Retrieval evaluation
Definition
Retrieval evaluation measures whether AI systems retrieve the right sources, passages, and citations for a target set of prompts. Using a set of prompts with known good answers, it scores how well retrieval surfaces the relevant content — and how much irrelevant or wrong content it pulls in — isolating retrieval quality from the language model's generation of the final answer.
How it works
Retrieval evaluation works against a labeled prompt set — questions paired with the sources or passages that should be retrieved to answer them well. For each prompt, the system's retrieved candidates are compared against those known-relevant sources, and the result is scored.
Common evaluation lenses include:
- Whether the relevant sources are retrieved at all, and how many of them.
- How highly the relevant sources rank among the retrieved candidates.
- How much irrelevant content is pulled in alongside the relevant sources.
These map to familiar information-retrieval ideas like recall (did we find the right sources?), precision (how much of what we found was relevant?), and rank-sensitive measures that reward putting the best sources first. Crucially, retrieval evaluation scores the retrieval step on its own, separate from how the model writes the final answer — so a weak answer can be traced to bad retrieval rather than bad generation.
Why it matters
In AI search and RAG systems, the answer can only be as good as the sources retrieved to ground it. If retrieval surfaces the wrong passages, the model either answers from weak evidence or fills the gap with fabrication. Retrieval evaluation isolates this failure mode, telling you whether errors originate in retrieval or in generation — two problems with very different fixes.
For teams building or relying on RAG, this makes evaluation the diagnostic that turns "the answers are wrong sometimes" into an actionable signal. For brand visibility, the same logic applies to how external engines retrieve sources: if the right page exists and is reachable but still isn't retrieved for a target prompt, evaluation pinpoints a relevance or ranking problem to solve, rather than a content or access one.
Frequently asked questions
How is retrieval evaluation different from retrieval coverage?
Coverage asks whether your important content is reachable and present at all. Retrieval evaluation asks whether, for specific target prompts, the system actually retrieves the right sources and ranks them well. Coverage is about access and presence; evaluation is about retrieval quality.
What metrics are used in retrieval evaluation?
Evaluations commonly draw on recall (were the relevant sources retrieved?), precision (how much of what was retrieved was relevant?), and rank-sensitive measures that reward placing the best sources highest. The exact set depends on whether the priority is finding every relevant source or surfacing the best one first.
Why separate retrieval quality from answer generation?
Because a bad answer can come from either step. If retrieval surfaces the wrong sources, no amount of model tuning fixes it; if retrieval is good but the answer is still wrong, the problem is generation. Evaluating retrieval on its own tells you which one to fix.
Do I need a labeled prompt set to evaluate retrieval?
In general yes — you need target prompts paired with the sources or passages that should be retrieved, so retrieved results can be scored against a known-good reference. That labeled set is what makes evaluation repeatable rather than subjective.
Retrieval coverage
Retrieval coverage measures how much of your important content is accessible to, and likely to be retrieved by, AI search and RAG systems. It captures whether your key pages can be crawled, are present in the indexes engines draw on, and surface for the prompts that matter — exposing the gap between the content you've published and the content AI can actually reach and use.
Retrieval-augmented generation (RAG)
Retrieval-augmented generation (RAG) is an AI architecture that gives a large language model real-time access to external documents at query time — retrieving relevant passages from a vector database or search index and inserting them into the model's context before it generates a response. RAG is the foundation of modern AI search and the most effective technique for reducing hallucination.
Reranking
Reranking is a second-stage retrieval step that reorders an initial set of candidate documents by deeper relevance to the query. After a fast first-stage retriever returns many candidates, a more powerful (often cross-encoder) model scores each query-document pair, surfacing the best passages to feed a language model for grounded, accurate answers.
Hybrid search
Hybrid search combines keyword (lexical) retrieval and vector (semantic) retrieval so an AI system matches both exact terms and underlying meaning. By blending methods like BM25 with embedding similarity, it improves recall and precision over either approach alone, producing better candidate passages for grounding and citation in AI answers.
Adaptive retrieval
Adaptive retrieval is a technique where an AI system dynamically decides whether to retrieve external information and how much, based on the query. Simple questions answered from a model's parametric knowledge trigger little or no search, while hard, knowledge-intensive queries trigger more retrieval steps — balancing accuracy, latency, and cost.
AI grounding
AI grounding is the practice of anchoring an LLM's response in retrieved, citable sources at inference time — instead of letting the model rely solely on its training memory. Grounding is what separates a hallucination-prone chatbot from a search-grade AI assistant like Perplexity, Google AI Overviews, Bing Chat, or retrieval-augmented ChatGPT.