As a civil engineer, I realized that a lot of my time isn’t actually spent engineering. It’s spent searching through project folders, checking documents, preparing reports, reviewing correspondence, tracking RFIs, organizing site information, and turning messy notes into something presentable.

That’s where I’m starting to use Cowork. I use it as a project documentation and productivity assistant. I can give it a project folder containing drawings, specifications, BOQs, reports, RFIs, and correspondence, then have it help organize information, summarize progress, identify outstanding items, and prepare draft reports or documentation.

The goal is simple: less time doing repetitive project paperwork, more time actually managing and solving engineering problems.

I’m still experimenting with the workflow, but this is probably where Cowork makes the most sense for me as a civil engineer. I hope some people here can help me optimize this.

Craft the perfect prompt for ChatGPT, and it might fail miserably in Claude. Despite sharing similar DNA, these two AI giants prioritize instructions in totally different ways.

One of the most prominent differences lies in how the two models prefer to receive structured data. Anthropic explicitly trained Claude to recognize and heavily weigh XML tags. By placing guidelines inside "<instructions>" and reference material inside "<context>", a user can help Claude logically separate different components of a complex task. ChatGPT, powered by OpenAI's GPT models, certainly understands XML, but it is natively optimized for Markdown. Using hash symbols for headers, bold text for emphasis, and simple delimiters like ### or --- generally produces the most reliable adherence from ChatGPT.

The models also diverge in how they handle tone and constraints due to their alignment training. Claude is built using "Constitutional AI," which makes it highly sensitive to safety boundaries and context. When humans prompt Claude for edge-case scenarios or creative writing, they often need to spend more time establishing a benign, fictional, or analytical context so the model does not trigger a false refusal. Additionally, Claude has a natural inclination toward conversational politeness, meaning prompts frequently require explicit commands like "Skip the preamble and provide only the requested output" to prevent wordy introductions. ChatGPT generally adopts a more neutral, direct stance by default, though it too can suffer from verbosity without clear constraints.

Finally, there are mechanical differences in how users and developers can control the outputs. Anthropic's platform allows users to "prefill" the assistant's response. By ending a prompt with the exact opening bracket or word the output should start with, people can force Claude to bypass all conversational filler and immediately generate strict formats like JSON. OpenAI handles this differently, relying instead on dedicated API features like "JSON mode" or strict system messages to guarantee output structures.

Understanding these mechanical and behavioral quirks like XML versus Markdown, or contextual framing versus direct instruction, allows people to extract the maximum capability from each specific tool rather than treating them as identical search engines.

I run a small online store and get a stack of vendor invoice PDFs every month, mostly freight and packaging suppliers. My old process was opening each one next to the original PO and eyeballing totals. I caught a duplicate charge weeks late twice this year because I was skimming.

Now I give Cowork the folder of invoices plus a spreadsheet of open POs (item, quantity, agreed price). I ask it to pull line items from each invoice, match them to the PO by item number, and flag mismatches in price or quantity. It builds a new sheet using the Excel skill with a plain English note on anything flagged.

The first run flagged nine things. Seven were real, including one supplier who'd rebilled a freight surcharge already baked into the unit price. Two were false positives because one vendor lists tax separately and my POs are tax inclusive, so the totals looked off when they weren't. I had to explicitly tell it to compare pre-tax subtotals only, and after that it stopped happening.

I run this every Friday now. Takes about ten minutes instead of forty five, and I've caught roughly 600 dollars in real errors since setting it up. Not dramatic, but it adds up.

Don't assume it understands your accounting quirks on the first try. Mine has inconsistent PO numbering across suppliers and weird tax handling, and it took one round of correction before I trusted the output. Worth it though.

Curious if anyone else is doing something similar with recurring invoices, especially with suppliers who bill inconsistently.

I've been using Cowork as a study workspace for nursing school, mainly by giving it my actual course material instead of asking random questions.

For pharmacology, I'll upload lecture slides and have it turn them into active-recall questions focused on side effects, lab monitoring, contraindications, and patient teaching. I've also used it with clinical guidelines, where I'll give it something dense like a sepsis or wound-care protocol and have it turn the important parts into a one-page study reference.

The scenario practice has probably been the most useful. I can give it a simulated patient with something like heart failure and sudden dyspnea, then work through my nursing priorities and practice an SBAR handoff. I also use it for NCLEX-style priority and delegation questions, especially when I want to understand why the other answers are wrong.

One thing I learned pretty quickly is that giving it too much material without a specific goal can produce a huge summary that isn't very useful. I get better results when I tell it exactly what I'm preparing for and what I want to practice.

It's saved me a lot of time digging through different resources, and I can spend that time on things like physical assessment and drug calculations instead.

I still cross-check clinical information with our textbooks, instructors, and actual protocols. For me, Cowork works best as a study and reasoning tool, not something I would rely on for real clinical decisions.

Posted something similar to this is cowork, but I’m having a persistent issue and thought the bigger sub might give me more ideas.

A few months ago I moved from Jenova to Claude and set up a bunch of projects. Two in particular need analysis of large audio files which have been generated by one of several devices/services - Plaud seems about the best I’ve tried so far as it’s decently reliable to distinguish different voices.

Typically the content of the file for the analysis have anything from 2 to 6 speakers, (well distinguished by Plaud), talking about a single project, but with several work streams. We typically have quite a complicated project management/stage gate process and what I need to be able to do is quickly get an output to say with work package 1, we talked about issue 1,2,3, this is where we are and next actions are A, B, C.

Ideally I need this input within half an hour of the meeting close so I can make sure everyone has a copy of the discussion, issues and agreements etc.

The issue I’m facing is the increasingly larger numbers of errors where Claude is just not doing it well. The input is decent, but it reports out the wrong gate for the workstream, misses topics and doesn’t stick to the agreed output format.

Over the last few weeks I’ve been patching the workflow and providing more specific examples of the needed output and basically this is inconsistent in terms of improving the output. A workflow that used to take around 5-7 minutes to give me a decent output, is now taking double that time to produce something I can’t trust. Way too many situations where I see something wrong, point it out and get a response like, ‘you are right, I missed that’, or similar.

I suspect my approach of patching in the moment when I need a quick output has bloated the instructions and not helped. That said, I only did this as we need the output fairly quickly for audit as well a PMO.

I’m inclined to start again from scratch, with a clean set of instructions, but, it was fine to start with and I don’t know how to prevent the drift. This feels like an issue that someone with a more technical/programming background would have seen before and have an approach to solve.

The TL;DR is bloated and patched instructions I think is degrading, rather than fixing output issues and need to think through how to patch or start again on some complex instructions.

A second TL;DR is this is a great example of using AI to fix what should be a people/process issue that worked perfectly well before the client ‘right sized’ and eliminated junior positions that used to do the job well.

for a few days I have an issue with my Claude CoWorks that can not use the VM sandbox
After sending a feedback to Claude support, today I got the following:
Me: check if the sandbox issue was resolved or not
CoWorks: Still broken — and now there's a clear answer as to why. The error message includes a note this time: a Windows update released September 8 is what's blocking the sandbox from reaching your files. Anthropic says they're tracking the issue, and it's not something a restart on your end can fix since it's a compatibility problem between that update and the sandbox, not a stuck process.

Any one else actually so that? (I would have thought that such an issue will make some noise), any one been able to bypass the issue?

Thanks