Any Piece of Code Explained in Plain English Using AI for Non-Developers

Someone sends you a script. A developer hands over a macro. You receive a vendor’s integration code and need to know what it does before you approve it. Or you just want to understand what the automation that’s been running quietly in the background for two years actually does. You don’t need to learn to code to answer these questions — you need to ask the right AI the right question.

Getting a plain-English explanation of code from an AI model is one of the most immediately useful things non-developers can do with these tools, and it’s considerably more straightforward than most people expect.

The Basic Approach

The core technique is simple: paste the code into Claude, ChatGPT, or Gemini and ask “explain what this code does in plain English, as if I’ve never coded before.” That prompt alone produces useful explanations for most common code types — formulas, scripts, SQL queries, macros. The model reads the code and translates its logic into language that describes the actions and outcomes without requiring you to understand the syntax.

The quality of the explanation improves with a bit more context. If you know what the code is supposed to do — or what system it came from — include that: “This is a Google Apps Script that a colleague wrote to automate our weekly report. Explain what it does, what data it processes, and what it outputs.” That framing helps the AI orient its explanation around the purpose rather than just the mechanics, which makes the result more useful for someone trying to understand whether the code does what it’s supposed to.

Asking Better Questions

Generic explanations are a starting point. Specific questions produce more useful answers. After getting the initial explanation, follow up with questions about the parts that matter most to you: “What happens if the input data is blank?”, “What does line 47 specifically do?”, “Is there anything in this code that modifies or deletes data, rather than just reading it?”, “What would break if we changed the column name from ‘Status’ to ‘status’?” These targeted questions produce precisely the information you actually need rather than a complete narrative you have to read through to find what’s relevant.

For scripts that connect to external services or databases, it’s worth specifically asking: “Does this code send data anywhere external, and if so, where and what data?” and “Does this code require specific permissions or access rights to run?” These questions surface the security and access implications that matter most when reviewing code you didn’t write.

Understanding SQL Queries

SQL is probably the most common code that non-developers encounter without understanding it — queries embedded in reports, dashboards, and data exports that were written by analysts or developers and are now running on production systems. Getting AI to explain a SQL query is particularly valuable because the consequences of misunderstanding a query can be significant: a query that’s deleting rather than selecting records, or joining tables in a way that inflates the row count, is exactly the kind of thing a non-developer needs to catch before trusting the output.

For SQL, the most useful explanatory prompt is: “Explain this SQL query in plain English. Tell me what tables it reads from, what filtering conditions it applies, how it joins the data, and what the final output contains. Note anything that looks like it could produce unexpected results or that I should be careful about.” That structured request produces a comprehensive explanation rather than a general summary.

Reviewing AI-Generated Code Before Using It

There’s a specific and increasingly common use case here: you asked an AI to write a script for you, and now you want to understand what it wrote before you run it on real data. This is good practice and AI code explanation tools it directly. Paste the code you received back into the same or a different AI model and ask it to explain what the code does, what data it modifies, and whether there are any risks or edge cases to be aware of before running it.

Getting a second model to review code from the first catches the most obvious problems — the cases where the generating model produced something technically plausible but subtly wrong. It’s not a substitute for a developer’s code review on anything important, but it’s a meaningful first check that catches a proportion of errors before they cause problems.

Building Vocabulary as You Go

One underappreciated benefit of using AI to explain code regularly is the vocabulary you accumulate. After asking for twenty explanations of scripts and queries, concepts like “loop,” “function,” “variable,” “conditional,” “API call,” and “database join” stop being opaque and start having meaning — not because you’ve studied them, but because you’ve encountered them explained in context repeatedly. That accumulated vocabulary makes the next explanation easier to understand, makes conversations with developers more productive, and makes it progressively easier to evaluate whether an AI explanation is accurate or whether something doesn’t quite add up.

You don’t need to aim for this deliberately — it happens naturally as a side effect of using AI code explanation regularly. The practical implication is that starting now, even with simple formulas and basic scripts, builds a foundation that makes the more complex explanations you’ll encounter later more accessible.

Code That Connects Systems: Integrations and APIs

A specific category of code that non-developers increasingly encounter is integration code — scripts and configurations that connect two systems together. A Zapier webhook handler, a Salesforce custom flow action, an API connector that pulls data from a third-party service. This code is often written by a developer years ago and is now maintained by whoever inherited it. Getting AI to explain integration code is particularly valuable because the consequences of misunderstanding it are high — integration code often runs automatically and touches live production data.

For integration code, the most important explanatory questions are: what triggers this code to run, what data does it receive, what does it do with that data, and what external systems does it interact with. Those four questions give you the information you need to assess risk before making any changes, and to explain to a developer or vendor what the code does if you need external help with it.

The habit of asking AI to explain code before modifying it — even code you’re fairly confident you understand — catches assumptions that turn out to be wrong more often than most people expect. Five minutes of explanation before an hour of modification is a consistently good investment, and it’s a habit that costs almost nothing to build.

Non-developers who build the habit of using AI to understand code before acting on it make better decisions about when to attempt a fix themselves, when to escalate to a developer, and when to flag something as a security or compliance concern. That judgment — informed by genuine understanding rather than guesswork — is the real value that AI code explanation delivers.

What to Do With the Explanation

Understanding what code does is the beginning, not the end. Once you have a clear explanation, you can make an informed decision about whether to run it, approve it, modify it, or flag it for a developer’s attention. For scripts and automations already running in your organisation, an explanation gives you the basis for documenting what the tool does — invaluable when the person who built it has left. For vendor-provided code, it gives you the basis for an informed conversation rather than blind acceptance. The explanation turns code from something opaque and intimidating into something you can reason about, even without technical training.