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description: As developers rely more on LLMs to write code, they risk losing intuitive familiarity with their own codebase. Several practical strategies help maintain code...
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> ## Documentation Index
> Fetch the complete documentation index at: https://daily.dev/llms.txt
> Use this file to discover all available pages before exploring further.

# How do you stay familiar with the code when it's written by an LLM?

**[RUBYLAND](https://daily.dev/sources/rubyla)** · 10 min read · 2 upvotes · 1 comments

## Summary

As developers rely more on LLMs to write code, they risk losing intuitive familiarity with their own codebase. Several practical strategies help maintain code understanding: deliberately making mistakes and seeking surprises, typing small changes yourself, asking probing questions about LLM-generated code, actively exploring the codebase in an editor rather than just reviewing diffs, generating HTML explainers for complex changes, and treating LLM output like a real PR review with inline comments. The core argument is that developers responsible for long-lived projects must actively cultivate code understanding rather than delegating it entirely to AI tools.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://www.aha.io/engineering/articles/staying-familiar-with-the-code-when-its-written-by-an-llm>

## Questions this post answers

### How can I stay familiar with code that an LLM wrote for my project?

Deliberately create friction instead of accepting smooth AI output: type small refinements yourself rather than delegating everything, ask an LLM to explain its choices and predict where changes will ripple, explore the code actively in an editor with jump-to-definition and breakpoints rather than passively reading a diff, and review AI-generated changes like a real pull request with inline comments before merging.

_daily.dev surfaces practical workflows for keeping ownership of AI-assisted codebases as habits, not one-off tips._

### What is the HTML explainer technique for reviewing large LLM-generated changes?

It means asking the LLM to generate a single-page HTML file that explains the feature and its architecture, including diagrams, inline code snippets, and editor links to the relevant files. This gives a starting point for exploring a large or complex change, though it does not replace actually digging into and writing code to build real understanding.

_Developers weighing new AI review workflows can track techniques like this one on daily.dev._

## Community discussion

Top comments from developers on daily.dev.

**@elenamanolitsaki** · 2 upvotes

> Very interesting, well-written, succinct and to the point :) It concerns a subject I think a lot about, and it's given me some lovely brain food to consider, in a very digestible way. Kudos to the author.

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---

Tags: [#llm](https://daily.dev/tags/llm), [#productivity](https://daily.dev/tags/productivity), [#ai-coding](https://daily.dev/tags/ai-coding), [#code-review](https://daily.dev/tags/code-review)

[View this post on daily.dev](https://daily.dev/posts/how-do-you-stay-familiar-with-the-code-when-it-s-written-by-an-llm--gtcifdlhr)

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