How to Prompt AI to Explain a Technical Concept

Ask AI to "explain this simply" and it usually just shortens the sentences while keeping the same technical vocabulary, which is not the same thing as making a concept understandable to someone without the background. The actual trick is naming the audience specifically and giving the model a concrete stand-in they already understand, a comparison to something ordinary, rather than a vaguer instruction to be simple.

Name the audience, not just the tone

"Explain this to a non-technical person" is under-specified, since a non-technical investor, a non-technical parent, and a non-technical new hire all need a different explanation. Name the specific person and what they already know.

Explain what a database index is to a small business owner who
understands how a filing cabinet works but has never written code.
Use the filing cabinet as the comparison throughout. Do not use the
words 'query', 'schema', or 'B-tree'. End with one sentence on why
they should care: it's the difference between waiting 3 seconds or
3 minutes for a report to load.

The banned-words list matters more than it looks. Left unconstrained, a model tends to define one jargon term using two others, which is how technical explanations quietly stay technical while sounding simplified. Naming the specific words to avoid forces an actual translation rather than a rephrasing.

Ask for the comparison before the explanation

A two-step version produces a noticeably better result than asking for both at once. First, ask only for the comparison object, then ask for the explanation built on it.

1. "What is an everyday process a small business owner would recognize that works like a database index?" (get the comparison first, check that it actually fits)

2. "Now explain a database index using that filing cabinet comparison, for someone who has never coded." (build the explanation on a comparison you have already approved)

Doing this in two passes lets you catch a bad comparison before it gets built into a full explanation. If the model suggests something as unfamiliar as the original concept, you find out at the cheap step, not after a paragraph is already written around it.

The most common failure: hedging

A second common failure is over-hedging, littering the explanation with "it's a bit more complicated than this, but" and "technically speaking" disclaimers that undermine the simplification the audience actually needs. Ask explicitly for a version with no hedging, followed by a separate short paragraph listing what was simplified away, for a technical reader who might check the work later.

Write the explanation above with zero hedging or qualifiers, stated
as plainly as if it were completely true. Then, in a separate
paragraph labeled 'What this leaves out', list the technical
nuances a developer would want to add back in.

Separating the clean explanation from its own caveats keeps both audiences served without watering down either one. The non-technical reader gets a clear, confident answer. Anyone technical who reads further gets the honest list of what was simplified.

A worked before-and-after

Before, from an open prompt ("explain APIs simply"): "An API is an interface that allows two software systems to communicate by defining a set of rules for requests and responses." This is shorter than a textbook definition, but it uses interface, systems, requests, and responses as if they were already understood, which is the exact failure this method fixes.

After, using a named audience and banned words ("explain to a restaurant owner who has never coded, comparing it to how a waiter takes an order to the kitchen, avoid the words interface, request, and response"): "Think of a waiter taking your order to the kitchen. You don't need to know how the kitchen works, you just tell the waiter what you want, and they bring back your food. An API is the waiter between two pieces of software, one asks for something, the other delivers it, without either side needing to understand how the other actually works inside." The same named-audience approach works for code too, covered separately in how to prompt AI to explain code line by line.

FAQ

Does this work for explaining AI concepts themselves?

Yes, and it is worth using on your own product's AI features when writing customer-facing copy or support documentation, for exactly the same reason: a feature description full of unexplained AI jargon fails the audience it is meant to help.

What if I don't know a good comparison myself?

That is what the two-step method is for. Ask the model to suggest three comparison objects first, pick the one that actually fits from your own judgment, then build the explanation on it, rather than trying to invent the comparison yourself before prompting.

How do I know if the explanation actually landed?

Test it on someone who actually fits the named audience, not a colleague who already understands the concept. A technical reader will nod along at almost any explanation because they can fill gaps automatically. The intended audience cannot, which is exactly why they are the only real test.

For the companion skill of getting a model to check its own accuracy, how to prompt AI to check its own work covers a related but distinct verification technique. For turning a technical explanation into something sellable, how to sell an AI product to a non-technical buyer goes further into that specific audience. Our prompt engineering overview covers the broader framework this technique sits inside.