Search and retrieval can distinguish AI products when their answers depend on external, changing or company-specific information. A model cannot use evidence its system never found, and a complex question may require several searches across different sources. But the evidence supports this as a meaningful advantage in search- and retrieval-heavy products—not as a proven ranking of the most important feature across all AI products.
Why is search important for AI products?
For an AI product that answers questions about documents, business systems or other external information, answer quality depends partly on what the system can retrieve and pass to the model. A fluent answer can still be incomplete or wrong if relevant evidence was missed. Retrieval is therefore part of the product experience, not just a behind-the-scenes implementation detail.
This matters especially when users ask questions that involve several facts, sources or steps. The system may need to find an initial document, follow an identifier or reference in it, and search another source for the missing detail. If it stops after the first search, the model is left to reason from partial context.
One example of the difficulty comes from Choubey and co-authors’ EMNLP 2025 Industry Track benchmark. It models 39,190 synthetic enterprise artifacts, including documents, meeting transcripts, Slack messages, GitHub content and URLs, and tests source-aware, multi-hop questions. The authors report an average performance score of 32.96 on that benchmark and describe retrieval failures that leave systems reasoning over partial context. That score characterizes this benchmark; it is not a general measure of AI product quality.
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What is retrieval-augmented generation?
Retrieval-augmented generation (RAG) is an approach in which a system searches a body of information for relevant material and supplies that material to a language model as context for an answer. The search step is retrieval; producing an answer from the question and retrieved context is generation. In practice, the usefulness of the result depends on both stages: a model cannot cite or reason from evidence it did not receive, and retrieved passages do not automatically guarantee a correct answer.
NIST’s TREC 2025 RAG track treats passage retrieval, augmented generation, full retrieval-augmented generation and relevance-judgment generation as separate tasks. That separation is useful for product teams because it helps identify whether a failure began with missing or irrelevant evidence, or with the model’s handling of evidence it did receive.
How does retrieval affect AI answer quality?
Missing evidence limits the answer
If a system retrieves only part of the evidence needed for a question, generation operates on an incomplete picture. The EMNLP 2025 benchmark is designed around this kind of source-aware, multi-hop challenge. Its results are a warning about the benchmark’s task, not a universal estimate of how often commercial systems fail.
Follow-up searches can resolve references
Google Research describes an agentic RAG framework that decomposes a complex question, routes searches across data sources and continues searching when the available context appears incomplete. Its example is a project document that contains a server ID: answering a question about the server may require another search for its specifications. This illustrates why a single search can be inadequate when the answer depends on a chain of related facts.
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Tool use results depend on the benchmark
The AgenticRAG paper’s Microsoft Research page reports results on three distinct benchmarks: 49.6% recall@1 on BRIGHT, 0.96 factuality on WixQA and 92% answer correctness on FinanceBench. The authors also report that moving from single-shot retrieval to agentic tool use was the most significant factor in their ablation. These results describe the authors’ experiments on those benchmarks. They are not a direct comparison of commercial products, and the three scores measure different tasks.
How can AI find information across multiple company data sources?
A system designed for multi-source questions needs to do more than issue one query and pass back the first useful-looking result. A practical approach is to identify what the question requires, search the relevant sources, and check whether the evidence found is sufficient to answer. When one source introduces an identifier, entity or unresolved detail, the system may need to search again using that clue.
Google Research’s description of agentic RAG provides an example of this pattern: decompose the question, route searches across sources and persist with additional searches when context is incomplete. That is a proposed system design, not proof that every agentic system will retrieve comprehensively or answer accurately. Product teams should test whether the system follows cross-source references reliably in their own data and tasks.
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How do I evaluate enterprise AI search?
Evaluate retrieval and answer generation separately before judging the end-to-end experience. Build a representative set of real questions, including questions with evidence in more than one source and questions that cannot be answered from the available corpus. Inspect both what the system retrieved and what it answered; a plausible response alone does not show that it found complete support.
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- Evidence coverage: Did retrieval return the passages or documents needed to support the answer, or did it miss a required fact?
- Multi-hop and cross-source support: Can the system follow a reference, identifier or relationship from one source into another?
- Freshness and source access: Does it search the current, authorized information relevant to the task?
- Grounding and answer quality: Can substantive claims be traced to retrieved sources? Does the system keep searching or abstain when evidence is insufficient?
- Evaluation design: Are retrieval and generation scored separately on representative answerable and unanswerable questions?
- Operational trade-offs: Measure latency, cost and complexity alongside quality in the intended use case.
These are evaluation dimensions, not results from a common vendor test. The sources summarized here do not provide cross-vendor measurements for latency, cost or operational complexity. TREC’s separation of retrieval and generation tasks offers a useful model for avoiding a single end-to-end score that hides where a system succeeds or fails.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What do the reported benchmark numbers show?
The figures below come from different studies and tasks. They should not be combined into a ranking or treated as comparable product scores.
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|---|---|---|
| Choubey et al., EMNLP 2025 Industry Track | 32.96 average performance score | A benchmark of 39,190 synthetic enterprise artifacts and source-aware, multi-hop questions. |
| Google Research, 2026 | Up to 34% higher accuracy | Google’s report for its agentic RAG framework on factuality datasets, compared with standard RAG. |
| AgenticRAG authors, 2026 | 49.6% recall@1 | BRIGHT benchmark. |
| AgenticRAG authors, 2026 | 0.96 factuality | WixQA benchmark. |
| AgenticRAG authors, 2026 | 92% answer correctness | FinanceBench benchmark. |
The measures, datasets and tasks differ, and the results do not constitute an independent apples-to-apples comparison of commercial AI products. In particular, Google’s improvement is vendor-reported, while the AgenticRAG figures are author-reported benchmark results.
Does search and retrieval decide which AI product is best?
Not universally. The available evidence focuses on enterprise RAG and search evaluation; it does not establish that retrieval is the leading differentiator across every category of AI product. Nor does it provide a market-wide independent comparison, establish broad adoption or willingness to pay, or show that better retrieval causes commercial success.
For products whose value depends on answering questions over external or enterprise information, however, retrieval quality is a consequential capability. Buyers and product teams should compare systems on the same representative tasks, inspect the evidence behind their answers and weigh answer quality against operational needs rather than treating one benchmark result as a universal verdict.
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