RAG (Retrieval Augmented Generation)
A technique that enhances LLM responses by first retrieving relevant information from a knowledge base, then using it to generate accurate, grounded answers.
In-Depth Explanation
Retrieval Augmented Generation (RAG) is an architecture that combines the power of LLMs with the accuracy of information retrieval. Instead of relying solely on a model's trained knowledge, RAG retrieves relevant documents and uses them to generate grounded, accurate responses.
How RAG works:
- Query: User asks a question
- Retrieve: System finds relevant documents from knowledge base
- Augment: Retrieved context is added to the prompt
- Generate: LLM generates response based on the context
- Return: User receives an answer grounded in your data
Benefits of RAG:
- Accuracy: Responses based on your verified data
- Freshness: Update knowledge without retraining
- Traceability: Cite sources for responses
- Cost: Cheaper than fine-tuning
- Control: Determine exactly what information is available
RAG components:
- Embedding model: Converts text to vectors
- Vector database: Stores and searches embeddings
- Retriever: Finds relevant documents
- LLM: Generates responses from context
- Orchestration: Coordinates the pipeline
Business Context
RAG is the most effective way to make AI accurate about your business. It reduces hallucinations by 80-95% and enables real-time knowledge updates.
How Clever Ops Uses This
RAG implementation is our core expertise at Clever Ops. We've built RAG systems for Australian businesses across industries, enabling accurate, trustworthy AI assistants grounded in business-specific knowledge.
Example Use Case
"A customer support bot retrieves relevant help articles and product documentation before answering questions, ensuring accurate responses."