RAG (Retrieval-Augmented Generation)

RAG is the most widely used technique for enabling an AI model to answer with your company's specific information, without training a custom model.

How it works

1. Your documents are split into chunks and indexed as embeddings (numerical vectors)
2. When a user asks a question, the most relevant chunks are retrieved
3. The AI model generates a response using those chunks as context

Advantages over fine-tuning

• No need to retrain models (time and cost savings)
• Data updates in real time
• You can audit which sources the model used to answer
• Lower risk of hallucinations

Use cases

• Internal chatbots with company documentation
• Sales assistants with product sheets
• Technical support with manuals and FAQs
• Semantic search engines over knowledge bases