Chaining workflows

Core Concept

• Prompt chaining breaks down one large task into a series of smaller, sequential subtasks
• Each subtask is sent as a separate request to Claude, rather than combining everything into one large prompt
• Allows Claude to focus on one task at a time for better quality outputs

Example Workflow: Social Media Video Generation

• Search for trending topics on Twitter based on user input
• Ask Claude to select the most interesting topic from the list
• Request Claude perform web research on the selected topic
• Ask Claude write a script for a short-format video
• Generate video using AI avatar and text-to-speech
• Post video to social media

Why Prompt Chaining Matters

• Handles constraint violations: When a single large prompt has many constraints (e. g. , "don't mention you're an AI," "avoid emojis," "use professional tone"), Claude may still violate some of them
• Improves focus: Breaking tasks into steps lets Claude concentrate on specific requirements rather than juggling multiple constraints simultaneously
• Iterative refinement: Follow-up requests can target specific issues from previous outputs

Practical Application

• Use chaining when you have a task with many constraints that Claude isn't fully satisfying
• Send initial request, accept imperfect output, then send follow-up request asking Claude to fix specific issues
• More effective than simply repeating constraints in one long prompt

Takeaways

• Prompt chaining breaks large tasks into sequential subtasks sent as separate requests, allowing Claude to focus on one task at a time for better quality
• Chaining handles constraint violations better than single large prompts—when many constraints exist, Claude is more likely to violate some, but sequential requests let it concentrate on specific requirements
• Use chaining when a single prompt with multiple constraints isn't producing satisfactory output; send initial request then follow-up requests targeting specific issues to refine results