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LLM Apps in Production (RAG + Vector DB + Caching) · Lesson

Observability: Logging, Metrics, Tracing

Integrate comprehensive logging, metrics collection, and distributed tracing to gain deep insights into your LLM application's behavior.

What is Observability?

In this lesson, we'll explore observability, a crucial concept for managing complex software systems, especially LLM applications.

Observability means understanding the internal state of a system by examining the data it produces. Think of it as having X-ray vision into your application's behavior.

For LLM apps, this helps us answer critical questions like:

  • Why is a request slow?
  • Is the RAG retrieval working as expected?
  • Are we incurring unexpected costs?

Logs: Recording Events

Logs are timestamped records of events that happen within your application. They are like a diary of your system's activities.

For LLM applications, logs are essential for:

  • Tracking incoming user prompts.
  • Storing responses from the LLM.
  • Recording intermediate steps in a RAG pipeline (e.g., documents retrieved).
  • Capturing errors or warnings.

They provide detailed contextual information for debugging and post-mortem analysis.

All lessons in this course

  1. Horizontal Scaling of RAG Components
  2. Observability: Logging, Metrics, Tracing
  3. Alerting and Incident Response for LLM Ops
  4. Load Testing and Capacity Planning
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