Definition
Retrieval-Augmented Generation (RAG) is a technique that enhances Large Language Models (LLMs) by retrieving relevant data from an external knowledge base before generating a response.
Why It Matters
Standard LLMs (like GPT-4) are frozen in time and don't know your private business data. RAG allows you to 'chat with your PDF' or database without the massive cost of fine-tuning a model.
How It Works
- 1
Your documents are split into chunks and converted into 'vectors' (numbers).
- 2
When a user asks a question, we search for the most similar vectors.
- 3
We paste those relevant chunks into the prompt context.
- 4
The LLM answers the question using ONLY that context.
The NetForce Take
For 95% of B2B use cases, RAG is superior to Fine-Tuning. It's cheaper, faster, and reduces hallucinations because you can cite the source.