Resources

Retrieval-Augmented Generation (RAG) is a way to make AI answers more reliable.

Instead of relying only on what the model learned during training, the AI first looks up relevant information from trusted sources, like company files, manuals, or databases. Then it uses that information to build its answer.

That helps because:

• it can use current information
• it gives answers that are more specific and grounded in real documents
• it is less likely to make things up (“hallucinate”)
• you do not need to retrain the whole model every time your information changes, which saves time and money

A simple way to think about it:

Normal LLM: answers from memory
RAG system: checks the notes first, then answers