RAG
Why RAG Is Needed
LLMs have knowledge boundaries:
- They don't know your private knowledge base
- They may not know the latest information
- They tend to hallucinate in specialized domains
RAG's value is: turning "model answers from memory" into "model checks references first, then answers", making responses evidence-based, more controllable, and more timely.
What Is RAG
One-line definition: RAG (Retrieval Augmented Generation) is an architecture of "retrieve external materials first, then generate answers based on those materials."
Its core process:
User question
↓
1. Retrieval: find the most relevant document chunks
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2. Augment: stitch the chunks into the Prompt
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3. Generate: model generates the answer based on these materials
Analogy: Like being allowed to "read reference materials first, then answer questions" during an exam — the reference materials are the retrieved content.
In this process, "retrieval" is the most critical step. For details on retrieval methods, see the Retrieval entry.
How to Do It: When to Use RAG
Scenarios suited for RAG:
- Company internal document Q&A
- Real-time information Q&A
- Legal, medical, financial professional Q&A
Scenarios where RAG may not be needed:
- General common sense Q&A (the model already has this knowledge)
- Simple one-time generation (no need for external knowledge)
Common pitfalls:
- RAG isn't just connecting a vector database: what truly affects results is the entire chain — how chunks are split, how retrieval is done, how context is stitched, how the model is guided to answer based on materials rather than improvise
- More retrieval isn't always better: too much irrelevant context confuses the model. Here you need to pay attention to Context Window limits — more isn't better
- RAG can't completely eliminate hallucinations: retrieval errors, stitching errors, and generation errors can all continue to cause problems
Remember this: RAG makes models "check first, then answer" — it solves the problem of model knowledge boundaries and hallucinations, but effectiveness depends on the entire retrieval chain's quality, not just swapping in a stronger model.
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