Retrieval + search
How AI retrieves answers from company knowledge (RAG, plain English)
RAG is fancy words for ‘find the right paragraph, then write the email.’
AnswerBee
AnswerBee
If you want a RAG AI explanation simple enough for your COO: retrieval finds likely passages in your files; generation turns them into a reply. No retrieval → the model guesses from the public internet.
Step 1 — Chunk documents
Big pages get split into sections. Good headings help—see best knowledge base structure for AI search.
Step 2 — Search (keyword + semantic)
Keyword search matches exact terms (“1099”, “EU VAT”). Semantic search matches paraphrases (“tax form for contractors”). Products like AnswerBee often blend both in the retrieval pipeline.
Step 3 — Compose with guardrails
The model writes an answer conditioned on retrieved text. Honest vendors still ask humans to review—especially for money and legal topics.
Why citations matter
Citations turn retrieval into something auditable. That is the difference highlighted in AI email reply generator vs generic AI writing tools.
Try it
Sign up at the homepage, upload a policy, and compare drafts with and without your documents—you will feel RAG immediately.
Frequently asked questions
Short answers tied to this article—useful for skimmers and search snippets alike.
What is RAG in one sentence?
Retrieve relevant snippets from your documents, then let the model compose an answer using those snippets as context.
Is RAG the same as fine-tuning?
No. Fine-tuning changes model weights; RAG changes the context you pass in each request—usually faster to adopt for internal policies.
Why do I still see wrong answers?
If retrieval pulls the wrong passage—or nothing useful—the draft will be wrong. Content quality still matters.
Do I need vector search?
Many products combine keyword and semantic search. What matters is relevance tuning and citations, not buzzwords.