Evals
Evals are tools and methodologies used to test, measure, and validate the performance of AI models. Since AI output can be unpredictable, these tools help developers ensure their applications are accurate, safe, and reliable before deploying them to users. Examples LLM-as-a-Judge: Using a highly intelligent model (like GPT-4) to grade the answers of a smaller, faster model. Hallucination Detection: Tools that specifically check if an AI's answer is factually incorrect or made up. Bias & Safety Testing: Automated stress-testing to ensure the model refuses to generate toxic or harmful content. Performance Benchmarking: Comparing model speed (latency) and cost across different providers.
6 tools
Langfuse provides an open-source LLM engineering platform that offers tracing, evaluations, prompt management, and metrics for debugging and improving LLM applications. It's designed for teams building complex LLM apps.
🪢 Open source LLM engineering platform: LLM Observability, metrics, evals, prompt management, playground, datasets. Integrates with OpenTelemetry, Langchain, OpenAI SDK, LiteLLM, and more. 🍊YC W23