There is a particular kind of professional frustration that doesn't get talked about enough: knowing something well, and then watching people's eyes glaze over the moment you try to explain it. You're not being unclear. You're using the right words — for someone in your field. For everyone else, you might as well be reading from a manual in another language.
This happens to IT professionals explaining a software issue to their director. To doctors describing a treatment plan to a worried patient. To engineers presenting to a sales team. To accountants walking a small business owner through what the numbers actually mean. The knowledge is there. The translation is hard.
AI tools — particularly ChatGPT and Claude — are surprisingly good at this specific task. Not at knowing the technical material better than you do, but at helping you find the version of an explanation that lands for a particular audience. It's one of the most practically useful things you can do with these tools, and most people haven't tried it yet.
Why AI Is Useful for Translation, Not Just Generation
Most people think of AI writing tools as something that generates content from scratch — you give it a topic, it writes something. That's one use, but it's not always the most valuable one. For technical explanation specifically, the more useful mode is translation: you bring the content, the AI helps you find a version that works for someone else.
Large language models — the technology behind ChatGPT, Claude, and similar tools — were trained on enormous amounts of text across almost every domain imaginable. That means they've absorbed not just the technical literature of a field but also the pop-science articles, the explainer videos' transcripts, the forum posts where experts answer beginner questions. They have, in a sense, practiced translating complexity into plain language many millions of times. You're drawing on that when you ask for help explaining something.
What this means practically: if you give Claude or ChatGPT a technical explanation in your own words, and tell it who the audience is and what they need to understand, it can usually produce something useful on the first attempt. Not perfect — you'll almost always need to review and adjust — but genuinely useful as a starting point.
The Basic Approach: Tell It the Audience First
The single most important thing you can do when asking AI to help you explain something is to describe the audience specifically. Not "explain this simply" — that's vague. Instead, describe who you're actually talking to and what they already know.
Compare these two approaches:
Vague: "Explain what a SQL database is in simple terms."
Specific: "I need to explain what a SQL database is to my company's marketing director. She understands spreadsheets well — rows, columns, filtering — but has no technical background. She needs to understand why the IT team can't just 'export everything to Excel.' Keep it under 150 words."
The second prompt produces something you can actually use. The AI knows the analogy to reach for (spreadsheets), the misconception to address (the Excel question), the length constraint, and the level of vocabulary that will land. Give it those inputs and you've done most of the work.
In my own experience, the audience description is the variable that matters most. "My boss," "a client," "my team" are all too broad. "A client who runs a restaurant and has never had a website before" — now the AI has something to work with.
Useful Prompt Patterns to Try
A few specific patterns that tend to produce good results for technical explanation:
The analogy request. Ask the AI to explain using an analogy drawn from something the audience already understands. "Explain how encryption works using an analogy that would make sense to someone who bakes — no technical terms." The AI is genuinely good at finding analogies. You may need to pick the one that fits best, but it will usually give you two or three options to work with.
The "what they actually need to know" version. Technical explanations often include more than the audience needs. Try: "I need to explain [topic] to [audience]. They don't need to understand how it works — they just need to understand why it matters to them and what decision they need to make based on it. What's the shortest version that achieves that?" This forces the AI to strip out the interesting-but-irrelevant parts.
The FAQ generator. If you're preparing to explain something in a meeting or presentation, ask: "What questions would a non-technical person in [role] most likely ask about [topic]? List the ten most common, in plain language." This is excellent preparation — it surfaces the questions people are likely to have but may not ask, and lets you prepare answers in advance.
The jargon finder. Paste a paragraph of your own explanation and ask: "What terms or phrases in this paragraph would a non-specialist not understand? List them and suggest plain-language alternatives for each." This is a useful editing pass — it catches the jargon you've stopped noticing because you use it every day.
What to Do With the Output
The AI's first attempt is a starting point, not a finished product. A few things to check before you use it:
Read it out loud. This sounds basic but it works. Explanations that look reasonable on screen sometimes sound stilted or oddly formal when spoken. If you're going to use this in a conversation or presentation, you need to know how it sounds, not just how it reads.
Check the analogies. AI-generated analogies are usually competent but occasionally wrong in subtle ways. The analogy might work for the first part of the explanation and then break down when extended. Read it with the audience in mind and check whether the analogy holds up throughout. If it doesn't, ask the AI to adjust it, or swap it for a different one from the alternatives it suggested.
Add your own context. The AI doesn't know the specific situation — the history, the stakes, the particular concern the person has. After you've got a clean plain-language explanation, you usually need to add a sentence or two that connects it to what that person actually cares about. "For you specifically, this means..." is often the sentence that makes the explanation land, and it's the one you have to write yourself.
Watch for overconfidence. AI tools will occasionally explain something in a way that sounds authoritative but is slightly off. For technical material you know well, this probably won't trip you up — you'll catch the error. But if you're explaining something at the edge of your own knowledge, review carefully before passing it on. The explanation may sound right without being right.
A Practical Example From Start to Finish
Say you're an IT manager who needs to explain to a small business owner why their file backup system is insufficient. They keep saying "we have a backup" but what they have is a single external hard drive sitting next to the main computer. Here's how the process might look:
You open Claude and type: "I need to explain to a small business owner why having a single external hard drive next to their main computer doesn't count as a proper backup. She understands the idea of backup in general but doesn't have a technical background. The key things I need her to understand: (1) the hard drive could fail at the same time as the main computer in a fire or theft, (2) there's no version history so if a file gets corrupted she won't catch it, (3) we need off-site or cloud backup as well. Keep it under 200 words, no jargon."
What comes back will be a serviceable explanation of those three points in plain language, probably using a simple analogy. You review it, adjust the tone to match how you actually talk, add a sentence about what you're recommending she do next, and you have something you can actually use in the conversation — either as a script to draw from or as a short document to send ahead of the meeting.
The whole process takes about five minutes. Writing that explanation from scratch, in the right tone and at the right level, might take twenty. The AI didn't do the thinking — you did. It handled the translation.
The Deeper Value of Getting This Right
There's something worth saying about what good technical explanation actually does. It's not just about communication efficiency. When someone understands something, they can make better decisions, ask better questions, and stop being anxious about the thing they didn't understand. The small business owner who finally understands what a real backup system means is not just better informed — she's less likely to have a catastrophic data loss event that damages her business.
That outcome has always been possible. What's changed is that the gap between "I know this thing" and "I can explain it to someone who doesn't" is now much easier to cross. You don't have to be a natural teacher to produce a good explanation. You have to know the material, know your audience, and be willing to spend five minutes with a tool that's quite good at the translation part.
Most of the time, the people who need to understand something from you are not asking because they want a lecture. They're asking because they need enough to move forward. AI can help you give them exactly that — no more, no less.