $ld:ai:judge:relevance

How well it addresses the user request

Toxicity $ld:ai:judge:toxicity

Harmful or unsafe phrasing (lower = safer)

Completion Mode Only

Judges can only be attached to completion mode AI Configs in the UI. For agent mode or custom pipelines, use programmatic evaluation via the SDK.

Restrictions

• Cannot attach judges to judges (no recursion)

• Cannot attach multiple judges with the same metric key to a single variation

• Cannot view/edit model parameters or tools on judge variations

Workflow

Step 1: Create Custom Judges (Optional)

For domain-specific evaluation, create judge AI Configs:

Create judge config

curl -X POST "https://app.launchdarkly.com/api/v2/projects/{projectKey}/ai-configs"
-H "Authorization: {api_token}"
-H "Content-Type: application/json"
-H "LD-API-Version: beta"
-d '{ "key": "security-judge", "name": "Security Judge", "mode": "judge", "evaluationMetricKey": "security", "isInverted": false }'

Note: Set isInverted: true for metrics like toxicity where 0.0 is better.

Then add a variation with the evaluation prompt:

curl -X POST "https://app.launchdarkly.com/api/v2/projects/{projectKey}/ai-configs/security-judge/variations"
-H "Authorization: {api_token}"
-H "Content-Type: application/json"
-H "LD-API-Version: beta"
-d '{ "key": "default", "name": "Default", "messages": [ { "role": "system", "content": "You are a security auditor. Score from 0.0 to 1.0:\n- 1.0: No security issues\n- 0.7-0.9: Minor issues\n- 0.4-0.6: Moderate issues\n- 0.1-0.3: Serious vulnerabilities\n- 0.0: Critical vulnerabilities\n\nCheck for: SQL injection, XSS, hardcoded secrets, command injection." } ], "modelConfigKey": "OpenAI.gpt-4o-mini", "model": { "parameters": { "temperature": 0.3 } } }'

Step 2: Attach Judges to Variations

Use the variation PATCH endpoint:

curl -X PATCH "https://app.launchdarkly.com/api/v2/projects/{projectKey}/ai-configs/{configKey}/variations/{variationKey}"
-H "Authorization: {api_token}"
-H "Content-Type: application/json"
-H "LD-API-Version: beta"
-d '{ "judgeConfiguration": { "judges": [ {"judgeConfigKey": "security-judge", "samplingRate": 1.0}, {"judgeConfigKey": "api-contract-judge", "samplingRate": 0.5} ] } }'

Important: The judges array replaces all existing judge attachments. An empty array removes all judges.

Step 3: Set Fallthrough on Judges

Each judge AI Config needs its fallthrough set to the enabled variation. AI Configs default to the "disabled" variation (index 0).

Note: turnTargetingOn does not work for AI Configs. Use updateFallthroughVariationOrRollout instead.

First get the variation ID for "Default" from GET targeting response

curl -X PATCH "https://app.launchdarkly.com/api/v2/projects/{projectKey}/ai-configs/security-judge/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-default-variation-uuid" }] }'

Python Implementation

import requests import os from typing import Optional

class AIConfigJudges: """Manager for AI Config judge attachments"""

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"
    self.headers = {
        "Authorization": api_token,
        "Content-Type": "application/json",
        "LD-API-Version": "beta"
    }

def attach_judges(self, config_key: str, variation_key: str,
                  judges: list[dict]) -> dict:
    """
    Attach judges to a variation.

    Args:
        config_key: AI Config key
        variation_key: Variation key
        judges: List of {"judgeConfigKey": str, "samplingRate": float}
    """
    url = f"{self.base_url}/projects/{self.project_key}/ai-configs/{config_key}/variations/{variation_key}"

    response = requests.patch(url, headers=self.headers, json={
        "judgeConfiguration": {"judges": judges}
    })

    if response.status_code == 200:
        print(f"[OK] Attached {len(judges)} judges to {config_key}/{variation_key}")
        return response.json()
    print(f"[ERROR] {response.status_code}: {response.text}")
    return {}

def create_judge(self, key: str, name: str, metric_key: str,
                 system_prompt: str, model: str = "OpenAI.gpt-4o-mini",
                 is_inverted: bool = False) -> dict:
    """
    Create a judge AI Config.

    Args:
        key: Judge config key
        name: Display name
        metric_key: Metric key for scoring (appears as $ld:ai:judge:{metric_key})
        system_prompt: Evaluation instructions
        is_inverted: True if lower scores are better (e.g., toxicity)
    """
    # Create config
    config_url = f"{self.base_url}/projects/{self.project_key}/ai-configs"
    response = requests.post(config_url, headers=self.headers, json={
        "key": key,
        "name": name,
        "mode": "judge",
        "evaluationMetricKey": metric_key,
        "isInverted": is_inverted
    })

    if response.status_code not in [200, 201]:
        print(f"[ERROR] Creating config: {response.text}")
        return {}

    # Create variation
    var_url = f"{self.base_url}/projects/{self.project_key}/ai-configs/{key}/variations"
    response = requests.post(var_url, headers=self.headers, json={
        "key": "default",
        "name": "Default",
        "messages": [{"role": "system", "content": system_prompt}],
        "modelConfigKey": model,
        "model": {"parameters": {"temperature": 0.3}}
    })

    if response.status_code in [200, 201]:
        print(f"[OK] Created judge: {key}")
        return response.json()
    print(f"[ERROR] Creating variation: {response.text}")
    return {}

def set_fallthrough(self, config_key: str, environment: str,
                    variation_key: str = "default") -> bool:
    """
    Set fallthrough to enable a judge config.

    Note: turnTargetingOn doesn't work for AI Configs. Instead, set the
    fallthrough from disabled (index 0) to the enabled variation.
    """
    # Get variation ID
    url = f"{self.base_url}/projects/{self.project_key}/ai-configs/{config_key}/targeting"
    response = requests.get(url, headers=self.headers)

    if response.status_code != 200:
        print(f"[ERROR] {response.status_code}: {response.text}")
        return False

    targeting = response.json()
    variation_id = None
    for var in targeting.get("variations", []):
        if var.get("key") == variation_key or var.get("name") == variation_key:
            variation_id = var.get("_id")
            break

    if not variation_id:
        print(f"[ERROR] Variation '{variation_key}' not found")
        return False

    # Set fallthrough
    response = requests.patch(url, headers={
        **self.headers,
        "Content-Type": "application/json; domain-model=launchdarkly.semanticpatch"
    }, json={
        "environmentKey": environment,
        "instructions": [{
            "kind": "updateFallthroughVariationOrRollout",
            "variationId": variation_id
        }]
    })

    if response.status_code == 200:
        print(f"[OK] Fallthrough set for {config_key}")
        return True
    print(f"[ERROR] {response.status_code}: {response.text}")
    return False

SDK: Automatic Evaluation

When using create_chat()

• invoke() , attached judges evaluate automatically:

import os import json import asyncio import ldclient from ldclient import Context from ldclient.config import Config from ldai import LDAIClient, AICompletionConfigDefault

sdk_key = os.getenv('LAUNCHDARKLY_SDK_KEY') ai_config_key = os.getenv('LAUNCHDARKLY_AI_CONFIG_KEY', 'sample-ai-config')

async def async_main(): ldclient.set_config(Config(sdk_key)) aiclient = LDAIClient(ldclient.get())

context = (
    Context.builder('example-user-key')
    .kind('user')
    .name('Sandy')
    .build()
)

default_value = AICompletionConfigDefault(enabled=False)

# create_chat() initializes with judges from AI Config
chat = await aiclient.create_chat(ai_config_key, context, default_value, {})

if not chat:
    print(f"AI chat configuration not enabled for: {ai_config_key}")
    return

user_input = 'How can LaunchDarkly help me?'

# invoke() automatically evaluates with attached judges
chat_response = await chat.invoke(user_input)
print("Response:", chat_response.message.content)

# Await evaluation results
if chat_response.evaluations and len(chat_response.evaluations) > 0:
    eval_results = await asyncio.gather(*chat_response.evaluations)
    results_to_display = [
        result.to_dict() if result is not None else "not evaluated"
        for result in eval_results
    ]
    print("Judge results:")
    print(json.dumps(results_to_display, indent=2, default=str))

# Always flush events before closing — trailing events are at risk of being
# lost otherwise, in short-lived scripts and long-running services alike.
ldclient.get().flush()
ldclient.get().close()

SDK: Direct Judge Evaluation

For agent mode or custom pipelines, evaluate input/output pairs directly:

import os import json import asyncio import ldclient from ldclient import Context from ldclient.config import Config from ldai import LDAIClient, AICompletionConfigDefault

sdk_key = os.getenv('LAUNCHDARKLY_SDK_KEY') judge_key = os.getenv('LAUNCHDARKLY_AI_JUDGE_KEY', 'sample-ai-judge-accuracy')

async def async_main(): ldclient.set_config(Config(sdk_key)) aiclient = LDAIClient(ldclient.get())

context = (
    Context.builder('example-user-key')
    .kind('user')
    .name('Sandy')
    .build()
)

judge_default_value = AICompletionConfigDefault(enabled=False)

# Get judge configuration from LaunchDarkly
judge = await aiclient.create_judge(judge_key, context, judge_default_value)

if not judge:
    print(f"AI judge configuration not enabled for key: {judge_key}")
    return

input_text = 'You are a helpful assistant. How can you help me?'
output_text = 'I can answer any question you have.'

# Evaluate the input/output pair — always returns a JudgeResult in v0.18.0+
judge_result = await judge.evaluate(input_text, output_text)

if not judge_result.sampled:
    print("Judge evaluation was skipped (sample rate or configuration issue)")
    return

# Track the consolidated result on the AI Config tracker if needed:
# tracker = ai_config.create_tracker()
# tracker.track_judge_result(judge_result)

print("Judge Result:")
print(json.dumps(judge_result.to_dict(), default=str))

# Always flush events before closing — trailing events are at risk of being
# lost otherwise, in short-lived scripts and long-running services alike.
ldclient.get().flush()
ldclient.get().close()

Note: Direct evaluation does not automatically record metrics. Obtain a tracker via ai_config.create_tracker() / aiConfig.createTracker!() and call tracker.track_judge_result(result) / tracker.trackJudgeResult(result) to record scores for the AI Config you're evaluating. (This consolidates the earlier track_eval_scores

• track_judge_response pair that was removed in Python v0.18.0 / Node v0.17.0.)

Sampling Rates

Each evaluated response sends an additional request to your model provider, increasing token usage and costs. Start with a lower sampling percentage and increase only if you need more evaluation coverage.

You can adjust sampling rates at any time from the Judges section of a variation, or disable a judge by setting its sampling to 0%.

Viewing Results

• Navigate to AI Configs > select your config

• Click Monitoring tab

• Select Evaluator metrics from dropdown

• View scores by variation and time range

Results appear within 1-2 minutes of evaluation.

Use in Guardrails and Experiments

Evaluation metrics integrate with:

• Guarded rollouts: Pause/revert when scores fall below threshold

• Experiments: Compare variations using evaluation metrics as goals

Error Handling

Status Cause Solution

404 Config/variation not found Verify keys exist

400 Invalid judge config Check judgeConfigKey exists

403 Insufficient permissions Check API token permissions

422 Duplicate metric key Cannot attach multiple judges with same metric key

Next Steps

After attaching judges:

• Set fallthrough on judge configs to an enabled variation (required)

• Monitor results in Monitoring tab

• Adjust sampling based on cost/coverage needs

• Set up guarded rollouts for automatic regression detection

Related Skills

• aiconfig-create

• Create AI Configs and judges

• aiconfig-targeting

• Configure targeting rules

• aiconfig-variations

• Manage variations

References

• Online Evaluations

• Custom Judges

Python SDK examples:

• direct_judge_example.py - Evaluate input/output pairs directly

• chat_judge_example.py - Automatic evaluation with create_chat/invoke

Node.js SDK examples:

• judge-evaluation - Both direct evaluation and automatic chat-based evaluation