How RAG Works...
Source: Dev.to
What is Retrieval‑Augmented Generation (RAG)?
If you’ve been following the AI space, you’ve definitely heard the buzzword RAG (Retrieval‑Augmented Generation). It sounds complex, but it’s essentially a way to make AI useful in real‑world scenarios.
Think of it as an “open‑book” test for an AI model:
- Standard LLM – The model must answer purely from memory, which can lead to forgotten details or hallucinations.
- RAG – The model can consult a reference (e.g., a textbook) at query time, retrieve the exact paragraph needed, and then answer based on that information.
How RAG Works
RAG breaks down into three simple steps:
1. Retrieval (The Search)
When a question is asked (e.g., “What is my company’s leave policy?”), the system first searches a private knowledge base—PDFs, documents, emails, etc.—to locate relevant passages. The query is not sent directly to the language model.
2. Augmentation (The Context)
The retrieved passages are combined with the original question to form a prompt. Example prompt:
Using these notes [paste notes here], answer this question: What is the leave policy?3. Generation (The Answer)
A language model such as GPT‑4 or Claude reads the augmented prompt and generates an answer solely based on the supplied context.
Benefits of RAG
- Trust – Because the answer is grounded in retrieved documents, the model is less likely to hallucinate.
- Recency – Updating a document in the knowledge base instantly makes the new information available to the AI, without costly retraining.
- Efficiency – You avoid the expense of repeatedly retraining large models whenever source material changes.
Why RAG Matters
RAG is often called the “Hello World” of AI engineering. It marks the transition from being a mere user of AI to becoming a builder who can integrate AI with proprietary data, delivering accurate, up‑to‑date, and trustworthy results.