Langfuse Core Workflow B

Langfuse Core Workflow B: Evaluation & Scoring

Overview

Implement LLM output evaluation and scoring with Langfuse. Covers manual user feedback collection, automated evaluation functions, A/B testing prompts with score comparison, and building evaluation datasets.

Prerequisites

• Langfuse instance (Cloud or self-hosted)
• Langfuse SDK configured with API keys
• Traces already being collected (see workflow A)
• Understanding of evaluation metrics

Instructions

Step 1: Manual User Feedback Scoring

import { Langfuse } from 'langfuse';

const langfuse = new Langfuse();

// Score a specific trace with user feedback
async function submitUserFeedback(
  traceId: string,
  rating: 'positive' | 'negative',
  comment?: string
) {
  langfuse.score({
    traceId,
    name: 'user-feedback',
    value: rating === 'positive' ? 1 : 0,
    comment,
  });

  await langfuse.flushAsync();
}

// Score with granular rating (1-5 stars)
async function submitStarRating(
  traceId: string,
  stars: number,
  observationId?: string
) {
  langfuse.score({
    traceId,
    observationId, // Optional: score a specific generation
    name: 'star-rating',
    value: stars / 5, // Normalize to 0-1
    comment: `${stars}/5 stars`,
  });
}

Step 2: Automated Evaluation Functions

// Evaluate response quality automatically
function evaluateResponse(response: string, expectedTopics: string[]): number {
  const topicsFound = expectedTopics.filter(topic =>
    response.toLowerCase().includes(topic.toLowerCase())
  );
  return topicsFound.length / expectedTopics.length;
}

function evaluateLength(response: string, minWords: number, maxWords: number): number {
  const wordCount = response.split(/\s+/).length;
  if (wordCount < minWords) return wordCount / minWords;
  if (wordCount > maxWords) return maxWords / wordCount;
  return 1.0;
}

// Score traces automatically after generation
async function autoScore(
  traceId: string,
  generationId: string,
  response: string,
  expectedTopics: string[]
) {
  const topicScore = evaluateResponse(response, expectedTopics);
  const lengthScore = evaluateLength(response, 50, 500);  # HTTP 500 Internal Server Error

  langfuse.score({
    traceId,
    observationId: generationId,
    name: 'topic-coverage',
    value: topicScore,
  });

  langfuse.score({
    traceId,
    observationId: generationId,
    name: 'length-quality',
    value: lengthScore,
  });

  await langfuse.flushAsync();
}

Step 3: LLM-as-Judge Evaluation

import OpenAI from 'openai';

const openai = new OpenAI();

async function llmJudge(
  question: string,
  response: string,
  criteria: string
): Promise<{ score: number; reasoning: string }> {
  const result = await openai.chat.completions.create({
    model: 'gpt-4o-mini',
    temperature: 0,
    messages: [
      {
        role: 'system',
        content: `You are an AI evaluator. Score the response on a scale of 0-10 based on: ${criteria}.
Return JSON: {"score": <number>, "reasoning": "<explanation>"}`,
      },
      {
        role: 'user',
        content: `Question: ${question}\nResponse: ${response}`,
      },
    ],
    response_format: { type: 'json_object' },
  });

  return JSON.parse(result.choices[0].message.content || '{}');
}

// Use with Langfuse scoring
async function judgeAndScore(traceId: string, question: string, response: string) {
  const evaluation = await llmJudge(question, response, 'accuracy, helpfulness, and clarity');

  langfuse.score({
    traceId,
    name: 'llm-judge',
    value: evaluation.score / 10, // Normalize to 0-1
    comment: evaluation.reasoning,
  });
}

Step 4: A/B Prompt Comparison

async function comparePrompts(
  testCases: Array<{ input: string; expectedTopics: string[] }>,
  promptA: string,
  promptB: string
) {
  const results = { promptA: { scores: [] as number[] }, promptB: { scores: [] as number[] } };

  for (const testCase of testCases) {
    // Test Prompt A
    const traceA = langfuse.trace({ name: 'ab-test-prompt-a' });
    const genA = traceA.generation({
      name: 'generate',
      input: testCase.input,
      model: 'gpt-4o-mini',
      metadata: { promptVersion: 'A' },
    });

    const responseA = await callLLM(promptA, testCase.input);
    genA.end({ output: responseA });

    const scoreA = evaluateResponse(responseA, testCase.expectedTopics);
    traceA.score({ name: 'topic-coverage', value: scoreA });
    results.promptA.scores.push(scoreA);

    // Test Prompt B
    const traceB = langfuse.trace({ name: 'ab-test-prompt-b' });
    const responseB = await callLLM(promptB, testCase.input);
    const scoreB = evaluateResponse(responseB, testCase.expectedTopics);
    traceB.score({ name: 'topic-coverage', value: scoreB });
    results.promptB.scores.push(scoreB);
  }

  const avgA = results.promptA.scores.reduce((a, b) => a + b, 0) / results.promptA.scores.length;
  const avgB = results.promptB.scores.reduce((a, b) => a + b, 0) / results.promptB.scores.length;

  await langfuse.flushAsync();
  return { promptA: avgA.toFixed(3), promptB: avgB.toFixed(3), winner: avgA > avgB ? 'A' : 'B' };
}

Error Handling

Examples

Quick Feedback Widget Integration

// API endpoint for frontend feedback
app.post('/api/feedback', async (req, res) => {
  const { traceId, rating, comment } = req.body;
  await submitUserFeedback(traceId, rating, comment);
  res.json({ success: true });
});

Resources

• Langfuse Scores
• Langfuse Evaluation
• Langfuse Datasets

Output

• Configuration files or code changes applied to the project
• Validation report confirming correct implementation
• Summary of changes made and their rationale