llm/065c6e83-d0d5-4aca-be3d-92768a8a3506/topic-19-445f1f14-7ab1-4c85-8c2f-9c1ccbc31a8b-input.json
The following is content for you to summarize. Do not respond to the comments—summarize them. <topic> Documentation as Side Benefit # Plans and research documents serve as valuable documentation for future maintainers. Version controlling plan files in git. Using plans to understand architectural decisions later. </topic> <comments_about_topic> 1. I’ve also found that a bigger focus on expanding my agents.md as the project rolls on has led to less headaches overall and more consistency (non-surprisingly). It’s the same as asking juniors to reflect on the work they’ve completed and to document important things that can help them in the future. Software Manger is a good way to put this. 2. AGENTS.md should mostly point to real documentation and design files that humans will also read and keep up to date. It's rare that something about a project is only of interest to AI agents. 3. I really like your analogy of LLMs as 'unreliable interns'. The shift from being a 'coder' to a 'software manager' who enforces documentation and grounding is the only way to scale these tools. Without an architecture.md or similar grounding, the context drift eventually makes the AI-generated code a liability rather than an asset. It's about moving the complexity from the syntax to the specification. 4. I think the real value here isn’t “planning vs not planning,” it’s forcing the model to surface its assumptions before they harden into code. LLMs don’t usually fail at syntax. They fail at invisible assumptions about architecture, constraints, invariants, etc. A written plan becomes a debugging surface for those assumptions. 5. if you don't plan perfectly, you'll have to start over from scratch if anything goes wrong This is my experience too, but it's pushed me to make much smaller plans and to commit things to a feature branch far more atomically so I can revert a step to the previous commit, or bin the entire feature by going back to main. I do this far more now than I ever did when I was writing the code by hand. This is how developers should work regardless of how the code is being developed. I think this is a small but very real way AI has actually made me a better developer (unless I stop doing it when I don't use AI... not tried that yet.) 6. That's not (or should not be what's happening). They write a short high level plan (let's say 200 words). The plan asks the agent to write a more detailed implementation plan (written by the LLM, let's say 2000-5000 words). They read this plan and adjust as needed, even sending it to the agent for re-dos. Once the implementation plan is done, they ask the agent to write the actual code changes. Then they review that and ask for fixes, adjustments, etc. This can be comparable to writing the code yourself but also leaves a detailed trail of what was done and why, which I basically NEVER see in human generated code. That alone is worth gold, by itself. And on top of that, if you're using an unknown platform or stack, it's basically a rocket ship. You bootstrap much faster. Of course, stay on top of the architecture, do controlled changes, learn about the platform as you go, etc. 7. I've been running AI coding workshops for engineers transitioning from traditional development, and the research phase is consistently the part people skip — and the part that makes or breaks everything. The failure mode the author describes (implementations that work in isolation but break the surrounding system) is exactly what I see in workshop after workshop. Engineers prompt the LLM with "add pagination to the list endpoint" and get working code that ignores the existing query builder patterns, duplicates filtering logic, or misses the caching layer entirely. What I tell people: the research.md isn't busywork, it's your verification that the LLM actually understands the system it's about to modify. If you can't confirm the research is accurate, you have no business trusting the plan. One thing I'd add to the author's workflow: I've found it helpful to have the LLM explicitly list what it does NOT know or is uncertain about after the research phase. This surfaces blind spots before they become bugs buried three abstraction layers deep. 8. I find “maintainable code” the hardest bias to let go of. 15+ years of coding and design patterns are hard to let go. But the aha moment for me was what’s maintainable by AI vs by me by hand are on different realms. So maintainable has to evolve from good human design patterns to good AI patterns. Specs are worth it IMO. Not because if I can spec, I could’ve coded anyway. But because I gain all the insight and capabilities of AI, while minimizing the gotchas and edge failures. 9. I use Claude Code for lecture prep. I craft a detailed and ordered set of lecture notes in a Quarto file and then have a dedicated claude code skill for translating those notes into Slidev slides, in the style that I like. Once that's done, much like the author, I go through the slides and make commented annotations like "this should be broken into two slides" or "this should be a side-by-side" or "use your generate clipart skill to throw an image here alongside these bullets" and "pull in the code example from ../examples/foo." It works brilliantly. And then I do one final pass of tweaking after that's done. But yeah, annotations are super powerful. Token distance in-context and all that jazz. 10. The multi-pass approach works outside of code too. I run a fairly complex automation pipeline (prompt -> script -> images -> audio -> video assembly) and the single biggest quality improvement was splitting generation into discrete planning and execution phases. One-shotting a 10-step pipeline means errors compound. Having the LLM first produce a structured plan, then executing each step against that plan with validation gates between them, cut my failure rate from maybe 40% to under 10%. The planning doc also becomes a reusable artifact you can iterate on without re-running everything. 11. Whenever I build a new feature with it I end up with several plan files leftover. I ask CC to combine them all, update with what we actually ended up building and name it something sensible, then whenever I want to work on that area again it's a useful reference (including the architecture, decisions and tradeoffs, relevant files etc). 12. This is the flow I've found myself working towards. Essentially maintaining more and more layered documentation for the LLM produces better and more consistent results. What is great here is the emphasis on the use of such documents in the planning phase. I'm feeling much more motivated to write solid documentation recently, because I know someone (the LLM) is actually going to read it! I've noticed my efforts and skill acquisition have moved sharply from app developer towards DevOps and architecture / management, but I think I'll always be grateful for the application engineering experience that I think the next wave of devs might miss out on. I've also noted such a huge gulf between some developers describing 'prompting things into existence' and the approach described in this article. Both types seem to report success, though my experience is that the latter seems more realistic, and much more likely to produce robust code that's likely to be maintainable for long term or project critical goals. 13. I do something very similar, also with Claude and Codex, because the workflow is controlled by me, not by the tool. But instead of plan.md I use a ticket system basically like ticket_<number>_<slug>.md where I let the agent create the ticket from a chat, correct and annotate it afterwards and send it back, sometimes to a new agent instance. This workflow helps me keeping track of what has been done over time in the projects I work on. Also this approach does not need any „real“ ticket system tooling/mcp/skill/whatever since it works purely on text files. 14. I've been teaching AI coding tool workshops for the past year and this planning-first approach is by far the most reliable pattern I've seen across skill levels. The key insight that most people miss: this isn't a new workflow invented for AI - it's how good senior engineers already work. You read the code deeply, write a design doc, get buy-in, then implement. The AI just makes the implementation phase dramatically faster. What I've found interesting is that the people who struggle most with AI coding tools are often junior devs who never developed the habit of planning before coding. They jump straight to "build me X" and get frustrated when the output is a mess. Meanwhile, engineers with 10+ years of experience who are used to writing design docs and reviewing code pick it up almost instantly - because the hard part was always the planning, not the typing. One addition I'd make to this workflow: version your research.md and plan.md files in git alongside your code. They become incredibly valuable documentation for future maintainers (including future-you) trying to understand why certain architectural decisions were made. 15. This is similar to what I do. I instruct an Architect mode with a set of rules related to phased implementation and detailed code artifacts output to a report.md file. After a couple of rounds of review and usually some responses that either tie together behaviors across context, critique poor choices or correct assumptions, there is a piece of work defined for a coder LLM to perform. With the new Opus 4.6 I then select specialist agents to review the report.md, prompted with detailed insight into particular areas of the software. The feedback from these specialist agent reviews is often very good and sometimes catches things I had missed. Once all of this is done, I let the agent make the changes and move onto doing something else. I typically rename and commit the report.md files which can be useful as an alternative to git diff / commit messages etc. 16. The separation of planning and execution resonates strongly. I've been using a similar pattern when building with AI APIs — write the spec/plan in natural language first, then let the model execute against it. One addition that's worked well for me: keeping a persistent context file that the model reads at the start of each session. Instead of re-explaining the project every time, you maintain a living document of decisions, constraints, and current state. Turns each session into a continuation rather than a cold start. The biggest productivity gain isn't in the code generation itself — it's in reducing the re-orientation overhead between sessions. 17. > Most developers type a prompt, sometimes use plan mode, fix the errors, repeat. > ... > never let Claude write code until you’ve reviewed and approved a written plan I certainly always work towards an approved plan before I let it lost on changing the code. I just assumed most people did, honestly. Admittedly, sometimes there's "phases" to the implementation (because some parts can be figured out later and it's more important to get the key parts up and running first), but each phase gets a full, reviewed plan before I tell it to go. In fact, I just finished writing a command and instruction to tell claude that, when it presents a plan for implementation, offer me another option; to write out the current (important parts of the) context and the full plan to individual (ticket specific) md files. That way, if something goes wrong with the implementation I can tell it to read those files and "start from where they left off" in the planning. 18. I’m a big fan of having the model create a GitHub issue directly (using the GH CLI) with the exact plan it generates, instead of creating a markdown file that will eventually get deleted. It gives me a permanent record and makes it easy to reference and close the issue once the PR is ready. 19. Cool, the idea of leaving comments directly in the plan never even occurred to me, even though it really is the obvious thing to do. Do you markup and then save your comments in any way, and have you tried keeping them so you can review the rules and requirements later? 20. It seems like the annotation of plan files is the key step. Claude Code now creates persistent markdown plan files in ~/.claude/plans/ and you can open them with Ctrl-G to annotate them in your default editor. So plan mode is not ephemeral any more. 21. Hub and spoke documentation in planning has been absolutely essential for the way my planning was before, and it's pretty cool seeing it work so well for planning mode to build scaffolds and routing. 22. last i checked, you can't annotate inline with planning mode. you have to type a lot to explain precisely what needs to change, and then it re-presents you with a plan (which may or may not have changed something else). i like the idea of having an actual document because you could actually compare the before and after versions if you wanted to confirm things changed as intended when you gave feedback </comments_about_topic> Write a concise, engaging paragraph (3-5 sentences) summarizing the key points and perspectives in these comments about the topic. Focus on the most interesting viewpoints. Do not use bullet points—write flowing prose.
Documentation as Side Benefit # Plans and research documents serve as valuable documentation for future maintainers. Version controlling plan files in git. Using plans to understand architectural decisions later.
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