When should I use RAG vs fine-tuning?

Use RAG when: knowledge changes frequently, you need citations, data privacy is important, or you want quick deployment. Use fine-tuning when: you need specific behaviours/styles, have stable knowledge, or need lower latency. Many systems use both.

How much data do I need for RAG?

RAG can work with any amount of data - from a single document to millions. The key is data quality: well-organised, accurate, comprehensive documentation will produce better results than sparse or outdated content.

What if RAG retrieves irrelevant documents?

Poor retrieval quality is the #1 RAG failure mode. Solutions include: better chunking strategies, improved embedding models, hybrid search (combining semantic and keyword), re-ranking retrieved results, and filtering by metadata.

How do I evaluate RAG system quality?

Key metrics: retrieval precision/recall, answer accuracy, hallucination rate, citation accuracy, and user satisfaction. Create a test set of questions with known answers and measure performance systematically.

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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."

Frequently Asked Questions

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What is RAG (Retrieval Augmented Generation)?

Learn how RAG combines the power of large language models with your business data to provide accurate, contextual AI responses. Complete guide to understanding and implementing RAG systems.

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