What is RAG and why it matters for customer support chatbots
RAG (retrieval-augmented generation) grounds AI answers in your own content so support bots stay accurate, on-brand, and up to date.
The short definition
Retrieval-augmented generation (RAG) combines two steps:
- Retrieve the most relevant passages from your knowledge sources.
- Generate a response that cites and uses that context.
Instead of guessing, the model answers with your documentation, policies, and product truth.
Why RAG changes support outcomes
Traditional chatbots fail when information changes. RAG keeps the assistant grounded, which reduces churn-driving mistakes.
Key benefits for support teams:
- Higher accuracy because answers are anchored to your latest docs.
- Lower handle time by resolving questions on the first response.
- Consistent brand voice even across long or complex questions.
- Faster onboarding since you update content, not bot scripts.
How Inqry AI applies RAG
Inqry AI builds a contextual index of your sources and uses hybrid search to find the right evidence quickly. That context is then injected into each answer, with safeguards to avoid hallucinations.
Typical sources include:
- Product documentation and API references
- Policy pages (refunds, security, SLAs)
- Internal FAQs and support macros
A quick checklist before you deploy
- Identify the top 20 questions your team answers weekly.
- Ensure those answers exist in a single source of truth.
- Keep policy changes time-stamped so updates flow into the bot.
FAQ
Is RAG the same as fine-tuning?
No. Fine-tuning changes model weights. RAG keeps the model general and supplies fresh context at runtime.
Do I need a vector database to use RAG?
Not necessarily. Inqry AI handles the retrieval layer for you, including semantic and keyword search.