How to give Your LLM a "memory upgrade" with RAG
A simple guide to the Retrieval-Augmented Generation (RAG) framework for non-technical teams
You ask ChatGPT a very specific question:
“What’s our refund policy for customers on the Pro plan?”
It gives you an answer that sounds confident. Polished, even.
But it’s wrong.
Not maliciously… it just doesn’t know. Because by default, most language models don’t have access to your docs, your dashboards, or your latest policy updates.
They only know what they were trained on months (or years) ago.
That’s not a model issue. It’s a data issue.
And it’s exactly where RAG comes in.
🔍 What RAG Actually Is (In Plain English)
RAG stands for Retrieval-Augmented Generation.
Let’s ignore the jargon for a second and focus on what it does:
RAG gives your AI access to real, current, context-specific information at the exact moment it needs it, instead of forcing it to guess from memory from a long time back.
Think of it like this:
Prompting is like talking to a smart intern.
Fine-tuning is like training that intern to follow your process.
RAG is like giving them access to Google Drive, Notion, or your help docs.
They don’t have to know everything. They just need to know where to look.
🧠 Why RAG Exists (And What Problem It Solves)
Language models are trained on frozen snapshots of the internet. They don’t know what changed last week. They don’t know your company’s internal knowledge. They can’t “look things up” by default.
So when they don’t know the answer, they guess.
Sometimes that guess is fine.
But sometimes it’s confidently wrong… and that’s worse than silence.
RAG solves this by retrieving real information in real time, feeding it into the model before it starts generating the answer.
🧠 How It Works
Here’s the high-level flow: (Without the Tech Headache)
You ask the AI a question
It runs a quick search over a connected database or doc set
It finds the most relevant snippets
It uses those snippets to generate a grounded, accurate response
No hallucinating. Just quoting the facts you gave it.
You can think of it as giving your AI an open book exam… with only your materials inside.
🧭 When to Use RAG (and When Not To)
RAG is especially useful when:
You need up-to-date or dynamic info (policies, prices, product details)
You work with large documents (contracts, knowledge bases, meeting notes)
You’re building tools that rely on internal knowledge (chatbots, assistants)
You want to avoid hallucinations in customer-facing content
RAG is not the best fit when:
You’re solving abstract, creative, or logic-based tasks
You need deep stylistic consistency (fine-tuning is better)
You don’t have high-quality data or documents to feed it
📚 Real Use Cases Where RAG Shines
RAG shines in situations where the model needs access to real-world, specific knowledge that lives outside of its default training.
Here’s how it shows up in practical ways:
🛠 Customer Support Bots
Imagine you’ve got a help center with 150+ articles. Instead of training the model on all of them (which becomes outdated fast), you let it fetch answers live from your current documentation.
A user asks, “Can I pause my subscription without canceling?”
The model pulls the exact paragraph from your billing policy and replies. Accurately, instantly, and without hallucinating.
📄 Contract & Policy Summarizers
Legal teams often deal with dense contracts, privacy policies, and terms of service. With RAG, the model can reference full documents and extract answers with source citations.
You upload a 60-page contract.
Then ask: “What happens if the supplier misses a delivery window?”
The model finds the exact clause and responds with the specific penalty terms… not just a generic guess.
📚 Product Onboarding & Internal Knowledge Assistants
Every company has internal documents: Onboarding decks, Notion pages, Slack threads. But nobody likes searching through them.
A new PM joins and asks: “What tools do we use for product analytics?”
The RAG-powered model checks your internal stack guide or team wiki and replies: “We use Amplitude for product analytics, and Metabase for internal dashboards.”
This is how you stop people from pinging ops or HR for answers they already wrote down.
🧑💼 Sales Enablement Tools
Sales reps need quick context before calls: past objections, deal history, and relevant feature updates.
A rep says: “Give me a quick rundown of the last 3 things this lead asked about.”
The model pulls from CRM notes, call summaries, and support tickets… and turns it into a quick prep brief.
Instead of sifting through tabs, the rep gets what they need in one question.
🏢 Internal HR and IT Bots
These teams answer the same 30 questions on repeat: “What’s our leave policy?”, “How do I access my payslip?”, “Where’s the security training link?”
With RAG, those answers are no longer copy-pasted by a human.
The model fetches the answer from your company handbook or internal SharePoint.
No more bottlenecks. Just real-time institutional memory.
…In any of the above cases, if your AI tool keeps saying, “I’m not sure” or “as of 2022…”
It’s probably missing RAG.
🛠 Try RAG Without Building Anything
You don’t need to be a developer to test how powerful RAG can be.
Here are real tools you can try right now:
ChatGPT + Upload a PDF / Link to Google Drive → Ask it anything about your docs
Perplexity.ai → Real-time web answers, with citations
ChatPDF / AskYourPDF → Drop a doc, ask it questions
Humata.ai → Deep file Q&A and summarization
Notion AI Q&A → Pull answers from your own workspace
Glean → Search across internal tools like Google Drive, Slack, and Confluence
Try uploading your own onboarding doc, company FAQ, or team handbook… and ask it the questions your teammates always ask.
Suddenly, it answers like someone who’s read the manual.
✏️ Don’t Have RAG Yet? Think Like Someone Who Does
If you can’t set up a full RAG workflow yet, here’s how to simulate it:
Create a Notion doc or Google Doc with all the content you want the model to “know”
Paste this content into ChatGPT before you ask your actual question
Optionally add:
“Based only on the following content, answer this question...”
Is it clunky? A bit.
But it works surprisingly well and helps you test if a RAG setup would improve results.
🧪 Try This in ChatGPT
Pick a doc your team relies on: onboarding guide, product FAQ, or pricing sheet.
Upload it to ChatGPT (Pro), then ask:
“What’s our latest refund policy?”
“What’s the difference between the Growth and Pro plans?”
“Which features are still in beta?”
Then try the same question without uploading the file… and watch the difference.
🧠 Final Takeaway
Prompts can only shape what the model already knows.
Fine-tuning can help it internalize your tone and workflows.
But RAG makes the model useful in the real world.
It's what connects your AI to the ever-changing, unstructured, messy knowledge your team actually works with.
RAG turns a model from a smart speaker into a search-savvy partner: one that knows when to pause, look something up, and respond with context that’s fresh, grounded, and trustworthy.
And here’s the part most teams miss:
If your AI is confidently wrong, it’s not the model’s fault.
It just didn’t have the right data in front of it.
RAG fixes that.
You don’t have to rebuild the model.
You just have to feed it something real to work with.
Next on LLMentary:
We’ll explore the hottest topics in LLMs: MCPs (Model Context Protocols) and AI Agents. We’ll see what even are they and how they’re making a significant improvement in your model’s performance by essentially giving it superpowers beyond your imagination!
Until then, stay curious.
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