Tutorials Prompt Engineering Tutorial
AI Prompt Chaining — Complete Guide
AI Prompt Chaining — Complete Guide: free step-by-step lesson with examples, common mistakes, and interview tips — part of Prompt Engineering Tutorial on Toolliyo Academy.
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Prompt Engineering Tutorial · Lesson 30 of 100
AI Prompt Chaining
Prompts → Apps
Prompts · 1 — Basics · ~6 min · Module 3: Advanced Prompt Engineering
What is this?
Prompt chaining links prompts where output text from call A is inserted into prompt B — often simpler than full workflow engines for 2–3 hops.
Why should you care?
PromptVerse blog workflow: outline prompt → section expander → SEO title prompt.
See it live — copy this example
Copy the prompt into ChatGPT, Claude, or your LLM API playground and compare outputs.
outline = llm("Outline 5 H2s for: " + topic)
for (h2 of parseH2s(outline)) {
body[h2] = llm("Write 150 words for section: " + h2 + "\nOutline: " + outline)
}
title = llm("SEO title under 60 chars for outline:\n" + outline)What happened?
- Outline constrains expander sections.
- Title prompt sees whole outline for coherence.
- Each hop has narrow responsibility.
Practice next
- Chain outline → one section expand.
- Check section matches outline.
- Add title as third hop.
- Cache outline by hash.
- Add reflect hop after expand.
Remember
Small prompts chain cleanly. Pass prior output explicitly. Validate each hop.
Content assembly line
Long-form post from one brief.
Outcome: Three-hop chain beats one 4k-word prompt.
Interview prep for this lesson
Practice these questions aloud after reading—each links to a full structured answer.
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