My AI Coding Workflow: Planning, Review, and Verification

Over the past two years, my day-to-day coding workflow has moved from quick AI help toward clearer task definition and verification. In early 2024, I used AI mostly for autocomplete-level tasks such as snippets, error explanations, and small refactors. By 2026, I still write code when needed, but far more of my time goes to defining tasks clearly, checking outputs, and fixing edge cases the tools miss.

2024

I started experimenting with Cline for straightforward tasks such as boilerplate, test scaffolding, and repetitive refactors. I avoided giving it larger tasks for two reasons. First, it was easy to get code that looked fine but was wrong in non-obvious ways. Second, anything non-trivial took too much prompt back-and-forth.

2025

I switched to Claude Code and stopped asking for immediate implementation. Instead, I asked for a plan first and reviewed it.

That improved outcomes, but two problems stayed persistent. Plans were often overbuilt for the actual problem, and the agent would report "done" before handling edge cases. The bottleneck moved from writing code to verification.

What made the workflow more reliable

Three changes made the workflow dependable enough for daily use.

Stronger model reasoning

For my use cases, newer models have been noticeably better at keeping constraints in context across longer tasks.

Better repo context

A lot of bad output came from missing context, not missing capability. I now keep project expectations in CLAUDE.md so every new session starts with the same baseline: architecture preferences, testing requirements, and coding conventions.

A repeatable execution loop

The biggest improvement was switching from one-pass execution to a repeatable loop:

1. Plan
2. Implement
3. Verify with build/lint/tests
4. Reflect and continue if needed

Current workflow

I often split the work into planning and implementation passes. In practice, that means I write a short feature brief with constraints and non-goals, turn that into checkpoints, let the implementation run through the loop per checkpoint, and then review design and risk rather than just syntax.

Example brief:

Add Google OAuth using existing AuthService. Store tokens in Redis, not SQL.

Trade-offs

This workflow is productive, but it has clear costs:

• Cost: Frequent tool calls and retries add up quickly.
• Legacy code friction: Agents struggle when systems rely on undocumented history.
• Personal skill drift: I type less code directly than I used to.
• Attention overhead: Running multiple agents sounds parallel, but review and coordination still funnel through one person. Human attention is limited, and I still don't have a great system for managing that bottleneck consistently.

Where the work moved

The useful part of my work has moved toward clearer requirements, tighter constraints, and stronger review discipline.

Implementation still matters, but I now spend more attention on deciding what to build, writing clearer constraints, and checking whether the result is right.

The biggest open question for me is this: if newer engineers spend less time in low-level debugging, how do they build the judgment needed to review AI-generated changes well?