RAG
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AI AutomationWhat is RAG?
Retrieval-Augmented Generation is an approach where the language model consults an external data source before answering.
The problem with regular ChatGPT is that it only knows what it is trained to do. He doesn't know anything about your company. And if you ask, he will make it up.
RAG solves this problem: • Retrieval (Search) - the system finds relevant fragments in your knowledge base • Augmented - found fragments are added to the query as context • Generation - the model formulates an answer based on these fragments
Basically, RAG is an AI that first "reads the documentation" and then responds.
How does RAG work?
Three steps: 1) Your documents are divided into fragments and indexed in the vector database. 2) When a customer asks a question, the system finds 3-5 relevant fragments. 3) The language model receives questions + fragments and formulates an answer. The client simply receives the correct answer in 2 seconds.
Options for using RAG in business
The bot answers questions about services, prices, conditions - based on real documents. Closes up to 85% of appeals.
New employee asks - RAG-bot finds answer in wiki in 3 seconds instead of 20 minutes.
100 contracts - RAG will find the right item in seconds.
RAG bot replaces L1 support for complex products with extensive documentation.
/ FAQ
Fine-tuning is expensive, time-consuming, and difficult to update. RAG - cheap, fast, updates in minutes. Added a document - the bot already knows.
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