Chaining Prompts: Get AI to Write Entire Features in One Session
I tested this myself. Here is the honest take.
How do you chain prompts for complex features?
Watch the workflow: See prompt chaining in action with a real coding example.
Chain prompts by breaking a feature into sequential steps. First prompt: architecture plan. Second: data model. Third: API routes. Fourth: UI components. Fifth: tests. Each prompt builds on the previous output. I used this to build a full Stripe integration in 5 chained prompts — 45 minutes total.
What is the best prompt order for a full feature?
Best prompt order: 1) “Plan the architecture for [feature].” 2) “Write the data model.” 3) “Write the API routes based on this model.” 4) “Write the UI components that call these routes.” 5) “Write integration tests.” Each prompt uses the previous output as context.
When should you not chain prompts?
Do not chain prompts when the feature is simple enough for one prompt. My rule: if the feature touches more than 3 files, chain prompts. If it is one file, write one prompt. Chaining adds about 50% more tokens. Only use it when the complexity justifies the cost.
Chain prompts for multi-file features: plan, model, routes, UI, tests. Each builds on previous. Complex features in 5 prompts = 45 minutes. Simple features = one prompt.
Prompt chaining is the most underused technique in vibe coding. Most people write one giant prompt and get garbage. Breaking into 5 small prompts gives 85% first-pass success vs 40% for one big prompt. The total tokens are the same. The quality difference is enormous.
I need to [task]. Give me the exact steps and common mistakes.
References
- How to write prompts — Vibe Coding Channel
- Write specs not prompts
- AI context window tips
- Prompt engineering
- AI coding prompt templates
How do you verify each step before chaining the next prompt?
Chain validation prevents error accumulation. After each step in the chain, review the output before proceeding. Check: does the generated code match the specification? Are there TODO comments where you expected complete implementation? Does the architecture still make sense? If something looks wrong, fix it before moving to the next step. Fixing errors after a full chain completes means undoing multiple layers of dependent code. A 2-minute verification at each step saves 30+ minutes of debugging later.
What is the optimal number of chained prompts for a feature?
I’ve found that 4-7 prompts per feature is the sweet spot. Fewer than 4 means each prompt is too broad and the AI produces shallow code. More than 7 means you’re over-engineering — the feature could have been done in fewer steps. My rule: count the distinct conceptual pieces. Database model = 1 prompt. API routes = 1 prompt. UI component = 1 prompt. Integration tests = 1 prompt. That’s 4 prompts for a complete CRUD feature. If you need more, break it into sub-features.
How do you pass context between chained prompts?
Each prompt in the chain needs enough context to continue without losing direction. Include: (1) a summary of what was completed in previous steps, (2) the exact file paths being modified, (3) any constraints from earlier decisions. Example: “In the previous step, I created a User model with fields: id, email, name, createdAt. Now write the API routes for CRUD operations. Use the User model from /models/user.ts. Follow the same pattern as the existing Post routes.” This keeps the AI aligned across the chain.
What are advanced chaining patterns for complex features?
For complex features, use parallel chaining: run two chains simultaneously for independent parts. For example, build the backend API and frontend UI in parallel, then merge. Another pattern: the review chain — after generating code, add a final prompt specifically for code review: “Review all changes from steps 1-5. Identify security issues, performance bottlenecks, and style inconsistencies. Suggest improvements.” This catches problems before they reach production. The review step typically adds 10-15% more code but significantly improves quality.
How do you handle dependencies between chained prompt outputs?
When one step’s output becomes another step’s input, always verify the contract matches. After generating a data model, confirm its field names before writing API routes that reference those fields. After generating API routes, check their response format before building UI components that consume them. Use a simple checklist: does the output of step N match the input requirements of step N+1? If not, adjust either the previous output or the next prompt to align them. This prevents the common problem where the AI generates mismatched interfaces.
What rollback strategies work when a chain goes wrong?
If a prompt chain produces broken code, don’t try to fix everything at once. Identify which step introduced the error, then regenerate only that step and subsequent steps. Use version control: commit after each successful step so you can roll back to the last known good state. If you’ve made 5 changes and step 3 broke things, revert to the step 2 commit, regenerate steps 3-5 with corrected prompts. This targeted approach is faster than debugging all 5 steps simultaneously.
How do you document prompt chains for future reuse?
Save successful prompt chains as templates. After completing a feature, document the exact prompts used, their order, and the outputs they produced. Store this in a PROMPTS.md file in your project root. Include: the feature description, each prompt in sequence, file paths modified, and any gotchas encountered. Next time you need a similar feature, start from the template and adjust parameters. Over time this becomes a powerful personal knowledge base that accelerates development significantly. I now have over 50 saved prompt chains covering common patterns like CRUD operations, authentication flows, and payment integrations.
What are the most effective prompt chain templates?
Three templates I use constantly. Template A (CRUD feature): 1) Define data model → 2) Create API routes → 3) Build UI components → 4) Add validation → 5) Write tests. Template B (Bug fix): 1) Describe bug with reproduction steps → 2) Identify root cause → 3) Implement fix → 4) Add regression test → 5) Verify no side effects. Template C (New feature): 1) Define user story and acceptance criteria → 2) Design architecture → 3) Implement core logic → 4) Build UI → 5) Integrate and test. Each template follows a consistent pattern that the AI learns to follow, reducing the need for extensive instructions in each step.
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