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Llm Response Evaluator

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from $0.01 / 1,000 results

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Llm Response Evaluator

Llm Response Evaluator

Evaluate LLM outputs with comprehensive quality metrics and A/B testing capabilities. Free alternative to Confident AI ($99/mo).

Pricing

from $0.01 / 1,000 results

Rating

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Developer

Cody Churchwell

Cody Churchwell

Maintained by Community

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Bookmarked

2

Total users

1

Monthly active users

17 days ago

Last modified

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LLM Response Evaluator & A/B Tester

Evaluate LLM outputs with comprehensive quality metrics and A/B testing capabilities. Free alternative to Confident AI ($99/mo).

Features

  • Multi-Metric Evaluation: Quality, relevance, coherence, toxicity, bias, factuality, creativity, conciseness
  • A/B Testing: Compare model variants with statistical significance testing
  • Response Comparison: Side-by-side evaluation of multiple responses
  • Custom Thresholds: Set quality gates and pass/fail criteria
  • Detailed Reports: Track evaluation trends and model performance over time

Operations

evaluate

Evaluate a single LLM response across multiple quality metrics.

compare

Compare multiple responses side-by-side and rank by performance.

abTest

Run A/B tests with statistical significance testing to choose the best model.

generateReport

Generate comprehensive evaluation reports with trends and insights.

Target Use Cases

  • Quality Assurance: Ensure LLM outputs meet quality standards
  • Model Selection: Compare different models and choose the best performer
  • A/B Testing: Test prompt variations and model configurations
  • Bias Detection: Identify and mitigate bias in AI responses

Pricing

  • Free forever on Apify (pay only for platform usage)
  • Competes with: Confident AI ($99/mo), Patronus AI ($149/mo)
  • Target MAU: 900 users

Example Usage

{
"operation": "evaluate",
"response": {
"id": "resp_123",
"prompt": "Explain quantum computing",
"response": "Quantum computing uses quantum bits...",
"model": "gpt-4"
},
"evaluationMetrics": ["quality", "relevance", "toxicity", "bias"]
}

License

MIT