AI Automation
RAG vs Traditional Chatbots
Traditional chatbots either follow scripts or answer from what a model memorised. Retrieval-augmented generation (RAG) looks up your current documents before answering. The difference shows up in accuracy, freshness and trust.
6 min read ·
How traditional chatbots answer
Scripted bots match keywords to pre-written replies and break on unexpected questions. Plain LLM chatbots write fluent answers from general training data, which can be out of date or confidently wrong about your business.
How RAG answers
A RAG system searches your documents for the passages relevant to the question, gives them to the model and asks it to answer only from that material — citing where each fact came from.
- Answers reflect your latest documents
- Every answer can link to its source
- Access can be filtered by who is asking
- Updating a document updates the answers
Where RAG needs care
Retrieval quality decides answer quality. Chunking, search strategy, re-ranking and evaluation on real questions are what separate a reliable assistant from a demo.
Frequently asked questions
Does RAG require training a model?
No. RAG uses existing models and retrieves your content at question time, so there is no model training step.
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