Where AI tools are really helpful...
1. Speeding up repetitive work
Great for writing testing helpers and small utilities.
Saves a lot of time on boilerplate and setup.
Can also help debug issues while generating that code.
2. Quick fixes without context switching
Super useful for small UI bugs or minor fixes.
I can trigger it directly from Slack, review the code, and test it in a preview branch.
No need to set up my local environment for tiny changes.
Works best when I already know roughly where the fix should go and give it some direction (files, components, etc.).
3. Executing on planned work
Once a task is well-defined, it can do a big chunk of the implementation.
Even if it's not perfect, it accelerates the initial draft significantly.
Still requires careful review and often follow-up prompts.
4. Iterative problem solving (especially with poor documentation)
Very useful when working with poorly documented third-party tools.
I've used it in a loop: run → check errors → adjust → repeat.
Helps automate trial-and-error cycles using logs/output.
5. Research assistance
Good at scanning docs/SDKs and surfacing relevant info.
I always verify, but it saves a lot of time getting to the right place.
Especially valuable when it points to sources or links directly.
Struggles & frustrations
1. Planning can be hit-or-miss
Sometimes gets key assumptions wrong early.
Fixing that can take a lot of back-and-forth.
By the time it's corrected, I may lose my original train of thought.
Works better for simpler problems than complex planning.
2. Can produce confidently wrong solutions
Occasionally generates code that looks correct but is fundamentally flawed.
Example: created an infinite loop when processing batches because it never updated the offset.
Raises the question of how detailed prompts need to be (sometimes feels like writing pseudocode is required).
3. Requires active steering
Often needs re-prompting due to odd or incorrect decisions.
Not fully reliable as a "one-shot" solution generator.
4. Gets stuck on bad ideas
Sometimes "locks into" an incorrect approach and keeps circling back to it.
At that point, it's often faster to start a new chat than to recover the thread.
Observations / Best Practices
Works best with context: Giving hints (files, expected behavior, constraints) improves results significantly.
Treat outputs as drafts, not final code: Always review carefully.
Use it more for execution than planning: It shines when the problem is already well-defined.
Iterative workflows are a strong use case: Especially debugging, integrations, or unknown systems.
Know when to reset: If it gets stuck, starting fresh is often more efficient.
Overall Take
It's definitely a productivity boost and helps move faster, especially for:
Boilerplate
Small fixes
Research
Iterative debugging
That said, it's not something you can fully trust or delegate end-to-end. It still requires strong guidance, validation, and occasional resets when it goes off track."
