If you've been using AI for a while, you've probably run into its biggest limitation: it doesn't know things that happened after its training data was collected, and it doesn't know anything specific to your organization.
Ask ChatGPT about your company's internal processes and it'll make something up. Ask it about events from last month and it'll either say it doesn't know or — worse — confidently tell you something that isn't true.
RAG is the main solution to this problem, and it's worth understanding how it actually works.
The Core Problem RAG Solves
LLMs are trained on massive datasets that have a cutoff date. After that date: nothing. And they never had access to your proprietary information in the first place.
Two consequences:
- Outdated information — the model doesn't know recent events
- Hallucination — when it doesn't know something, it often invents a confident-sounding answer
RAG addresses both by connecting the model to an external knowledge base that you control and can update.
How It Works: The Three-Step Process
Step 1: Build the Knowledge Base (Done Once)
You take your documents — internal wikis, product docs, PDFs, customer FAQs, whatever — split them into smaller chunks, convert each chunk into a mathematical representation called an embedding, and store everything in a database.
The embedding is what makes search work. Instead of searching for exact keywords, a vector database matches based on meaning. So "can I return my order?" finds a chunk about the refund policy even if that chunk never uses the word "return."
Step 2: Retrieve Relevant Documents (Per Query)
When a user asks a question, the system:
- Converts the question into an embedding (same process as step 1)
- Searches the database for the most similar document chunks
- Returns the top 3–5 matches
This happens in milliseconds.
Step 3: Generate the Answer (Per Query)
The retrieved chunks get stuffed into the prompt alongside the user's question:
You are a helpful assistant. Answer using only the provided documents.
If the answer isn't in the documents, say so.
DOCUMENTS:
[Chunk 1: Return policy details...]
[Chunk 2: Shipping timeframe...]
[Chunk 3: Contact information...]
USER QUESTION: How do I return a damaged item?
The model reads the relevant chunks and produces a grounded answer. Because the actual policy is right there in the prompt, it can't make things up — or at least, it's much less likely to.
Why This Works Better Than Just Having a Bigger Model
A common question: can't I just use a model with a bigger context window and put all my docs in there?
Sometimes yes. If your knowledge base is small (under ~100 pages), this "long context" approach can work. But:
- Cost — feeding 100,000 tokens on every request is expensive
- Attention degradation — models don't attend equally to all content in a huge context; things buried in the middle get less attention
- Freshness — RAG databases can be updated instantly; training data can't
- Audit trail — with RAG, you can show users exactly which documents were used to generate an answer
RAG remains the better architecture for most production use cases even as context windows grow larger.
RAG in Plain Terms: An Analogy
Imagine you're a customer service rep for a company you just started at. You know nothing about the company's products yet. Before each customer call, a colleague hands you the 3 most relevant pages from the company's knowledge base.
You read those pages, then answer the customer's question based on what you just read. If the answer isn't in those pages, you say "I don't have that information — let me find out and get back to you."
That's RAG. The LLM is the customer service rep. The knowledge base is the company wiki. The retrieval system is the colleague finding the right pages. The pages handed to you are the retrieved chunks injected into the prompt.
Where RAG Falls Short
RAG is powerful but not magic. Common failure modes:
Retrieval misses the relevant document
The right answer exists in your knowledge base but the search doesn't find it. This happens when the query phrasing doesn't match the document phrasing, chunks are too small or too large, or the knowledge base has poor coverage.
Fix: better chunking strategies, metadata filtering, query rewriting.
Retrieved content contains errors
The source documents have outdated or incorrect information. RAG doesn't fact-check your knowledge base — it just retrieves from it.
Fix: keep source documents current and review them regularly.
Model ignores the retrieved content
The model answers from its training knowledge instead of the retrieved documents. This happens when grounding instructions in the prompt aren't strong enough.
Fix: stronger "use only the provided documents" instructions, lower temperature.
Sensitive information in the wrong hands
If different users should see different information, a naive RAG setup might retrieve documents a user isn't authorized to see.
Fix: implement access control at the retrieval layer — filter by user permissions before injecting content.
Prompting Well in RAG Systems
The prompt you wrap around retrieved content matters a lot. A few things that work:
Answer using ONLY the provided documents. Do not use knowledge from your
training data.
If the documents contain the answer, answer directly and cite which document
you're drawing from.
If the documents do NOT contain the answer, say: "I don't have that
information in my knowledge base."
Do not guess or extrapolate beyond what the documents say.
That last instruction — "do not guess or extrapolate" — is particularly important. Without it, models will take partial information and fill in gaps with plausible-sounding hallucinations.
Should You Use RAG?
RAG is the right choice when:
- Your information changes frequently (prices, policies, news)
- You need to cite sources
- You're working with proprietary or internal information
- The information is too large to fit in a single prompt
It's probably overkill when:
- You have a small, stable set of facts (just put them in the system prompt)
- You're doing general reasoning or creative tasks
- Latency is critical and the retrieval step is too slow
For most applications where you need the AI to know specific, current facts: use RAG. It's the most reliable way to ground LLM outputs in real information.
