Blog/Building Reliable RAG Pipelines with Observability
RAG
Observability
Engineering

Building Reliable RAG Pipelines with Observability

Aayush Gid
Aayush Gid
AI Architect
Jan 10, 2026
8 min read

The Reality of RAG

Retrieval-Augmented Generation (RAG) has become the standard for building knowledgeable AI agents. However, a RAG system is only as good as its retrieval step. If the model fetches the wrong context, it will generate the wrong answer—a phenomenon known as "Garbage In, Garbage Out."

Why Observability Matters

You can't fix what you can't measure. In a production RAG pipeline, you need visibility into three key stages:

  1. Retrieval: Are we finding the most relevant documents?
  2. Ranking: Are the best documents being prioritized?
  3. Generation: Is the model effectively using the context provided?

Key Metrics to Track

  • Context Precision: The proportion of retrieved chunks that are actually relevant to the query.
  • Context Recall: Is the system retrieving all the necessary information?
  • Answer Faithfulness: Does the generated answer rely solely on the provided context, or is the model hallucinating from its pre-training data?

Improving Reliability

By instrumenting your pipeline with tools like Guardrailz, you can trace individual requests and pinpoint exactly where the breakdown occurs. This allows for data-driven iteration, moving from "it feels better" to "score improved by 15%."