Tutorials Prompt Engineering Tutorial
Multi-Step AI Workflows — Complete Guide
Multi-Step AI Workflows — 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 29 of 100
Multi-Step AI Workflows
Prompts → Apps
Prompts · 1 — Basics · ~6 min · Module 3: Advanced Prompt Engineering
What is this?
Multi-step workflows chain distinct LLM calls or human gates — each step has one job. Output of step N becomes input to step N+1.
Why should you care?
PromptVerse ticket pipeline: extract → classify → retrieve → draft → moderate → queue.
See it live — copy this example
Copy the prompt into ChatGPT, Claude, or your LLM API playground and compare outputs.
step1 = llm(EXTRACT, ticket)
step2 = llm(CLASSIFY, step1.json)
chunks = search(step2.intent, ticket)
step3 = llm(DRAFT, { ticket, chunks, category: step2.label })
if step3.needs_human: queue else send_to_reviewWhat happened?
- Extract structured fields first so classify sees clean JSON.
- Retrieve uses intent from step 2.
- Draft gets grounded chunks.
Practice next
- Draw your pipeline as 4 boxes.
- build two steps in a script.
- Pass JSON between steps.
- Insert moderation between draft and send.
- Parallelize extract and PII redact.
Remember
One job per LLM call. Structured handoffs. Branch on step outputs.
Support automation
Enterprise wants audited drafts.
Outcome: Five-step pipeline logs each intermediate JSON.
Interview prep for this lesson
Practice these questions aloud after reading—each links to a full structured answer.
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