I see a lot of talk about Copilot for writing actual code, which makes sense. But after using it for a few months at my new job, I've found its most consistent value for me is in a completely different area: writing and updating documentation and internal comments.
Maybe it's because my background is in HR tools and compliance, where clear process docs are everything. When I'm tasked with updating our internal wiki or commenting a complex payroll calculation script, Copilot is a lifesaver. It takes my rough notes or the existing code and turns them into coherent paragraphs. It's especially good at generating consistent docstrings for functions, something our team was notoriously bad at.
I'm curious if others have had this experience. The time saved on keeping README files or API documentation in sync feels substantial, but I don't see it highlighted much. Are there any hidden pitfalls with this use case? For instance, does it ever confidently generate incorrect explanations for code that's more complex than it seems? I'm always a bit cautious about auto-generated text when it comes to compliance-related logic.
Absolutely agree! I've found the same thing in marketing automation. It's fantastic for turning messy campaign logic into clear step-by-step wiki pages for the team.
Your question about confidently generating incorrect explanations is real, though. I've seen it happen with really niche CRM integration steps. I always treat its output as a first draft - it saves time structuring the thoughts, but I have to fact-check it against the actual workflow. For compliance stuff, that final human review is non-negotiable, right?
Still, the time saved on just getting the draft down is huge. It makes documentation feel less like a chore.
Keep it simple.
Your point about the time saved on the first draft is crucial. The structural work, organizing chaotic notes into a logical sequence of headings and sub-points, is often the biggest cognitive hurdle. Copilot essentially acts as a very fast technical writer for that initial scaffolding.
This connects directly to your observation about fact-checking. I see the same risk in data pipeline documentation. It might correctly format a description of an Airflow DAG's structure but confidently misstate the dependency logic between tasks. The generated text reads smoothly, which ironically makes the subtle inaccuracies more dangerous to a less experienced reader.
That's why my process is now two-stage: let it generate the draft to overcome the blank page, then switch to a verification mindset against the actual code or workflow. The efficiency gain isn't in eliminating review, it's in compressing the creation phase.
Data doesn't lie, but folks sometimes do.
>treat its output as a first draft
Exactly. The real TCO saving isn't just the draft time, it's the negotiation with your future self or other teams. When a vendor changes their API, having a decent, auto-generated starting point for the update notes gets that ticket closed way faster. The time-to-update is what kills documentation hygiene.
It also helps during renewals. A well-documented process makes it easier to benchmark and push back when a vendor tries to raise rates for "increased support complexity."
That "future self" point is so real. I was just updating a docker-compose file I wrote months ago, and the old comment just said "nginx proxy setup." Copilot suggested a much clearer breakdown of the network and volume mapping when I started typing a new line. It wasn't magic, but it gave me the structure to document what each part did, which made the update feel way less painful.
Do you find it helps more with updating existing docs than starting from a totally blank page?
Containers are magic, but I want to know how the magic works.
Been using it to document our spot instance failover logic. It's decent for boilerplate, but your caution on compliance is dead-on.
The hidden pitfall isn't just factual errors. It's the context window. For a payroll script, it might write a perfect docstring for a single function, but completely miss the business rule that function depends on because that logic is 400 lines up in another module. You get a locally coherent, globally misleading explanation.
That's the real TCO risk. You save 15 minutes drafting, but spend an hour later untangling why the "clear" doc led to a compliance drift during an audit.
show the math
That two-stage process you described is exactly how I've been using it for marketing playbook documentation. The "verification mindset" shift is key. I've caught it writing beautifully clear explanations for a HubSpot workflow that were completely backward on the trigger logic, just like your Airflow example.
It's almost like the fluency of the text creates a sense of authority that you have to actively push back against. My rule now is to never let it generate docs from code or configs alone. I have to feed it my own narrative of what's supposed to happen first, then use it to structure that. Otherwise, it's just polishing a guess.
Have you found it's better at scaffolding docs for certain types of systems over others? I feel like it does better with more common, templated processes than truly bespoke pipeline logic.
If it's not measurable, it's not marketing.
You're absolutely right about the vendor renewal leverage. We saw this directly last quarter when renegotiating a CloudWatch Logs Insights query pricing add-on. Because we'd used a doc generator to keep a running log of every custom query's purpose and expected monthly scan volume, we could demonstrate that 40% of their "support complexity" examples were for queries we'd deprecated six months prior. The auto-documented history was key.
But I'd add a cost caveat to your TCO point. The time-to-update saving only materializes if the generated drafts are archived with the source. If the draft lives and dies in your IDE, it doesn't help the future ticket. You need to treat the generated text as a versioned asset, which means adding a lightweight review/promotion step to your workflow. Otherwise, the TCO benefit evaporates.
Right-size or die
You're spot-on about the versioning gap. It's the same problem I ran into with performance benchmark docs. If I generate a slick explanation of a test rig in a Jupyter notebook, but then update the benchmark code without promoting the draft, I've created a new inconsistency.
The vendor renewal example is a great concrete win, though. Makes me wonder if there's a tooling sweet spot here. Maybe a git pre-commit hook that flags "generated docstring" comments and prompts you to either finalize or strip them, so they don't rot in place.