daBongo LMS AI Training Courses

Lesson Content

Finding your highest-value use cases first.

Not all development tasks benefit equally from Claude Code. The highest-value use cases tend to share: high repetition or high time cost, well-defined scope, and easily reviewable output. Map your daily tasks:

High value: boilerplate generation, test writing, refactoring existing code to a new pattern, documentation drafting, debugging with clear reproduction steps, codebase navigation on unfamiliar code.

Lower value: creative architecture decisions, performance optimization in complex systems, security-critical logic requiring expert review, novel algorithm design.

Build your daily habit around the high-value cases first.

The daily session habit.

A productive daily Claude Code habit:

• Start each session with a fresh context review (update CLAUDE.md if the current focus has changed)
• Use Code mode for all direct code work – do not stay in Chat mode when you need file edits
• Review every change before staging: no exceptions
• End each multi-step task with a test run before closing the session
• Use Cowork/Agent mode for well-scoped multi-step tasks only (clear goal, defined scope, defined check-in points)

Avoiding the failure modes.

Common Claude Code failure modes:

• Uncommitted understanding: Committing AI-generated code without understanding it – the most common source of AI-assisted technical debt
• Scope creep: Using Cowork mode for tasks with vague goals, allowing Claude to interpret "improve this module" too broadly
• CLAUDE.md neglect: The project context file drifts out of date, causing Claude to work from stale assumptions
• Review atrophy: Reviewing AI changes becomes cursory over time as the developer builds false confidence in Claude's output quality

Each failure mode has a simple remedy: review discipline, scoped requests, CLAUDE.md maintenance, and periodic unassisted coding sessions to maintain craft.

Maintaining craft alongside AI assistance.

A developer who never writes code without AI assistance may find their own problem-solving ability atrophying. Periodic unassisted sessions – where you work on real problems without opening Claude Code – maintain the underlying engineering skill that makes you able to evaluate AI output accurately. AI fluency requires maintaining the expertise to judge AI output. That expertise requires exercise.