What is retrieval augmented generation (RAG)?
Fetch the relevant documents first, put them in the prompt, then ask the model to answer using them.
Also called RAG.
In one sentence
Fetch the relevant documents first, put them in the prompt, then ask the model to answer using them.
Why it matters to a business
It is the cheapest way to make a model answer from your data rather than its training. Try this before anyone proposes fine-tuning or a custom model.
Real examples
- Azure AI Search
- LlamaIndex
- LangChain
RAG vs fine-tuning
Retrieval hands the model the right documents at question time. Fine-tuning changes the model itself. Retrieval is cheaper, updates instantly when your content changes, and is the right answer far more often than it gets chosen.
What is fine-tuning?
Every term here was explained to us once, too. Asking is how you learn it.
Retrieval augmented generation, in questions
The same answer, in the shape people ask it.
What is retrieval augmented generation (RAG)?
Fetch the relevant documents first, put them in the prompt, then ask the model to answer using them.
Why does Retrieval augmented generation matter to a business?
It is the cheapest way to make a model answer from your data rather than its training. Try this before anyone proposes fine-tuning or a custom model.
What are examples of Retrieval augmented generation?
Azure AI Search, LlamaIndex, LangChain.
RAG vs fine-tuning: what is the difference?
Retrieval hands the model the right documents at question time. Fine-tuning changes the model itself. Retrieval is cheaper, updates instantly when your content changes, and is the right answer far more often than it gets chosen.
Wondering what this means for your organization?
Thirty minutes, no slides, no jargon. We will tell you which of this applies to you, and which is noise.