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Retrieval Augmented Generation Explained Print

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Giving a model access to your information.

THE PROBLEM IT SOLVES

A language model knows only what it learned during training.

It does not know your documents, your policies or your data.

THE APPROACH

When a question is asked, search your documents for relevant passages, then give the model those passages along with the question.

The model answers from the material provided.

WHY THIS IS BETTER THAN FINE-TUNING FOR KNOWLEDGE

The information stays in your documents, so it can be updated without retraining The model can cite what it used Hallucination is substantially reduced, because the answer is grounded in provided text

WHERE IT IS USED

Internal knowledge assistants Customer support answering from documentation Document question-answering Research over a collection

WHAT IT REQUIRES

Your documents, indexed for search A system connecting search to the model

WHAT STILL GOES WRONG

If the search retrieves the wrong passages, the answer is wrong The model may still add unsupported detail

WHY IT MATTERS TO YOU

Most practical business applications of language models use this approach.


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