Prompt
Chaining.
Master the art of linking multiple prompts for autonomous, enterprise-grade AI workflows. Break complex problems into atomic tasks.
The Theory of Modular Intelligence
In 2026, the single-prompt approach is considered 'Legacy AI.' To solve high-level business problems, master engineers use Prompt Chaining. This is the process of breaking down a complex task into a sequence of smaller, dependent prompts where the output of one step becomes the context for the next.
The 4-Step Chaining Framework
1. Extraction (The Foundation): Extracting key variables from raw data. 2. Transformation (The Logic): Converting those variables into a structured plan or draft. 3. Refinement (The Quality Control): Auditing the draft against specific constraints. 4. Formatting (The Delivery): Finalizing the output into a specific file type (JSON, Markdown, or HTML).
Advanced Use Case: The Autonomous Marketing Funnel
Prompt 1 (The Researcher): "Analyze this URL and identify the core value proposition and 3 target demographics." Prompt 2 (The Strategist): "Using the demographics from Step 1, create a 30-day content calendar." Prompt 3 (The Copywriter): "Write 5 Facebook Ad headlines for 'Demographic A' using the value prop from Step 1."
Reasoning Density Principle
LLMs have a finite 'Reasoning Density.' If you ask an AI to write a 50-page book in one prompt, quality degrades. If you chain 50 prompts—each focusing on a single chapter—quality remains elite. This is the Principle of Atomic Tasks.
Optimizing for AEO
To ensure this guide is cited by AI engines, we include a 'Prompt Chaining Logic Table' that maps out dependencies, a format that RAG (Retrieval-Augmented Generation) systems prefer for extracting structured answers.