Explaining Every RAG Failure
Explore how to analyze and explain failures in retrieval-augmented generation systems by applying four crucial diagnostic checks: retrieval, grounding, responsiveness, and answerability. Understand how to identify common failure patterns, diagnose issues in real traces, and connect failures to specific, actionable fixes to improve overall system quality.
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- Is the system pulling the right information in the first place?
- Does the answer stick to what was actually retrieved?
- Does the answer actually address the user’s question?
- Did the system have enough information to answer the question at all?
- Try it: Diagnose RAG failures using the four checks
- What’s next?
Most teams treat RAG evaluation as a complex scoring problem. They experiment with many metrics, compare dashboards, and build layered evaluation pipelines. In practice, failure patterns in retrieval-augmented systems are often straightforward. Most incorrect responses can be traced to a small set of mismatches between the user’s request, the retrieved context, and the model’s output.
This lesson presents a practical framework for analyzing these failures. Instead of framing RAG evaluation as a purely metric-driven exercise, it focuses on a small set of concrete checks that can be applied directly to real traces. These checks are straightforward to learn, simple to communicate, and closely aligned with the fixes that improve quality.
At the core of this approach are four questions every RAG trace must answer.
Retrieval: Did it retrieve the right information?
Grounding: Did it stay grounded in that information?
Responsiveness: Did it respond to what the user asked?
Answerability: Could it realistically answer, given the available context?
Together, these four checks form the backbone of effective RAG evaluation.
Is the system pulling the right information in the first place?
The ...