Retrieval
Retrieval that stays useful when the question gets hard.
FirmGround RAG combines retrieval, reasoning, and verification so enterprise Q&A covers domain, metrics, and novel questions—with measurable accuracy, not vibes.
Talk about RAGEval
Agentic Hybrid RAG leads on overall accuracy—because it answers, and answers well.
On 150 questions, Agentic Hybrid RAG reached 81.33% overall accuracy with almost no refusals, while Naive RAG fell to 21.33% mainly due to mass refusals.
Hybrid
Naive
Closed
| System | Correct | Incorrect | Refused | Overall accuracy |
|---|---|---|---|---|
| Agentic Hybrid RAG | 122 | 26 | 2 | 81.33% |
| Naive RAG | 32 | 14 | 104 | 21.33% |
| Closed-book LLM | 36 | 51 | 63 | 24% |
Overall accuracy = correct ÷ total. Refusals count as misses.
Key features
01
Goal: retrieve, reason, then answer—with rare refusals.
Move beyond single-shot vector search. Hybrid paths recover when simple retrieval misses, so overall accuracy stays high across the eval set.
02
Goal: every claim can point to a source.
Answers are tied to passages and metadata so reviewers can check the trail—critical for regulated and internal knowledge work.
03
Goal: quality has a number, not a feeling.
Ship with regression sets for domain, metrics, and novel questions. Compare against naive RAG and closed-book baselines on the same bank.
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