Understand AI Better than 90% of People in 2 Minutes
Part 5: RAG
Imagine talking with the smartest person in the world who just woke up from a 3-year coma. They know millions of facts but have no idea about anything that's happened over the past three years.
Now imagine asking them: "Who won the 2026 World Cup?"
They'd have no idea.
That's essentially how Large Language Models work. They're trained on enormous amounts of public information, but they don't know about anything that happened after the data was pulled from the internet for training.
This is where Retrieval-Augmented Generation (RAG) comes in.
Instead of expecting the model to be retrained over and over again, RAG gives it the ability to look things up before answering. When you ask a question, the system first searches for relevant information from all of the information it has been given access to. It then sends those results along with your question to the language model, allowing the model to generate an answer based on current information plus its pre-trained data.
Think of it like having your phone in hand while playing Thursday Night Trivia at the local pub. If you don't know the answer off the top of your head, you just look it up.
Without RAG, an LLM answers from its memory, but that information may have been loaded years ago. With RAG, it answers using its memory and the new relevant data.
RAG Is Only as Smart as Your Filing Cabinet
One important thing to understand about RAG is that it can only retrieve information it's been given access to. That might be the internet, a company's internal documentation, a code repository, or all of the above. If the information isn't accessible—or doesn't exist—RAG can't magically find it.
It's like the filing cabinet in your office. If someone asks you to find last year's tax returns, your success depends entirely on how well it's organized and if they were ever filed away in the first place.
AI works the same way. If its "filing cabinet" is filled with accurate, up-to-date information, it can quickly find what it needs and generate a great answer. But if it's searching through outdated documents, missing files, or conflicting information, don't be surprised if the answer misses the mark. Great AI starts with great data.
Why You Need to Know RAG
If you're looking to get into AI, understanding RAG is one of the best skills you can gain. Almost every company building AI applications today uses some form of RAG. Businesses want engineers who can develop AI that searches their own documentation, knowledges base, and databases for the best responses. Your RAG skills will bubble you up to the top of the pile.
If you have legit RAG skills, you can add a quick internet search to make sure the soccer fan asking your AI who won the 2026 world cup, they can count on getting the right answer...Belgium. (yeah, that's right!)
Next up, Part 6: The AI Glossary
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