but.. but they did this in one week

Description and use

Using LLM agents sped up delivery but caused loss of code ownership and debugging skills. The author recommends phase‑wise planning, handover notes, self‑reading errors, and maintaining a fully understood code segment.

Problem

Relying on LLM agents for rapid development leads to loss of developer ownership, debugging skill erosion, and time‑management challenges due to API limits.

Outcome

The AI‑driven workflow delivered the product quickly, but developers lost ownership and debugging proficiency, leading to frustration and reliance on the model for fixes.

Workflow

• 1. Plan work in small, phase‑wise chunks before prompting the agent. 2. Write handover notes after each phase to capture progress and context. 3. Read and understand error traces before asking the agent to fix. 4. Treat debugging as a primary skill, not just code writing. 5. Schedule heavy phases immediately after the agent’s reset time. 6. Keep at least one fully understood part of the codebase (e.g., deployment, DB).

Evidence

make phase wise plans before you write a single prompt. not "build the app"

write handover notes after every phase

read the error yourself first

debugging is the real skill now