Prompt Chaining & Workflows
Combine multiple AI prompts into effective workflows for complex tasks using prompt chaining, branching, and validation. What is Prompt Chaining? Prompt chaining breaks complex tasks into smaller steps, each handled by a separate prompt. The output of one step becomes input for the next. This improves accuracy because each prompt has a focused objective, and errors can be caught at each stage. Chain Structure A typical chain: 1) Analyze input (extract key information), 2) Generate outline (structure the response), 3) Write content (fill in details), 4) Review and refine (check quality). Each step has its own instructions and validation. Example: Writing an article → Research → Outline → Draft → Edit → Format. Branching & Conditional Logic Branching chains evaluate the output at a decision point and route to different paths. If analysis detects positive sentiment → generate positive response. If negative → generate escalation workflow. This enables sophisticated AI applications that adapt to input. Use structured output (JSON) at decision points for reliable routing. Validation & Quality Control Add validation steps between chain links: check output format, verify required fields, test against criteria. If validation fails, loop back to regenerate or flag for human review. Validation prompts should be strict — they act as quality gates. This dramatically improves reliability of multi-step AI workflows.