A Practical Guide to Integrate Evaluation and Observability into LLM Apps

This guide introduces Opik, an open-source framework by CometML for evaluating and monitoring LLM applications. It demonstrates how to integrate Opik with LLM workflows (including RAG pipelines) to track performance, log interactions, and evaluate outputs against predefined metrics. The article provides step-by-step instructions for setup, tracing functions, monitoring LLM calls, and evaluating RAG systems using Opik’s dashboard.

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2. Core Technical Concepts/Technologies

• Opik: Open-source framework for LLM evaluation and observability.
• Retrieval-Augmented Generation (RAG): LLM pipeline combining retrieval and generation.
• LlamaIndex: Library for building and querying vector indexes.
• Ollama: Platform for running LLMs locally.
• Observability: Real-time monitoring of system behavior (e.g., model drift, performance bottlenecks).
• Evaluation Metrics: Relevance, factuality, coherence, and hallucination scoring.

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3. Main Points

• Opik’s Features:
  • Tracks LLM performance across metrics (relevance, factuality, etc.).
  • Logs inputs/outputs, token usage, costs, and latency.
  • Supports integrations with OpenAI, Ollama