DEV Community: Rob Methven

7 Prompt Tricks to Stop AI From Overexplaining Like It's Defending a Thesis

Rob Methven — Wed, 19 Aug 2026 21:56:21 +0000

7 Prompt Tricks to Stop AI From Overexplaining Like It's Defending a Thesis

You asked where the bathroom is. Your tour guide launches into the geological formation of the building's foundation, the architectural significance of the plumbing system, and a brief tangent about Roman aqueducts. Five minutes later, you still don't know which hallway to take, and you're seriously reconsidering this whole excursion.

That's what happens when you ask ChatGPT a simple question without giving it boundaries. The model wants to be helpful, which in its training meant being thorough. Really thorough. Geological-history-of-every-pebble thorough. Let's fix that.

Why Your Guide Won't Shut Up

AI models learn from massive datasets where comprehensive answers typically got higher marks. When you ask a question, the system starts generating tokens (the individual word chunks that make up its response) and keeps going until it believes it's covered every possible angle. It doesn't know you just need the three-step fix.

Ask ChatGPT "How do I reset my password?" and you'll get a 300-word essay covering security best practices, password managers, and the history of authentication. You needed three steps. Your guide thinks you want the full historical walking tour.

Tell Them You're On a Tight Schedule

Add length constraints directly to your prompt. Use "in one sentence," "in 50 words or less," or "give me just the steps." Request specific formats: "as a bullet list," "as a numbered list," "in a single paragraph." These give the model a clear stopping point instead of letting it wander through every related topic.

Instead of "How do I make a pivot table in Excel?", try "In 3 steps: how do I make a pivot table in Excel?" The second gets you a tight, actionable answer. No detour through data analysis theory.

Make Them Skip the Scenic Route

Add "no preamble" or "skip the introduction" to cut the warm-up paragraph. The model typically starts with context-setting because that's how good explanations work in its training data. But you already know why you're asking.

When asking "What's the difference between PNG and JPG?", add "no preamble, direct answer only" and you'll skip three paragraphs about image compression history. You get straight to "PNG supports transparency and is lossless; JPG is smaller but loses quality." You've just avoided learning about bitmap rendering when you only needed to know which export button to click.

Try "assume I know the basics" to prevent explanations of foundational concepts. End with "be direct" to reinforce brevity throughout.

Set Up a Fast-Pass System

Use Custom Instructions in ChatGPT settings to set a default tone: "Always be concise. No introductions or conclusions unless asked." This trains the model's context window to expect brevity from you automatically, without adding instructions to every single prompt.

Create reusable templates saved in a doc: "[Question]. Answer in under 100 words, no background needed." Chain commands: "First give me the answer in one line, then if I reply ASK, give me more detail." This lets you control the depth on demand.

A product manager can save "Explain [concept] to a non-technical stakeholder in 2 sentences" and reuse it for every feature explanation. The system remembers your preferences across the conversation, so you're not constantly redirecting your guide back to the main path.

When You Actually Want the Full Tour

Sometimes comprehensive answers are useful. The verbose default exists for a reason. Use "explain thoroughly" or "include examples and context" when you need depth. Toggle between modes: start with "brief overview," then "now explain [specific part] in detail."

Learning a new programming language? Start with "What are Python functions in one sentence?" to get oriented, then ask "Now show me 3 examples with different use cases" to go deeper. You're building understanding in layers, pulling detail only where you need it, rather than trying to absorb everything at once and retaining nothing.

So What Can YOU Do With This?

Customer support teams can get faster answers to policy questions without scrolling past three paragraphs of context they already know. Writers can request snappy headline options instead of explanations about why each headline works (they'll judge that themselves, thanks). Developers can get code snippets without the accompanying tutorial when they're just trying to remember syntax. Students can quiz themselves with direct answers before requesting full explanations. Anyone can save 30 seconds per query, which compounds to hours per week.

Real workflow: a social media manager asks "Caption for a product launch post, 15 words max, enthusiastic tone" and gets five options immediately. No lecture on brand voice strategy. They paste the best one, move to the next task, and finish their queue an hour earlier than usual.

TL;DR

Add length limits ("in one sentence," "under 50 words") to your prompts
Use "no preamble" and "direct answer only" to cut the warm-up paragraphs
Save prompt templates in Custom Instructions or a doc for consistent brevity
Toggle between brief and detailed modes depending on whether you're learning or executing

At least your guide means well. They just really, really love talking about sedimentary rock formations.

How to Teach Your AI Agent a New Trick: Building a Custom Skill in 15 Minutes

Rob Methven — Mon, 10 Aug 2026 23:38:29 +0000

How to Teach Your AI Agent a New Trick: Building a Custom Skill in 15 Minutes

You know that guy at the mall in the red suit? He's amazing at the classics. "Ho ho ho!" Nailed it. Asking kids what they want for Christmas? Decades of experience. But when a seven-year-old asks for a Squishmallow in the shade "somewhere between lavender and periwinkle, but NOT the axolotl," he just smiles and nods. He has no idea what any of those words mean. Now imagine you slip him a laminated cheat sheet with photos, prices, and exact store locations. Suddenly he's giving parents aisle numbers and SKU codes. Same Santa, completely different level of usefulness.

That's what we're doing today. You're going to teach your AI agent something specific to your actual work, and it'll take about as long as your lunch break.

Why Your AI Keeps Saying "I'll Do My Best" (Then Doesn't)

AI agents arrive with impressive general knowledge. They can write emails, explain concepts, summarize documents. But they don't know anything about YOUR world. They've never seen your company's spreadsheet format. They don't know that "adjusted revenue" means something different in your department than it does everywhere else. They've certainly never heard of a Squishmallow.

Without specific instructions, the agent improvises. It guesses. It produces something that looks roughly right but misses the details that actually matter. You wanted periwinkle, you got blue.

A skill is packaged instructions combined with resources (data files, code snippets, templates, access to specific tools) that teach the agent how to handle one particular task your way. Not the general way. YOUR way.

The difference between an AI that's "pretty helpful sometimes" and one that actually saves you three hours every week comes down to whether it has the right cheat sheet.

The Two Things Every Skill Needs (Instructions and Props)

First, you need clear, step-by-step instructions. Not vague guidance. Actual steps that tell the agent exactly what to do and in what order. "Pull data from these specific columns. Calculate this specific thing. Format the output this specific way." The kind of instructions you'd give someone who will ask zero follow-up questions because they're not allowed to.

Second, you need the actual resources required to execute those instructions. Code snippets the agent should run. Data files it should read. Templates it should fill in. API access it needs to fetch information.

Santa needs to know what a Squishmallow IS (the concept, the variations, the current hot sellers) and where to actually get one (aisle 7, third shelf, also available online with two-day shipping). One without the other leaves him either knowledgeable but useless or confident but wrong.

Every AI tool that supports skills does some version of this pairing. To make it concrete, the rest of this guide uses Claude Code, the version of Claude that runs in your terminal, because its skills are just files on your computer and you can read them with your own eyes. A skill there is a folder. Inside the folder is a file called SKILL.md holding the instructions, and any other file you drop in beside it becomes one of the props.

Say you're building a skill that generates your weekly email report. The instructions are "pull data from columns B through F, calculate percent change from last week, format as three bullet points highlighting the biggest changes, use casual but professional tone." The resources are your actual spreadsheet template and the email format you always use. Both pieces, one package.

Building Your First Cheat Sheet (The 15-Minute Version)

Pick one task you do every week that follows the same steps every time. Maybe it's formatting meeting notes. Maybe it's pulling numbers for a status update. Something repetitive enough that you could do it half-asleep.

Write down those steps in plain English. Pretend you're training someone who takes everything literally and never asks clarifying questions. "Open the attached template. Sum column C. Compare to last week's total. Write three bullet points. The first bullet should highlight the biggest change. Use percentages, not decimals. Keep it under 100 words."

Now gather the resources. The spreadsheet template. The email format. The data source. Whatever the agent needs to actually execute those steps. If you reference "the template" in your instructions, you need to attach the actual template file.

Now make the folder. The name you give it becomes the command you type later, so keep it short and obvious:

mkdir -p ~/.claude/skills/weekly-report

Inside that folder, create a file named SKILL.md. The top of the file is a small block of settings fenced by three dashes, and the rest is the instructions themselves:

---
description: "Builds the Friday sales email from the weekly numbers. Use when the user asks for the weekly report or the Friday email."
---

## Instructions

1. Read numbers.csv in this folder.
2. Sum column C and compare it to last week's total.
3. Write exactly three bullet points, biggest change first.
4. Use percentages with the % symbol and no decimals.
5. Keep the whole thing under 100 words, casual but professional.

That description line is doing more work than it looks. It's how Claude decides on its own that this skill is the right one for what you just asked, so write it as "what this does, and when to reach for it" rather than a title. Everything in the settings block is optional, but this is the one worth getting right.

Then drop your props in the same folder, the numbers.csv your instructions reference, a template, whatever the steps actually need. Put it where you said it would be.

Test it by typing /weekly-report. You can also just ask for the Friday email in plain English and let Claude match your request to that description. The first test, you'll spot something you forgot to specify. You'll add it. Second test usually works.

Two details worth knowing before you build a pile of these. A skill in ~/.claude/skills/ follows you into every project, while one in a project's own .claude/skills/ folder stays put and ships to whoever clones the repo, which is the one you want for anything team specific. And skill instructions cost you nothing until they're actually used, so a long, fussy, deeply specific skill is not a tax on every other conversation you have.

When Your Skill Bombs (And How to Fix It Fast)

First test, the agent does something confidently and completely wrong. It sends parents to the toy aisle from 1987 where Squishmallows definitely do not exist and never have.

Your instructions were too vague. You assumed knowledge the agent doesn't have. You said "format it nicely" when you meant "use bullet points with bold headers and no more than 50 characters per line." Add one example showing the exact output you want. Show him an actual photo so he knows what he's looking for.

Second test, the agent says it can't access something. Your resource file isn't actually attached, or it's in a format the agent can't read, or you referenced "the Q3 template" but attached a file named "sales_data_final_v2.csv."

Check your attachments. Make sure file names match what your instructions say. Confirm the agent can actually open that file type.

Your report-generating skill outputs 0.15 instead of 15% because you didn't specify format. You add "format all percentages with the % symbol, no decimals" and test again. This time it works. Third time is usually the charm because you've made all the implicit stuff explicit.

So What Can YOU Do With This?

Create a "Weekly Report Generator" skill with your actual spreadsheet format and email template. Every Friday at 3pm becomes a three-minute task instead of a thirty-minute one.

Build a "Meeting Notes Formatter" that takes your messy real-time notes and outputs them in your company's standard format with action items, decisions, and follow-ups properly categorized.

Make a "Customer Email Responder" skill loaded with your FAQ answers, your company's tone guidelines, and examples of great responses. Handle the routine questions in seconds, spend your time on the complex ones.

Design a "Data Cleanup" skill that knows your specific column names, understands your validation rules, and fixes the recurring errors in that one report everyone uses but nobody wants to maintain.

Any task you do the same way more than twice a month is a skill waiting to happen. A marketing manager creates a "Social Post Analyzer" skill with their brand guidelines and competitor examples. What used to take thirty minutes of review now takes three minutes of tweaking AI output.

TL;DR

A skill packages step-by-step instructions with the actual resources (templates, data files, code) an AI agent needs to handle a specific task in your workflow
In Claude Code a skill is just a folder holding a SKILL.md file, where a short description tells Claude when to reach for it and the numbered steps tell it what to do
Build one in 15 minutes by picking a repetitive task, writing the steps in plain English like you're training someone who asks no questions, dropping your files in beside them, and testing twice
First test will probably fail because you assumed knowledge or forgot to attach something. Add examples and check your resources are actually there
Any task you do the same way twice a month is worth automating into a skill that saves you hours every week

After all, nobody wants to be the Santa who confidently directs parents to the Cabbage Patch Kids when the kid specifically asked for the purple Squishmallow unicorn.

What Are AI Agent 'Tools' and Why Can't It Just Know How to Book a Flight?

Rob Methven — Mon, 10 Aug 2026 23:36:04 +0000

What Are AI Agent 'Tools' and Why Can't It Just Know How to Book a Flight?

Picture a highly motivated octopus sitting in a control room. Eight arms ready, eyes alert, brain firing on all cylinders. This octopus is incredibly smart. It understands exactly what you need. It can read complex instructions, make sophisticated decisions, and communicate brilliantly about what should happen next.

There's just one problem: the control room came empty. The octopus can only interact with buttons, levers, and switches that someone actually installed in its tank. No matter how intelligent it is, if there's no "book a flight" button bolted to the wall, those tentacles are just waving at water.

This is the gap that trips up almost everyone first encountering AI agents. You ask for something perfectly reasonable, the AI responds with confident enthusiasm about helping, and then... nothing happens. Or worse, something definitely didn't happen but the AI seems to think it did.

The Tank Came Empty

Language models like GPT-4 or Claude are astonishingly good at understanding your requests and generating relevant text. They can write that email, draft that flight itinerary, compose a perfect message to your team about why you'll be out next Tuesday.

But generating text about an action is completely different from performing it.

What transforms a language model into an agent is the addition of tools, which are specific functions or APIs the agent can call to interact with external systems. One button might trigger "send email via Gmail API." Another lever could pull "search calendar for conflicts." A third switch might activate "query flight booking database."

Without these explicit connections, the octopus just floats there, understanding everything, capable of nothing. ChatGPT can write a beautiful email to your boss requesting time off, but it can't actually send it or check your calendar for conflicts unless those specific controls are connected.

Installing Buttons

Developers write function descriptions that the AI can read and understand. These descriptions work like instruction manuals mounted next to each button.

"This lever searches Google Drive. Give it a search term as text. It returns a list of matching files."

"This button creates a Slack message. Provide a channel name and message content. It posts the message and returns a confirmation."

The octopus reads these manuals and builds a mental map of its control room. When you make a request, it figures out which buttons to push, in what order, and with what settings. Then it actually calls those functions, triggering real actions in real systems outside the tank. The tool executes, something happens in the external world, and results flow back in.

Zapier's AI Actions or custom GPTs let you connect specific services. You might give your agent a "search my Google Drive" button, a "create Slack message" lever, and a "check weather API" switch. When you ask "Find that proposal doc and share it in the marketing channel," the octopus decides which buttons to push and in what order, then actually triggers those real functions. First the search happens, returning actual files. Then the Slack post goes out, pinging real people.

Reaching for Buttons That Aren't There

Here's where things get weird. The octopus is so smart and so eager to help that it will confidently reach for buttons it thinks should obviously exist, even when they don't.

The AI invents plausible-sounding functions that were never actually installed, then tries to use them. From its perspective, "book_flight" sounds perfectly reasonable. Surely that button exists, right?

This is why you see responses like "I've booked your flight to Denver for next Tuesday" followed by... nothing. No confirmation email. No calendar entry. No actual reservation anywhere. The octopus is miming pushing a button that doesn't exist, generating confident text about actions it cannot actually perform.

The tool library determines capability boundaries, not the AI's intelligence. A brilliant octopus in a sparse control room is still limited to whatever's been bolted down.

More Buttons, More Complexity

Each new tool expands what's possible. Connect a weather API, and suddenly your agent can check forecasts. Add a database query function, and it can pull sales numbers. Install a calendar integration, and meetings can actually get scheduled.

But each addition also increases complexity. With fifty tools available, the agent must choose correctly from fifty options every single time. Which button for this particular situation? Should I pull two levers in sequence or just one?

The octopus has to read your request, survey its entire control room, and pick the right combination of actions. More buttons means more power, but also more opportunities to push the wrong one or forget a crucial step. Well-designed tool sets have clear, distinct purposes that minimize overlap and confusion. Vague, redundant tools create hesitation and errors.

Microsoft's Copilot in enterprise settings might have tools for searching SharePoint, querying databases, sending Teams messages, creating calendar events, and generating reports. "Schedule a meeting with everyone who worked on the Q3 campaign" needs calendar access, employee directory lookup, and project tracking queries. Get the order wrong or use the wrong search parameters, and the octopus just flails around pressing things randomly.

So What Can YOU Do With This?

When evaluating AI assistants, ask what tools they actually have access to, not just how smart they sound. A demo that shows fluid conversation means nothing if the agent can't actually touch your systems.

For custom agents (GPTs, Zapier bots, or whatever platform you're using), start with two or three essential tools rather than connecting everything at once. Pick the actions you need most often. Test those thoroughly. Add more only after the core functions work reliably.

If an AI claims it did something but nothing happened, check whether that tool was actually installed. Nine times out of ten, the button simply doesn't exist. The agent isn't lying exactly, it's just reaching for controls that aren't there.

Build your own simple tools if you're comfortable with it. Many platforms let you connect APIs without writing code. Zapier, Make, and similar services offer visual interfaces for linking services together, then exposing those connections as tools your agent can use.

Understand that "I can help with that!" doesn't mean the agent CAN, only that it understands the request. Comprehension and capability are completely separate things.

Before trusting an AI agent to manage your calendar, verify it has actual Google Calendar or Outlook API access, not just the ability to talk about calendars persuasively. Test it with a low-stakes request first. If you're building a custom agent for your team, connect your three most-used systems (Slack, your CRM, your project tracker) before adding the nice-to-haves. Watch what works and what causes confusion.

TL;DR

AI agents are only as capable as the specific tools (functions, APIs, integrations) you give them access to. Smart responses don't equal real actions.
Tools are explicitly connected functions that let the agent interact with external systems. Without them, the agent can only generate text about what should happen.
A confident response explaining how it "booked your flight" doesn't mean the agent actually has the tool to do what you asked. Check what's really connected.
Start simple when building agents: connect a few essential tools first, test thoroughly, then expand based on what actually works in practice.

And remember, no matter how many times the octopus gestures enthusiastically toward the northwest corner of the tank, if there's no button there, nothing's getting booked.

What Is Temperature in AI? (And How to Stop Getting Poetry When You Asked for a Grocery List)

Rob Methven — Thu, 30 Jul 2026 21:16:31 +0000

What Is Temperature in AI? (And How to Stop Getting Poetry When You Asked for a Grocery List)

Remember Magic 8-Balls? Those plastic oracles you'd shake for life advice, only to get "Reply hazy, try again" when you asked if your crush liked you back? Imagine someone added a little dial on the bottom. Turn it all the way to zero and the thing becomes painfully predictable, only ever offering "Yes" or "Most likely." Crank it all the way up and suddenly it's inventing answers that never appeared in the original twenty options, things like "Ask your neighbor's cat" and "The moon suggests Thursday."

That dial is temperature, and every AI language model has one.

How the dial works

Temperature is a setting, usually ranging from 0 to 2, that tells an AI model how much risk to take when picking the next word. The model calculates the probability of every possible next word, then has to pick one. At low temperatures, it plays it safe and picks the most probable option almost every time. At high temperatures, it's willing to gamble on unlikely choices further down the list.

This is why you can ask ChatGPT the exact same question twice and get a straightforward answer on Monday and what appears to be surrealist fiction on Tuesday.

When you ask ChatGPT to write a professional email at temperature zero, you'll get "Dear Sir or Madam, I am writing to follow up on our previous correspondence..." every single time you hit enter. Set temperature to 1.5 and it might open with "Greetings, fellow traveler of the inbox wilderness" because that phrasing, while statistically improbable, is now in play.

Why boring is sometimes good

At temperature zero, you get the most boring dinner guest imaginable. It always picks the single most likely next token (the technical term for a chunk of text, usually a word or part of one). No variety, no surprises, just the statistical favorite every single time.

This turns out to be perfect when you need factual accuracy, code that actually compiles, or data extraction where creativity equals mistakes. If you're asking AI to pull invoice numbers from receipts, temperature zero means it outputs "12345" when it sees "12345" on the page. Temperature 1.8 means it might decide "12345" looks like it could also be "12346" or "INVOICE_NUM" and helpfully improvise. You did not ask for help. You asked for accuracy.

Higher temperatures let the model sample from less probable options, which introduces variety and surprise. The model isn't working harder or thinking more deeply. It's just willing to pick the seventh most likely word instead of always crowning first place.

The settings you'll actually use

Temperature 0 to 0.3 is where you only get the top one or two stock answers. Use this range for math, code, formatting tasks, translations, or anything where there's objectively a right answer and you'd like the AI to find it instead of workshopping alternatives.

Temperature 0.7 to 1.0 is the default zone. Responses have personality without veering into experimental theater. This range works well for drafting emails, brainstorming with a partner who stays on topic, writing explanations, and general question-answering where you want it to sound human.

Temperature 1.2 to 2.0 is where things start consulting the stars. Answers get weird, creative, and sometimes incoherent. The model is now free to choose words it would normally never pick, which means you'll get unexpected metaphors, unusual phrasings, and the occasional sentence that seems to have been written by someone who just woke up from a vivid dream. Use this range for creative writing, breaking through writer's block, or generating ideas you'd never think of yourself.

Most tools default to 0.7 or 1.0 because it balances usefulness with personality. The AI sounds natural without routinely suggesting you consult celestial bodies for business decisions.

Ask Claude or ChatGPT to name your new bakery at temperature 0.2 and you'll get "Sweet Treats Bakery" and "The Bread Box." Crank it to 1.8 and you'll get "Flourish & Punish," "The Carb Conspiracy," and "Gluten Intentions" because the model is now free to take swings at word combinations it would normally consider too risky.

So what can YOU do with this?

When using ChatGPT, Claude, or API tools, look for "temperature" in advanced settings. It's often tucked away under "model parameters" or a similar menu that the interface doesn't show you by default. You may need to click a settings icon or switch to a playground mode.

Drop temperature to 0 or 0.1 when you need consistent, repeatable outputs. This means generating CSV files, writing SQL queries, proofreading documents for typos, summarizing meeting notes into action items, extracting data from forms, or translating text where you want it to mean the same thing every time.

Keep it at default (0.7 to 1.0) for everyday tasks like drafting emails, answering questions, getting writing assistance, or having the AI explain concepts. This is the range where it sounds like a helpful colleague instead of either a robot or someone who took too much cold medicine.

Push it to 1.3 or higher when you're stuck creatively. Use it for brainstorming campaign slogans, writing fiction, coming up with team names, exploring wild "what if" scenarios, or generating options you'd never consider yourself. The goal here is surprise.

If the AI is being too weird or off-topic, lower the temperature. If it's boring you with the same phrasing over and over, raise it. You're adjusting how much it's allowed to wander from the beaten path.

In tools like OpenAI Playground or when making API calls, you can set temperature per request. This means you can use low temperature for the facts and high temperature for the flavor in the same project. You're writing a product launch email? Use temperature 0.2 to generate the bullet points listing features (accurate, no creative reinterpretation of what your product actually does). Then switch to temperature 1.2 to write three different subject line options that don't sound like every other SaaS email currently clogging your inbox.

TL;DR

Temperature controls how predictable (low) versus creative (high) AI responses are, usually on a scale from 0 to 2.
Low temperature (0 to 0.3) makes AI pick the most likely next word every time. Use it for facts, code, data, and anything where accuracy matters more than variety.
High temperature (1.2+) lets AI gamble on unlikely word choices. Use it for brainstorming, creative writing, and when you need unexpected ideas.
Most tools default to 0.7 to 1.0, the sweet spot for natural conversation without the moon suggesting Thursday.

Though honestly, the cat might have some thoughts on your Q3 projections.

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.