Can AI stay grounded in facts-

Ever had AI give you a confident answer that turned out to be wrong? Picture two chefs: one cooks from memory (fast, sometimes wrong), the other checks the recipe book (current, traceable). Good AI products work like the second chef. The technique is RAG — retrieval-augmented generation: retrieve trusted sources, augment the question with what you found, then generate the answer.

How RAG answers a question

An employee asks the HR assistant, "Can I work from home?"

1. Search the library of company documents.
2. Open the right document — the remote work policy.
3. Find the exact passage: employees may work remotely up to three days a week with manager approval.
4. Answer from that passage and show the source, so you can check it yourself.

The search decides the answer. If it finds the wrong page, the AI writes a confident answer from the wrong page.

Four things that decide if RAG gets it right

• Freshness — is the library current? A clinician asking for the blood pressure protocol needs last month's version, not last year's.
• Cutting — how documents are split before search. Split a quote from its speaker and you'll name the wrong person.
• Exact terms — search should catch codes and names like Pantone 2685, not only general meaning.
• Ranking — the enterprise refund clause should beat the consumer policy at the top of results.

Many AI "hallucinations" are really search failures. That's why good teams test the search separately from the generator, and why responsible AI always shows its sources. For a deeper look at the fixes, see how to reduce AI hallucinations in enterprise answers.

Before you choose an AI product, ask three questions: can I see the source behind each answer, how often is the content refreshed, and does search catch exact terms like codes or names? Good memory is nice — an open book is better. Learn more about our Responsible AI-by-Design Framework.