Complete Workflow: From Feature Idea to Implementation

• Jader Correa

Complete Workflow: From Feature Idea to Implementation

Learn how to use Chuchu’s full workflow to go from a feature idea to working, tested code.

The Three-Phase Workflow

Chuchu provides a structured approach to feature development:

Phase 1: Research → Understand your codebase
Phase 2: Plan → Create detailed implementation steps
Phase 3: Implement → Execute with verification (interactive or autonomous)

Why This Workflow?

Traditional AI coding assistants give you code immediately. Sometimes that works. Often it doesn’t because:

❌ No context about your codebase
❌ No understanding of existing patterns
❌ No incremental verification
❌ No way to course-correct

Chuchu’s workflow1 solves this:

✅ Research phase builds context
✅ Planning ensures coherent approach2
✅ Implementation is incremental and verified
✅ You control the pace (interactive or autonomous)

Quick Example

Let’s add a “password reset” feature:

1. Research

chu research "How is user authentication currently implemented?"

Chuchu will:

  • Search your codebase semantically
  • Read and analyze relevant files
  • Document current architecture
  • Save findings to ~/.chuchu/research/

2. Plan

chu plan "Add password reset feature with email verification"

Chuchu creates a detailed plan:

  • Phase 1: Database changes (migration, columns)
  • Phase 2: Email service (templates, sending)
  • Phase 3: API endpoints (routes, validation)
  • Phase 4: Tests (unit, integration, e2e)

Plan saved to ~/.chuchu/plans/2025-01-23-password-reset.md

3. Implement (Interactive)

chu implement ~/.chuchu/plans/2025-01-23-password-reset.md

Walk through each phase:

─── Step 1/4: Database changes ───

Add reset_token and token_expiry columns to users table.
Create migration file...

Execute this step? [Y/n/q]: Y
✓ Step completed

─── Step 2/4: Email service ───
...

Or Implement (Autonomous):

chu implement ~/.chuchu/plans/2025-01-23-password-reset.md --auto

Chuchu executes everything:

  • Runs each step
  • Verifies with build + tests
  • Retries on errors
  • Creates checkpoints
  • Completes autonomously

Interactive vs Autonomous: When to Use Each

Interactive Mode (Default)

Use when:

  • 🎓 Learning unfamiliar codebase
  • 🔒 Making sensitive/production changes
  • 🤔 You want to understand each step
  • 👀 Need to review before proceeding

Pros:

  • Full control over execution
  • See what changes before they happen
  • Skip or quit at any point
  • Learn as you go

Cons:

  • Slower (requires manual confirmation)
  • Can’t walk away
  • More active attention needed

Autonomous Mode (–auto)

Use when:

  • ✅ Plan is well-defined and reviewed
  • 🚀 Want fast iteration
  • 🤖 Trust your AI agent configuration
  • 📦 Batch processing multiple features

Pros:

  • Fully automated execution
  • Verification at each step
  • Error recovery with retry
  • Checkpoint/resume support

Cons:

  • Less visibility during execution
  • Need to review changes after
  • Requires good plan quality

Real-World Tips

For Best Results

  1. Always start with research for unfamiliar areas
  2. Review and edit plans before implementing
  3. Use interactive mode first, then autonomous for iterations
  4. Review with git diff after autonomous runs
  5. Commit incrementally (one phase at a time is fine)

When Plans Fail

Interactive mode:

  • Quit, edit plan, restart
  • Or continue anyway and fix manually

Autonomous mode:

  • Automatic retry (3x default)
  • Rollback to last checkpoint on failure
  • Edit plan and --resume from checkpoint

Good Plan Characteristics

✅ Clear, single-responsibility steps
✅ Specific file paths mentioned
✅ Test requirements for each phase
✅ Incremental, verifiable changes

❌ Vague “implement feature X”
❌ Too many changes in one step
❌ No verification criteria

Neovim Integration

All three phases work from Neovim:

" Phase 1: Research
<C-d>  " Open chat
> research: How does authentication work?

" Phase 2: Plan  
> plan: Add password reset feature

" Phase 3: Implement (autonomous)
:ChuchuAuto
" Or: <leader>ca

Cost Optimization

Using the full workflow actually saves money:

  1. Research (one-time): ~10-50k tokens
  2. Plan (one-time): ~20-100k tokens
  3. Implement (verified): ~100-500k tokens

vs. Direct coding without context:

  • Multiple failed attempts
  • Back-and-forth corrections
  • Wasted tokens on wrong approaches
  • Final cost: often 2-5x higher

Example costs with Groq:

  • Research: $0.01-0.05
  • Plan: $0.02-0.10
  • Implement: $0.10-0.50
  • Total: $0.13-0.65 per feature

Compare to 10+ coding attempts without planning: $1-3+ easily.

Language Support

Research & Plan: Works with any language (language-agnostic)

Implement verification:

  • ✅ Go
  • ✅ TypeScript/JavaScript
  • ✅ Python
  • ✅ Elixir
  • ✅ Ruby

Implementation itself works for any language (LLM-based), but build/test verification is language-specific.

Try It Yourself

  1. Pick a small feature to implement
  2. Start with: chu research "How does X work?"
  3. Create plan: chu plan "Add Y feature"
  4. Implement interactively: chu implement <plan>
  5. Review results, iterate if needed
  6. Next time: use --auto for speed

See Also

References

Questions or issues? Open an issue on GitHub

  1. Beck, K. (2003). Test-Driven Development: By Example. Addison-Wesley Professional. ISBN: 978-0321146533 ↩

  2. Fowler, M. (2018). Refactoring: Improving the Design of Existing Code (2nd ed.). Addison-Wesley Professional. ISBN: 978-0134757599 ↩