The devs at work keep saying: RAG
I figured it was just another AI acronym. Turns out it’s one of the most important concepts for actually using LLMs in production.
It’s easy to confuse terms like RAG with “fine-tuning” or “retraining.” But it’s different — and it matters.
Training vs Fine-tuning vs Retraining vs RAG
There’s a lot of jargon in AI, and half the time even people using the terms casually don’t define them. Here’s the breakdown I wish I’d had sooner:
Training
Who does it: OpenAI, Anthropic, Google — the labs that build foundation models.
What it is: Feeding the model massive datasets (internet text, books, code) and adjusting weights (billions of numerical knobs) until it predicts the next token well.
Cost: Tens of millions + supercomputers. Not something you or I can do.
Why it matters: Explains why GPT-4 can sound fluent on almost anything, but it’s frozen in time (knowledge cutoff).
Fine-tuning
Who does it: Companies or advanced ML teams.
What it is: Taking the already-trained model and adjusting a smaller set of weights with a domain-specific dataset (like 10k support transcripts).
Cost: Still expensive + technical. Requires curated data and infra.
Why it matters: Fine-tuning makes sense when you need the model to consistently behave differently across all interactions — not just when specific data is relevant.
Retraining
Who does it: Labs.
What it is: Training again — either from scratch or by resuming large-scale training with new data.
Cost: Astronomical.
Why it matters: You’ll hear it, but unless you’re in a frontier AI lab, you won’t do it.
RAG (Retrieval-Augmented Generation)
Who does it: Anyone — you, me, a GTM team.
What it is: Leaving the model’s weights alone but giving it external data at runtime. Build a knowledge base (your docs, blogs, case studies) and convert them into embeddings (mathematical representations that help the model find relevant content). When you ask GPT something, it retrieves the most relevant chunks → inserts them into the prompt → then generates an answer grounded in your data.
Cost: Far cheaper. Usually just needs an embedding DB + pipeline.
Why it matters: You can ground GPT in your actual brand data today, without retraining.
👉 In short:
Training / fine-tuning / retraining = change the model itself.
RAG = leave the model alone, just feed it better context.
Cheat Sheet: Training vs Fine-tuning vs RAG
Custom GPTs vs RAG
Custom GPTs use a lightweight form of RAG: you upload files, and the model retrieves from them in context.
But enterprise-grade RAG systems go further:
Control over indexing (how data is stored).
Control over chunking (how docs are split).
Control over retrieval strategies (which chunks get pulled).
That extra control is what makes RAG robust for high-stakes use cases (customer support, sales enablement, compliance).
Use Case: Marketers
When you want GPT to:
Write LinkedIn posts in your past style (retrieves your archive).
Generate campaign copy referencing your latest product launch.
Draft support docs based on your actual help center.
Answer sales questions using current case studies.
RAG is the difference between “generic AI copy” and “content grounded in your brand.”
Practical Next Steps:
Tools like Notion AI, ChatGPT (with file upload), and Custom GPTs already use lightweight RAG.
Use these for personal style grounding, content drafts, or small team knowledge bases.
If you’re dealing with large-scale knowledge (enterprise docs, customer support, sales enablement), advocate for proper RAG pipelines.
⚠️ RAG Reality Check
RAG isn’t magic. If your knowledge base is messy, RAG outputs will be messy too.
Chunking matters: split your docs poorly, and you’ll get Frankenstein answers stitched from random bits.
Garbage in, garbage out — but now with citations.
📝 Notes
RAG ≠ retraining. It pulls info in real time without touching weights.
Fine-tuning ≠ RAG. One rewires the model; the other feeds it fresh context.
Custom GPTs = basic RAG. Great for solo marketer projects (like maintaining brand voice or formatting spintax automatically).
Enterprise RAG = control over indexing, chunking, and retrieval strategies. Needs ML engineers + infra.
⚡ Outcome
Now when someone says RAG I know exactly what they mean: hook the LLM to a clean knowledge source so the output isn't hallucinating — it's grounded in data.