Prompt Chaining

Prompt chaining transforms AI from a single-shot tool into a step-by-step collaborator. Complex features become manageable when broken into a chain of focused requests.

Why Chain Prompts?

Single complex prompts often produce:

• Incomplete implementations
• Inconsistent patterns across files
• Hallucinations from overloaded context
• Results that miss important details

Chained prompts produce:

• Focused, high-quality output at each step
• Consistent patterns that build on each other
• Less hallucination per step
• Opportunities to correct course early

Chaining Patterns

Sequential

Each step feeds the next:

Design schema → Build API → Create UI → Add tests

Refinement

Each step improves the previous:

Generate draft → Review for issues → Fix problems → Polish

Expansion

Start simple, add complexity:

Basic component → Add validation → Add error states → Add animations

Effective Chaining Tips

1. Start with the foundation — Data models, types, interfaces
2. Build layer by layer — Schema → API → UI → Tests
3. Reference previous output — "Using the schema from step 1..."
4. Verify each step — Don't chain on broken foundations
5. Keep context — Summarize previous decisions when starting a new chain

When to Chain vs Single Prompt