aiconfig-online-evals

name	aiconfig-online-evals
description	Use Online Evaluations (LLM-as-a-judge) to automatically score AI Config responses for accuracy, relevance, and toxicity.
compatibility	Works with AI Configs in completion mode only. Requires judges configured via LaunchDarkly UI.
metadata	{"author":"launchdarkly","version":"0.5.0"}

AI Config Online Evaluations

Automatically score AI Config responses using LLM-as-a-judge methodology.

Prerequisites

• LaunchDarkly SDK initialized (see aiconfig-sdk)
• AI Config in completion mode (judges don't work with agent mode)
• Judges enabled in LaunchDarkly UI (AI Configs → your config → Variations → Attach judges)

Core Concepts

Three built-in judges are available:

• Accuracy - Scores 0.0-1.0 for correctness
• Relevance - Scores 0.0-1.0 for addressing the request
• Toxicity - Scores 0.0-1.0 where lower is safer

Judges evaluate asynchronously (1-2 minute delay). Results appear in the Monitoring tab.

SDK: Check Judge Configuration

from ldclient import Context
from ldclient.config import Config
from ldai.client import LDAIClient, AICompletionConfigDefault
import ldclient

# Initialize (see aiconfig-sdk)
ldclient.set_config(Config("your-sdk-key"))
ld_client = ldclient.get()
ai_client = LDAIClient(ld_client)

def check_judges(ai_client, config_key: str, user_id: str):
    """Check which judges are attached to a config."""
    context = Context.builder(user_id).build()
    config = ai_client.completion_config(
        config_key,
        context,
        AICompletionConfigDefault(enabled=False),
        {}
    )

    if config.judge_configuration and config.judge_configuration.judges:
        print("[OK] Judges attached:")
        for judge in config.judge_configuration.judges:
            print(f"     - {judge.key}: {int(judge.sampling_rate * 100)}% sampling")
    else:
        print("[INFO] No judges configured")

    return config.judge_configuration

SDK: Automatic Evaluation with create_chat

For automatic judge evaluation, use the create_chat() method. This handles the full conversation flow and triggers judges automatically.

Important: create_chat() passes model parameters directly to the provider. LaunchDarkly uses camelCase (maxTokens), but OpenAI expects snake_case (max_tokens). If your variation has maxTokens set, create_chat() will fail with OpenAI. Either:

• Omit maxTokens from the variation's model parameters, OR
• Use completion_config() + track_openai_metrics() instead (but judges won't auto-evaluate)

from ldai.client import AICompletionConfigDefault, ModelConfig, ProviderConfig, LDMessage

async def generate_with_automatic_evaluation(ai_client, config_key: str, user_id: str, prompt: str):
    """Generate AI response with automatic judge evaluation using create_chat."""
    context = Context.builder(user_id).build()

    chat = await ai_client.create_chat(
        config_key,
        context,
        AICompletionConfigDefault(
            enabled=True,
            model=ModelConfig("gpt-4"),
            provider=ProviderConfig("openai"),
            messages=[LDMessage(role='system', content='You are a helpful assistant.')]
        )
    )

    if not chat:
        return None

    # Invoke chat - judges evaluate automatically (1-2 min delay)
    response = await chat.invoke(prompt)

    # Results appear in Monitoring tab as:
    # $ld:ai:judge:accuracy, $ld:ai:judge:relevance, $ld:ai:judge:toxicity
    return response.message.content

Sampling Rate Guidelines

Configure sampling rates in the LaunchDarkly UI:

Environment	Rate	Use Case
Development	100%	Full evaluation for testing
Staging	50%	Validation coverage
Production (initial)	10%	Start conservatively
Production (stable)	20%	Ongoing monitoring
Critical features	30%	Important flows

Viewing Results

1. Go to AI Configs in LaunchDarkly
2. Select your config
3. Click Monitoring tab
4. View judge scores by variation and time range

Best Practices

1. Completion Mode Only - Judges don't work with agent mode configs
2. Async Results - Evaluation takes 1-2 minutes; don't wait for results
3. Monitor Costs - Judge evaluations use LLM tokens
4. Start Low - Begin with 10% sampling, increase as needed
5. Flush Events - Call ld_client.flush() in serverless environments

Related Skills

• aiconfig-sdk - SDK setup and config retrieval
• aiconfig-ai-metrics - Automatic AI metrics tracking
• aiconfig-variations - Manage variations

References

• Online Evaluations Documentation
• Node.js judge-evaluation example