And that's where most AI-generated explanations simply fail to deliver.
They're 100% correct, but they often seem to have been written by a lawyer who has never had the misfortune of trying to teach something to a thick junior dev at 11pm when you're being chased by the deadline.
Learning how to humanize AI generated code explanations is not just a nice addition; it's what determines whether a developer is just copying pieces of code blindly and not really understanding why they do, or understanding what's really going on.
Why "technically correct" won't cut it
Here's the problem with AI-given explanations—their goal is usually to be everything, not an—to be complete at the sacrifice of being understandable.
All will be documented, all weirder cases will be underlined...
But the why the part that gets under people's skin - is usually left out, forgotten, or hidden away.
If a junior developer reads the super dense, technical explanation, she doesn't say to herself, "I'll learn that." She says, "I can't learn that." That's a real problem.
Not simply for that individual developer's confidence, but for the productivity of the team, the rate at which the code is adopted, and the health of the project.
However, let's not forget: senior developers are also human.
Even complex engineering concepts benefit from straightforward an clear explanation, opposed to an overwhelming onslaught of irrelevant information.
Good communication helps all levels of skill through effective AI code explanation techniques that prioritize clarity and understanding.
Always.
The utility of analogies in explaining code
Analogies are probably the single most effective method used in making technological concepts stick.
And AI-produced material almost always doesn't use them properly - or not at all.
Say someone asks you to explain what a callback function is.
From a strictly technical perspective would be: "A callback is a piece of executable computer code that is passed as an argument to another function."Yes.
Quite unmemorable.
How about trying this: >the number of another restaurant when you're told the wait will be an hour.
Figure 6. You don't stay indefinitely at the host's desk. You go and sit down, carry on with whatever else, and they summon you when your table is ready.
The callback is that phone number.It's what gets "called back" when we are finished with the job.[...] Same idea.
completely different level of understanding.
The analogy would give the brain something to 'hold on to'.
Other analogies that actually do the trick: - Promises "Like ordering food through the web.
You don't get brought out a meal, but you get promised the meal will be brought out.
You can tailor your planning around that. APIs Like a waiter in a restaurant you tell the waiter what you want, they go into the kitchen and do the work. Recursion Like standing between two mirrors. Recursive
It is a reflection of a reflection. that's less than dumbed-down explanation.
They 're efficient.
—Simple Language without Loss of Depth Simple language doesn't equal superficiality.
Honoring the reader's time and cognitive capacity.
You are two completely different (two separate).
Always have an easy to grasp summary (approach
- here is a good demonstration of this). For example, instead of "iterates through an array via for loop, and applies a transformation to each entry", just say "takes a list and modifies each entry in it". Factor in one sentence at a time.
Followed by build.
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Time just how long you have to spend explaining, so that you can introduce the 'special terminology' as late as possible. Do this by -What do we mean by this -Turn to the page, 4 before you begin.
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Write in the second person. "You call this function" sounds more natural than "The developer may invoke this method." - Prefer active voice instead of the default passive voice.
Active are more natural and humane.
It's not to write down to a fifth grade level.
Knowing how to write at the right level for your audience – and when you don't know your audience, it is usually best to err on clarity over complication while enhancing code readability.
Making static explanations more interactive
Some explanations are plain.
Developers are all about the doing, not just the reading—and by creating interactions, you can combine the two in a way that text alone cannot.
Interactive techniques I find useful are:—• embedded snippets in a CodePen or Replit playground edition allowing the reader to play and execute the code through their browser • step-by-step revealing of the code block accompanied by label or comment hideouts • inline quizzes accompanying new concepts before progressing to a new section • try breaking it questioning, encouraging the developers to break the code to see the effect (honestly one of the best learning techniques out there) • code examples with mouse hover or commentout label explanations Written content becomes a lot more interactive with these.
And engagement, to me, is what turns explanations into something that actually changes how someone codes rather than just scanning over and forgetting.
Creating collaborative spaces with good explanations
The well-humanized code explanation achieves a number of subtle but valuable goals: it brings down the amount of effort one needs to put in if one wants to inquire.
When it's easy to have documentation or easy tutorials on top, it will encourage more developers to have a mock on and on.
Compare a team that relies on a cold, auto generated documentation with one that spends a moment describing the background, personality and context to the stuff.
The other team is going to have:- More meaningful / beneficial code reviews. (as all participants can understand what is being discussed...)- Faster onboarding- Less "what does this even do?" Slack messages at 9am- More enthusiasm to refactor or improve things Collaboration doesn't happen just because people share the same room or Slack team.
It's when people feel empowered and knowing they are capable to contribute.
Effective explanations foster such confidence.
The practical case for code adoption
Ill-documented code has a tendency to be throw away in favor of rewriting it, or even get used by somebody who doesn't know what it does. When code is documented well, it tends to be kept around the right way. had two copies of things in the system.
Codes documented with well-explained and comprehensible human language.
In my experience working with various development teams, the most significant factor influencing the usage of internal libraries and tools is the quality of its documentation.
Not on the code quality as such but on its documentation.
That's a shocking fact that most engineering team fails to consider.
Good-well-explain explained: - Get developers more "up to speed" on unfamiliar code faster - Optimize the number of bugs new developers cause, by having better understood implementation details. - Make it easier to integrate AI generated code blocks or thirdparty libraries. - Optimize project timelines by minimizing time wasted on clarifying things back-and-forth. Good explanations are an efficiency multiplier.
Chargebacks are there. The net effect is they are self funding.
Best practices for humanize AI generated code explanations
Humanized code documentation doesn't mean dramatically changing your existing workflow.
Small, frequent behaviors stack up quickly.
For creators of explanations: - Summarize everything with one simple sentence before diving into details - use at least one analogy per dogma - include a "common mistake" - section (these are often orders of magnitude more useful that simply explaining the nice happy path) - Explain things as if to a bright colleague new to this instance of the concept For consumers of AI-generated explanations: - Remember that all AI explanation is a readily-modifiable first draft - Add in your own personal context, team-specific or project-specific examples, etc - Cut out background noise that doesn't help you understand ("Backwards derivatives is a very sticky special case with a very gloppy implementation" is a considerable reduction from "Backwards derivatives happens in many tricky situations, whereas other cases use a more direct approach") - Employ a junior colleague to read the explanations, if said junior becomes confused, provide better explanations For uses of explainer by tech writers or researchers: - Be sure you're testing explanations with the folks who will use them, not just yourself, before you publish - Cycle to the earliest, lowest-tech form of the description that still work well, get feedback, then iterate down - Use progressive disclosure, build in layers - begin with high level overview, then zoom in as needed - use interactivity if the platform allows
And at this very moment, this shortage is literally eating up time, affects the team morale and discourages collaboration on a daily basis.
Humanizing code explanations- by means of similes, simple language, engaging interactivity and sincere consideration of how the humans who are learning actually do- is more than writing.
It's a wise investment in your team's productivity.
Developers at any level perform best when they know what they are working on.
A basic starting point and the foundation of understanding has to be simple explanations that really talk to humans, not just machines.
the code is only half the story.
be clear, make sure to also give the other half of the story.
