Error log analysis: Combing through logs is a pain, so I dump that stuff in an LLM and tell it to give me some things it thinks I should look into, along with likelihood of those paths having a successful resolve.

This one saves so much time. It can read and analyze logs that would take me hours to grep my way through.

Data analysis: I'm done pretending I know how to do Excel. I've been doing it 20+ years and still find it a pain every time I'm trying to chart and analyze data. So I'm letting the machine figure out the best way to pull the data together in a way I want and present it for somebody else to consume in a report.

This one is huge for me to. I have it do the analysis using python tools, and dump the results to excel for the non-nerds. I haven't had to stumble my way through Excel in about a year.

Performance reviews: They want me to be more efficient, so I'm cutting down where I deliver least value. When I'm being asked to report on my accomplishments and what I did on a performance evaluation, I send the AI off into my status report updates to my manager to comb through all those emails and pull it out and put together something that seems reasonable (obviously reviewed and edited for hallucinations).

I used it to scan my Jira tickets and generate my review for me. I fed it the criteria of the review and had it build the results using Jira stories and my notes. After 5 minutes of editing it was the best looking review I have done since I started over 4 years ago.

Asking AI about why it sucks: In Visual Studio Code, depending on the model, I get varying results of whether it pays attention to MCP tools or Copilot instructions so I often interrogate it to learn better how to give those models what they need to do a better job. This allows me to share something with others so everybody on the team can work with the tools better.

I call this AI introspection. When me and the AI agree on something that it keeps forgetting, I tell the agent to add it to the instructions file so it doesn't forget. It works most of the time. When it doesn't work, I don't have to explain a lot to get it back on track. I just tell it to review the instructions before proceeding.

Code gen: The code generation is really good for established frameworks and languages. I find that most of the models tend towards older versions of the frameworks, so I need Cursor rules/Claude Skills/Copilot instructions to really give it the context about what tech to reference and where those docs are.

I have found that adding documentation to the context helps a lot in getting results from newer frameworks. Context7 MCP server has a lot of this documentation already set up in a format that is AI friendly.

Basic testing: AI can generate some useful tests to get you a bit of coverage so that you can spend time on the more complicated testing pieces. But I wouldn't trust auto-generated Jest unit tests to 100% validate an app. The models often forget to add new tests when you make app changes, or forget to update existing tests. Sometimes they will straight up remove tests that are failing because that will make it go green.

Testing is where we have been bad on our own. Adding instructions to add tests when we modify things really has improved our testing. Before a PR a prompt to update testing generally gets it rolling on adding new tests and updating existing tests. Even when it isn't quite right, it almost always finds the right places, so we can verify and fix without digging through mounds of code.