How We Work with RAG: Smarter Answers from Your Own Knowledge

At airi.chat we believe AI should help your support team sound like you — not like a generic chatbot. That’s why we built our AI coach, Airi, on top of RAG (Retrieval-Augmented Generation). In simple terms: we find the right information first, then use it to craft accurate, on-brand answers.

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What Is RAG, and Why Does It Matter?

RAG is a technique that improves AI answers by grounding them in real data instead of guessing from training data.

Without RAG:

The model answers from its general knowledge. It can be wrong, outdated, or off-brand. “What’s your return policy?” might get a made-up or generic answer.

With RAG:

The system looks up your return policy from your knowledge base and past conversations, then generates an answer based on that. It’s specific, accurate, and aligned with how you actually do business.

Think of it like giving a colleague a reference shelf before they answer a customer: they look things up, then answer using your documents.

Where Do We Look for Information?

We don’t rely on a single source. We combine several so agents get the most useful context:

1. Your Knowledge Base

Your help docs, FAQs, and internal guides. If you’ve written it, we can use it.

2. Curated Answer Snippets

Short, approved answers you’ve already used — especially for policies, pricing, and common questions. These are prioritized when they’re a strong match.

3. Past Conversations

Similar support conversations and how they were resolved. Airi can surface patterns from your own team’s best responses.

4. Company Context

Your product, terminology, and business rules. This keeps suggestions consistent with how you talk and operate.

All of this is fetched and blended before the AI generates a suggestion, so answers stay grounded in your data.

How We Decide What to Use

Not every piece of information is equally good. We use a clear priority system:

1. Verified snippets first — Pre-approved answers that match the question well.

2. Knowledge base articles — When no snippet fits, we use the most relevant articles.

3. Conversation summaries — When helpful, we add context from similar past chats.

4. General knowledge — Only when nothing else is a good fit.

We also use confidence thresholds: if a match isn’t strong enough, we don’t force it. Better to say “I’m not sure” or suggest a human handoff than to give a shaky answer.

Making Search Smarter

Questions can be vague or phrased in many ways. We use several techniques to improve retrieval:

HyDE (Hypothetical Document Embedding)

Instead of only searching by the exact question, we first imagine a good answer (“Our return policy allows returns within 30 days…”), then search using that. This helps us find content that answers the question, not just text that repeats it. It’s especially helpful for short or ambiguous queries.

Reranking

We don’t take the first match. We pull several candidates and rerank them to pick the most relevant one. That reduces noise and improves accuracy.

Cross-Language Search

If your knowledge base is in one language and the customer asks in another, we handle that. We can translate queries or search across languages so multilingual teams still get the right context.

Verification

Before suggesting a snippet, we can run it through a quick check: does it really answer this question? If not, we skip it and look elsewhere instead of forcing a bad match.

Built for Support Teams

Our RAG setup is designed around customer support:

Speed — Caching and parallel lookups keep response times low, so agents get suggestions in real time.
Consistency — Answers come from your knowledge base and approved snippets, so agents stay on message.
Safety — Strict workspace isolation so each customer only sees their own data.
Flexibility — Works with different languages, query styles, and knowledge base structures.

The Result

When an agent gets a suggestion from Airi, it’s grounded in your knowledge base, your past conversations, and your policies — not generic AI output. That means:

Fewer wrong or off-brand answers
Faster onboarding for new agents
More confident, consistent support
A smarter AI that actually learns from your data

Questions about how we use RAG at airi.chat? Reach out — we’re happy to dive deeper.