Tutorials Prompt Engineering Tutorial
AI Workflow Basics — Complete Guide
AI Workflow Basics — 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 10 of 100
AI Workflow Basics
Prompts → Apps
Prompts · 1 — Basics · ~6 min · Module 1: Prompt Engineering Foundations
What is this?
An AI workflow chains steps: ingest input, maybe retrieve docs, call the LLM, validate output, then hand off to a human or downstream system.
Why should you care?
PromptVerse Workflow Engine connects triggers (webhook, schedule) to prompt nodes and approval gates.
See it live — copy this example
Copy the prompt into ChatGPT, Claude, or your LLM API playground and compare outputs.
trigger: new_zendesk_ticket
steps:
1_retrieve: hybrid_search(query=ticket.subject)
2_classify: llm(prompt=classifier_v3, context=chunks)
3_draft: llm(prompt=draft_v2, category=step2.category)
4_review: human_if priority=highWhat happened?
- Each step has named inputs from the previous step.
- Retrieve happens before generate.
- Human review is explicit for high priority.
Practice next
- Sketch 4 boxes for a ticket workflow on paper.
- Label data in/out of each box.
- Mark where the LLM runs.
- Insert a moderation step after draft.
- Parallelize retrieve and PII scan.
Remember
Workflows decompose AI into steps. Validate between LLM calls. Humans at high-risk gates.
Support pipeline
Ticket arrives at 2am.
Outcome: Workflow drafts reply; human approves at 9am.
Interview prep for this lesson
Practice these questions aloud after reading—each links to a full structured answer.
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