How to write code documentation fast
Why traditional documentation breaks down and how long term memory systems are changing how we preserve and transfer engineering knowledge.
Developers know what code documentation is. We know we should be doing it. We also know we rarely do it well, or consistently.
This isn't because we're lazy, but instead, because most documentation systems were never designed to keep up with the actual pace of software development.
Unless you’re writing and rewriting documentation as you code (a major context switcher), your documentation is being written after the fact, which means any future coding can inherently invalidate it. In other words, code evolves, teams change, and context gets lost.
The end result is a README that references deprecated flags, or inline comments that are stale within a sprint, and architectural diagrams that never survived the pivot. The worst part is that these docs become misleading, and misleading documentation is worse than having none at all.
This is what I call the documentation ‘death spiral.’
The more out-of-sync the docs are, the less we trust them.
The less we trust them, the less we use them.
The less we use them, the less we maintain them
… and so on.
That’s why so many teams quietly give up on docs altogether– but that’s starting to change. AI tools are shaping the way we write documentation–and the tools that work best aren’t changing the way we write, but remembering what we worked on.
What code documentation really is
A lot of developers still define documentation as a README file or an API reference. And while it’s true that those are important, they're just the surface.
Code documentation is anything that captures intent.
A single line of documentation might answer the question…
Why did we use this library over another?
What trade-off are we accepting here?
What edge case does this test cover?
Who originally requested this behavior?
When your code is well-documented, you don't just understand what it does, you also understand how to extend and build on top of what’s already there continuously, without breaking it.
What are the different types of code documentation?
Not all code documentation serves the same purpose. Understanding these distinctions helps teams write clearer, more targeted material.
There are several different types of ‘buckets’ that code documentation can be described as:
Internal documentation – written for developers within your organization. These docs provide technical details, decisions, and context for maintaining, extending, or debugging the codebase. They sometimes include system diagrams, configuration references, and environment setup guides.
External documentation – Your interface to the broader developer world. SDK guides, API references (similar to these ones), integration tutorials, anything that helps third-party users work with your tools. Clarity and polish are critical here because this is often where developer trust begins. This is also one of the most important types of documentation to keep up-to-date.
In-line documentation – Comments left directly in source code. Small, targeted explanations for why a function exists, what a specific argument does, or why a certain approach was chosen. These are essential for maintaining readability in complex or unconventional code sections.
Architectural documentation – Describes system design, component relationships, and the principles that guide how different parts interact. Helps new contributors orient themselves and makes cross-functional collaboration scalable.
Walkthrough documentation – Takes readers through real workflows, user journeys, or patterns within the codebase. It helps new team members understand what it’s like to work with a specific code repo and is usually invaluable for onboarding and tackling new feature areas.
Each type requires a different context, captured at various stages of the development process.
But here’s the thing–you’re not a robot who can document every single move while staying perfectly focused on the task. That’s exactly where AI comes in–to assist in your dev workflow, capture, and fill in the gaps so you can stay coasting.
The context capture problem (and how AI solves it)
Suppose you're working on a search indexing feature and abandon some type of overly-complicated pattern match in favor of a different type of implementation. You explain it in Slack, mention it again in a PR, then move on.
Three sprints later, someone asks: "Why did we do this?"
In a traditional setup, you'd need to dig through Slack, try to remember which PR that was, ask your coworker if they remember, and even then you’d probably misremember the rationale.
With ambient memory systems that have workstream activity, the whole thread is there, captured, timestamped, and embedded next to the code that shipped.
This is what the best AI tools do well. They remember, so they can surface contextual memories based on real events that happened–real decisions, conversations, fixes, bugs, and more.
How workstream activity changes everything
Pieces Workstream Activity, powered by the Long-Term Memory (LTM-2.5) Engine, represents the evolution from ‘reactive’ (i.e., after-the-fact) documentation to proactive context capture.
Instead of trying to remember what happened, it automatically generates timestamped roll-ups of your actual workflow.
Here's how it works in practice:
Every 30 minutes (or on-demand for custom time ranges), LTM-2.5 creates comprehensive summaries that include:
Core tasks & projects: Rich overviews of what you worked on, including problems encountered and solutions discovered
Key decisions & discussions: Important conversations with teammates, capturing who shared critical details and their implications
Documents & code reviewed: References to files, articles, and code snippets you accessed – including deep-linked URLs for instant context. Also includes relevant communication history (e.g., Slack, Google Chat, Teams), meeting notes, GitHub PRs, and other developer materials, extending beyond just terminal commands or browser activity.
Follow-up actions: Unfinished tasks, next steps, and reminders that help preserve momentum and ensure nothing falls through the cracks.
When you need to explain a decision to a teammate or write documentation, you don't need to start from memory. You start from a searchable, contextual record of what actually happened.
Need to onboard someone to a repository? Pull the relevant Workstream Activity from when key features were built.
Forgot what you fixed in that late-night debugging session? Search Workstream Activity by keyword.
Want to write documentation? Paste activity into your template and refine it.
Other tools that make fast documentation possible
There are plenty of traditional documentation generators, but most focus on static output: parsing docstrings, formatting references, auto-generating HTML. Tools like Doxygen, JSDoc, or Sphinx are strong for this.
But for memory-aware workflows where documentation evolves from context, not just comments, we need a different approach:
Memory-first tools
Pieces: Context-first long term memory capture with AI-assisted activity. Captures decisions as they happen across your entire workflow.
Anthropic for code documentation: Excellent for summarizing logic, reviewing context, and structuring technical docs with minimal prompting (still, if you are tight on budget, it might be worth going for one tool that allows switching mid-conversation for different LLMs).
Cursor: Integrated doc suggestions during coding; helpful in-line IDE pairing, yet lacks long term memory but works well when paired with Pieces.
Traditional generators (still valuable)
Notion AI: Great for summarizing Slack threads or meetings into internal team docs.
Doxygen/JSDoc/Sphinx: Solid choices for API references and structured documentation from code comments.
The key difference is that memory-first tools provide quality, grounded knowledge on the process and not just the ‘way things are.’
They document the why along with the what–this allows for documentation (especially internal or inline-code docs) to have more accurate justifications for decisions, along with the history that revolved around the final output.
Best practices on turning context into living documentation
Here’s how forward-thinking teams use Workstream Activity and Long Term Memory to document better, communicate more clearly, and scale knowledge across contributors, codebases, and time.
Make onboarding instant and insightful
Instead of scheduling repetitive knowledge transfer sessions, create workspaces for new team members to share, copy, and search code.
It’ll give newcomers access to the decisions, trade-offs, and evolution of the project, so they learn why things were done a certain way, not just what was done. Paired with GitHub, new hires get direct links to related code and pull requests that mirror the context.
Power asynchronous communication with real data
Replace generic status updates and daily standups with rich summaries generated from actual work.
By the time a feature ships, the documentation already exists in your activity–so instead of staring at a blank doc trying to recall what you did two weeks ago, you can pull in context-rich summaries, refine the structure, and publish.
Regain momentum after breaks or context switching
Documentation shouldn't be something you do after the code is merged. Instead, it should be a part of your review checklist.
If the workstream doesn’t capture the rationale or implications, update it. Use Pieces alongside GitHub so every commit, pull request, or release includes the context it was built with.
Customize time ranges for strategic insights
While LTM-2.5 creates automatic roll-ups in 30-minute increments, you can generate custom summaries across larger time frames – like “This Week” or “Last Sprint.”
These extended views are perfect for design docs, retrospectives, or architectural overviews.
Maintain privacy and precision in what you capture
With Pieces, you can exclude sources you don’t want captured (browsers, chat applications, etc.) and control what gets included and excluded in Pieces’ output. Interactive activity respects your boundaries while still offering full context for work-related activities.
From context to code and how systems remember
The future of code documentation is figuring out ways to expedite its creation and make sure it’s more and more relevant–this means optimizing the process and the output so it has less debt, more depth, and is more easily indexable and maintainable.
AI won't replace documentation. But the right approach to AI code documentation will replace the need to document blindly, manually, or retroactively.
Instead of asking "What did we do?" we'll start asking "What should we do next?"
This shift from retroactive documentation to ambient memory capture changes everything.
Context gets preserved automatically. Decisions stay connected to their reasoning. Knowledge compounds instead of evaporating with each team transition.
The organizations that adopt this approach first will have a significant competitive advantage, since they’ll be writing cleaner documentation, onboarding faster, and making more informed decisions.
Most importantly, they'll create environments where engineering knowledge becomes a strategic asset, not just supporting content that goes stale after every sprint.
