RAG triad

What is RAG triad?

RAG triad is a RAG evaluation framework that checks context relevance, groundedness, and answer relevance. It is most closely associated with TruLens, which uses the triad to help localize where a retrieval-augmented generation system is failing. (trulens.org)

Understanding RAG triad

In practice, the RAG triad breaks a RAG system into three linked quality checks. Context relevance asks whether the retrieved passages actually match the user query, groundedness asks whether the answer is supported by that retrieved context, and answer relevance asks whether the final response addresses the original question. (trulens.org)

That makes the framework useful for debugging because it separates retrieval problems from generation problems. If context relevance is weak, the retriever is likely surfacing the wrong information. If groundedness is weak, the model may be over-claiming or inventing details from the supplied context. If answer relevance is weak, the response may be supported but still not useful to the user. Key aspects of RAG triad include:

1. Context relevance: measures whether each retrieved chunk helps answer the query.
2. Groundedness: checks whether the response is supported by the retrieved evidence.
3. Answer relevance: evaluates whether the final answer actually addresses the prompt.
4. Failure localization: helps teams tell whether the issue is retrieval, synthesis, or response quality.
5. Hallucination focus: emphasizes whether unsupported claims are creeping into the answer.

Advantages of RAG triad

The RAG triad is popular because it gives teams a simple, structured way to inspect RAG behavior. Its main advantages are:

1. Clear debugging signal: it narrows quality issues to a specific stage of the RAG pipeline.
2. Easy to communicate: the three metrics are intuitive for engineers and product teams alike.
3. RAG-specific: it maps directly to the retrieval and generation steps that matter most.
4. Useful for iteration: teams can compare retriever, prompt, and model changes over time.
5. Practical for observability: it fits naturally into logging, eval, and monitoring workflows.

Challenges in RAG triad

Like most LLM evaluation methods, the RAG triad is helpful but not complete. Common challenges include:

1. Metric subjectivity: judging relevance and groundedness can depend on the evaluator model or rubric.
2. Partial coverage: the triad does not capture every product concern, like style, policy compliance, or latency.
3. Chunking sensitivity: results can vary depending on how source context is split and retrieved.
4. Multi-hop complexity: questions that need multiple evidence sources can be harder to score well.
5. Threshold tuning: teams still need to decide what score counts as acceptable.

Example of RAG triad in action

Scenario: a support assistant answers questions from a product knowledge base.

A user asks whether a feature is available on the enterprise plan. The retriever pulls in pricing docs, a release note, and an unrelated blog post. RAG triad scoring shows high answer relevance, low groundedness, and mixed context relevance, which suggests the answer sounds right but is leaning on weak evidence.

The team then tightens retrieval filters, updates the prompt to cite only approved sources, and re-runs the same eval set. If groundedness and context relevance improve together, they know the change helped the right part of the pipeline.

How PromptLayer helps with RAG triad

PromptLayer helps teams operationalize RAG triad-style evaluation by tracking prompts, responses, and test cases in one place. That makes it easier to compare retrieval and generation changes, review failures, and keep prompt iterations organized as your RAG stack evolves.

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