RAG (Retrieval-Augmented Generation)

RAG, or Retrieval-Augmented Generation, is a method that lets AI models pull in real-time information from external sources to answer questions more accurately. Instead of relying only on what the model learned during training, RAG gives it access to fresh, relevant data.

For example, if you ask, “How do this month’s sales compare to last month?”, a retrieval system searches your internal documents, databases, or knowledge bases to find the most relevant info. That information is then added to your original prompt, giving the model the context it needs to generate a smarter, more grounded response.

Without RAG, the model may try to guess or "hallucinate" an answer if it wasn’t trained on the exact details you’re asking about. RAG helps reduce those hallucinations by giving the model access to real, up-to-date facts.

Quick recap of where RAG fits in:

• Pre-training: Teaches the model general knowledge and language

• Fine-tuning: Customizes the model for specific tasks or industries

• RLHF: Aligns responses with human preferences

• Prompt engineering: Helps users phrase prompts to guide better outputs

• RAG: Adds relevant, real-time information the model wasn’t trained on

Want a deeper dive? Here’s a great overview comparing fine-tuning, RAG, and prompt engineering: