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What Is Retrieval Augmented Generation (RAG), and Why Does It Make AI So Much Less Confidently Wrong?

Rob Methven — Thu, 30 Jul 2026 15:42:25 +0000

What Is Retrieval Augmented Generation (RAG), and Why Does It Make AI So Much Less Confidently Wrong?

You know that game show contestant who buzzes in before the host finishes reading the question, shouts "MOUNT EVEREST!" with absolute certainty, and then looks genuinely confused when the correct answer turns out to be "the Treaty of Westphalia"? That's been AI for most of its existence. Supremely confident, occasionally correct, and deeply committed to whatever pops into its head first.

Now imagine that same contestant gets a new rule: before answering, they can phone a friend who has the exact relevant textbook already open to the right page. The friend reads them the actual answer, word for word, and then the contestant puts it in their own words for the judges. Suddenly, our buzzer-happy friend is getting questions right. That phone call is Retrieval Augmented Generation, and it's the reason AI chatbots have gotten weirdly more useful in the past year.

The Old Way: Confidently Wrong at 200 Miles Per Hour

Traditional large language models (big AI systems trained on tons of text) get trained on enormous dumps of text scraped from the internet, books, and whatever else researchers can feed them. Then the training ends. The model gets sealed off, frozen in time with whatever it learned.

When you ask a question, these models generate answers by predicting the most plausible-sounding next words based on patterns they memorized during training. It's essentially very sophisticated autocomplete. The AI has no fact-checking mechanism. It doesn't "know" anything in the way you know your own phone number. It just knows what words tend to follow other words.

This leads to what researchers politely call hallucinations, which is a fancy term for "making stuff up with tremendous confidence." The AI generates text that sounds authoritative and well-structured because it's learned the pattern of how authoritative text sounds. But the actual facts? Those might be completely invented.

The contestant hears "This Australian city..." and immediately slams the buzzer. "SYDNEY! The capital is Sydney!" It sounds right. It feels right. Major city, everyone's heard of it, definitely in Australia. The fact that the actual capital is Canberra becomes irrelevant when you're pattern-matching your way through life.

ChatGPT famously invented entirely fictional legal cases that lawyers then cited in real court filings. Customer service bots have confidently stated refund policies that don't exist. The AI isn't lying, exactly. It's just assembling plausible-sounding words with no mechanism to check if those words correspond to reality.

The Lifeline: Calling a Friend

RAG adds a crucial step before the AI opens its mouth. When you ask a question, the system first searches through a specific database, document collection, or knowledge base looking for relevant facts. This is the retrieval part, and it works like a very fast, very focused search engine.

The system pulls the most relevant chunks of actual text from real documents. These might be paragraphs from your company wiki, sections from product manuals, snippets from recent news articles, or passages from technical documentation. Then it hands those retrieved chunks to the AI as context before the AI generates its answer.

The friend on the phone is flipping through the textbook right now, finding the section on Australian capitals, and reading it out loud. "Canberra, founded in 1913, purpose-built as a compromise between Sydney and Melbourne..." The contestant hears this, processes it, and can now formulate an answer grounded in actual facts rather than vibes.

The AI still generates the final answer in its own words. It's not just copy-pasting from the retrieved documents. But now it's working from source material instead of pure pattern completion.

A company chatbot using RAG searches your internal product documentation before answering customer questions. When someone asks about the return policy, the system retrieves the actual text from page 47 of the policy manual, then generates a natural-sounding response based on that real information. No improvisation required.

Why This Fixes the Lying Problem (Mostly)

The answer is now grounded in retrieved text, not just statistical patterns from training. This changes everything.

First, the system can cite sources. You can trace the answer back to the specific document chunk it came from. If the AI tells you something weird, you can check the source it retrieved and see whether it interpreted correctly or went rogue.

Second, updates happen instantly. Change the document in the knowledge base, and the next retrieval pulls the new information. No expensive retraining needed. Your AI chatbot learns about the updated return policy the moment you save the new PDF.

Third, you can actually trust the answers more. The contestant has gone from "Sydney sounds right?" to "My friend just read me the encyclopedia entry: Canberra, founded 1913, chosen as a compromise between rival cities." That's a different level of reliability.

It's still not perfect. The friend might grab the wrong book. The contestant might misinterpret what they hear. But you've moved from "making things up" to "working from imperfect notes," which is a massive improvement.

Microsoft's Bing Chat and Perplexity AI show you the actual web pages they pulled facts from. You can click through and verify. Compare that to base ChatGPT, which would just generate plausible-sounding URLs that led nowhere.

The Two-Step Dance

Here's how it actually works when you ask a question.

Step one: your question triggers a search through the knowledge base. The system uses something called vector search or semantic matching, which is a fancy way of saying it looks for meaning, not just exact word matches. It finds documents that are conceptually related to your question.

Step two: the top results get stuffed into the AI's prompt as additional context. The AI receives both your original question and several paragraphs of retrieved information.

Step three: the AI generates its answer using both its training (all those patterns it learned) and the retrieved text (the actual facts you need). This combination is what makes it augmented generation. The generation process is enhanced by retrieval.

The contestant hears the question. The friend sprints to the reference shelf, checks the book, and reads back the relevant passage. The contestant then formulates their answer in complete sentences, using both the retrieved facts and their own ability to speak coherently.

This separation of knowing versus looking up keeps the AI flexible and current. The base model doesn't need to memorize every fact. It just needs to be good at understanding questions, searching effectively, and synthesizing retrieved information into helpful answers.

Notion AI searches your team's notes and meeting documents before answering "What did we decide about the logo?" Instead of hallucinating a decision based on what decisions typically sound like, it finds the actual meeting notes from last Tuesday and tells you what your team actually decided.

So What Can YOU Do With This?

Use AI tools that cite sources for research instead of vanilla ChatGPT. Perplexity, Bing Chat, and ChatGPT with web browsing enabled all use RAG under the hood. They'll show you where they got their information.

If you're building chatbots for work, RAG lets you feed them company wikis, instruction manuals, or Slack history without expensive retraining. The bot searches those documents in real time and generates answers grounded in your actual information.

Personal knowledge bases are getting interesting. Tools like Mem or Reflect use RAG to search your own notes when you ask questions. You can treat your scattered thoughts like a searchable database that talks back.

Always check the sources these systems show you. Retrieval can grab irrelevant chunks, especially if your question is ambiguous or your document collection is messy. The friend might read from the wrong chapter, and the contestant will confidently relay incorrect information with perfect grammar.

Understanding that "AI with access to the internet" usually means RAG under the hood helps you evaluate these tools. They're not magically omniscient. They're just really good at searching and synthesizing.

Ask ChatGPT with web browsing "What's the current mortgage rate?" and you'll get today's data retrieved from recent sources. Ask base ChatGPT the same question and you'll get numbers from 2021, confidently stated as if they're still current.

TL;DR

RAG (Retrieval Augmented Generation) makes AI look up real information in a knowledge base before answering, instead of just generating plausible-sounding text from training patterns alone.
It works through a two-step process: first searching documents for relevant facts, then feeding those facts to the AI as context while it generates its response.
This approach dramatically reduces hallucinations, enables source citations, and lets AI stay current without expensive retraining every time information changes.
You're already using it in tools like Bing Chat, Perplexity, ChatGPT with browsing, and workplace chatbots that actually know your company's internal documentation.

The contestant still gets questions wrong sometimes, but at least now they're misreading the textbook instead of just guessing that everything important happened in Sydney.

What Are Vector Embeddings? (And Why Your Spotify Wrapped Knows You Too Well)

Rob Methven — Thu, 30 Jul 2026 15:42:23 +0000

What Are Vector Embeddings? (And Why Your Spotify Wrapped Knows You Too Well)

Imagine a postal worker who never learned to read. Not a single word. Can't tell an A from a Z, wouldn't recognize their own name on a birthday card. And yet, this worker has memorized the precise physical location of every house in an infinite city. They navigate by pure spatial memory, knowing exactly which homes sit in the same cul-de-sac, which ones are clear across town, and which are practically next-door neighbors. They've never read a street name or house number in their life, but ask them which residences are similar and they'll tell you instantly based on coordinates alone.

This is how vector embeddings work. An embedding is a representation of data (a word, a song, an image, anything) as a list of numbers that captures its relationships to other data. Your Spotify playlist, that photo of your dog, the word "pizza," they all get converted into coordinates in a vast mathematical space. The system doesn't "understand" content the way you do. It just knows where everything sits and can measure distances between points. Close together means similar, far apart means different.

How the Worker Learned the Territory

The worker didn't start with this comprehensive mental map. They built it gradually by walking millions of routes and noticing what appeared together. Which houses had mail delivered on Tuesdays. Which residents waved to each other. Which blocks had similar holiday decorations. Over time, patterns emerged, and the worker positioned each house based on these observed relationships.

The AI does the same. It processes massive amounts of examples and notices what appears in similar contexts. Words that show up near the same other words get placed close together in the coordinate system. "King" and "queen" both appear frequently alongside "royalty," "throne," "crown," and "castle" in text, so their coordinates land in the same neighborhood. "Dog" and "puppy" show up in similar sentences ("walked the...," "fed the...," "adopted a..."), so despite sharing zero letters, their positions sit remarkably close.

When Spotify analyzes millions of songs, each track becomes a point in mathematical space based on tempo, instrumentation, mood, lyrical themes, and which playlists group them together. Songs that appear in similar contexts (the same workout mixes, the same rainy-day collections) land near each other. When you hit play on a track you love, Spotify finds its nearest neighbors in that mathematical space and suggests them next.

The resulting space doesn't have just two dimensions like latitude and longitude. It typically has hundreds or even thousands, each capturing different subtle patterns. One dimension might encode "is this about royalty," another "is this typically young or old," another "does this appear in formal or casual writing." The worker's mental model is unfathomably complex, tracking relationships across more directions than human brains can visualize.

Google's search trained on billions of web pages this way, gradually learning which words and phrases appear in similar contexts across the entire internet. The system never attended English class, never learned grammar rules. It just walked enough routes to know the territory.

Measuring Distance Between Houses

Ask the worker which two houses are practically neighbors, and they'll tell you instantly by measuring the distance between coordinates. No need to check street names or calculate driving routes. The mathematical distance in their mental map reveals similarity directly.

Measuring the distance between two sets of coordinates (often using cosine similarity, a particular way of calculating how close two points are) tells you how similar the underlying things are. Romance novels and tax law textbooks end up in completely different neighborhoods. "Hot" and "cold" sit far apart. "Hot" and "warm" are much closer. "Boiling" and "scalding" are practically roommates.

This distance measurement enables the famous "king minus man plus woman equals queen" example. The worker can do spatial math: start at the king's house, measure the direction and distance to the man's house, then move that exact same direction and distance from the woman's house. You'll land very near the queen's coordinates. The mathematical space captured a pattern (gender relationships in royal terminology) without anyone explicitly teaching it that rule.

Netflix uses this constantly. Your viewing history gets converted into coordinates. The system measures distances to millions of other users' coordinates, finds your nearest neighbors in taste-space, then recommends what those nearby viewers watched. Nobody manually tagged "Breaking Bad" as similar to "Better Call Saul." The coordinates just landed close together because people who watched one often watched the other.

Why This Illiterate Postal Worker Is Everywhere Now

The same trick works for any kind of data. Turn it into coordinates, measure distances, find neighbors. Product descriptions become coordinates powering Amazon's recommendations. Customer support tickets become coordinates that automatically route to the right team. Images become coordinates, which is how Instagram recognizes faces and how Google Photos finds every picture of your cat without you tagging a single one.

The worker doesn't need to understand what makes two houses similar, just that their coordinates sit close together. The AI doesn't need to "know" why two songs feel related or why two faces look alike. The mathematical distances handle everything.

This spatial approach also enables comparing across different types of content. Text search can find relevant images because both get converted to coordinates in related spaces. You can describe a mood in words and get song recommendations because the coordinates capture abstract similarities like "energetic" or "melancholy" that transcend the specific medium.

Your iPhone's photo app turns every face into coordinates, then clusters nearby points together. Those 47 photos spanning five years, different lighting, different angles, different haircuts? All land close enough in face-space that the system realizes they're the same person and creates an album automatically. You never labeled anything. The distances told the whole story.

So What Can YOU Do With This?

Understanding coordinates helps you use AI search tools more effectively. Instead of hunting for exact keywords, search by concept or example. Many new tools let you search your own documents, photos, or bookmarks by describing what you want ("that article about productivity I read last spring") rather than remembering precise titles.

You can improve your prompts to ChatGPT by knowing it finds responses "near" your query in meaning-space. More context helps it triangulate your location in that space and find better neighbors. Vague queries leave it guessing which neighborhood you're even in.

If you're building anything involving recommendations or search, coordinate-based tools are now accessible without advanced degrees. Vector databases like Pinecone, or built-in features in Notion and Obsidian, let you add "find similar items" functionality to your projects. You feed in your data, the system converts it to coordinates, and similarity search becomes almost trivial.

This knowledge also clarifies why AI makes certain weird mistakes. If two unrelated things accidentally land near each other in the mathematical space (maybe they appeared in similar contexts by coincidence), the system thinks they're similar even when they're obviously not to a human. The worker trusts the coordinates completely, even when the map occasionally lies.

Google Photos lets you search "beach sunset" and surfaces those exact scenes despite zero manual tags. ChatGPT answers questions phrased awkwardly because it finds meaning-neighbors to your garbled query. Both rely on placing related concepts near each other in coordinate space, making "close enough" astonishingly powerful.

TL;DR

Vector embeddings convert any data (words, images, songs) into coordinate lists where mathematical distance represents similarity, powering most modern AI recommendations and search. AI learns these coordinates by processing millions of examples, placing items that appear in similar contexts near each other automatically without manual rules. This single technique drives Netflix recommendations, Google Photos face recognition, Spotify suggestions, and semantic search across basically every platform you use daily. You can use coordinate-powered search right now in tools like Google Photos, Notion, and ChatGPT to find things by describing what you want instead of remembering exact keywords.

The postal worker still can't read their own name tag, but they just delivered your package to the right house on the first try anyway.