Prompt Chaining & Workflow Design

SecAI+ Domain
1.0: Basic AI Concepts Related to Cybersecurity​

SecAI+ Objectives
1.1: Understand important AI concepts.​

1.2: Recognize limitations and potential errors in AI-generated outputs.​

1.3: Evaluate the trustworthiness of AI findings by cross-referencing with credible sources.​

1.4: Apply critical thinking skills to identify gaps, conflicts, and inconsistencies in information.​

1.5: Synthesize and communicate findings clearly in a workplace-ready research brief.​

Overview

This lab introduces learners to prompt chaining, in which each AI-generated output serves as input to the next prompt in a multi-step workflow. Through guided activities, learners build structured AI workflows to perform complex tasks such as summarizing data, generating content, refining style, or automating reasoning steps. Participants will plan, implement, and document a fully functional chained prompt sequence while considering efficiency, consistency, and risk in automated decision paths.

Learning Objectives


By the end of this lab, learners will be able to:

  • Explain the purpose and benefits of prompt chaining.
  • Design multi-step AI workflows where outputs feed subsequent prompts.
  • Apply structured input/output formatting to enhance consistency between steps.
  • Evaluate efficiency gains and potential error propagation risks in chained systems.
  • Create and document a reusable AI workflow that performs a real-world task.
Courses

Key terms and descriptions

Prompt Chaining
Designing a multi-step sequence where the output of one AI prompt is deliberately used as the input to the next step to accomplish a larger task.
AI Workflow
A structured series of AI-assisted steps, often combining prompts, tools, and validation checks, to support a cybersecurity or business objective.
Intermediate Output
A partial result produced at an intermediate step in a chain that shapes or constrains later prompts.
Structured Input
Information passed between steps using a consistent format (such as lists, tables, or JSON-like schemas) to reduce ambiguity.
Error Propagation
The way inaccuracies, hallucinations, or biases in one AI output can be amplified when reused in subsequent steps.
Hallucination Risk
The likelihood that an AI model produces confident but incorrect or fabricated details that may enter a chain as if they were facts.
Verification Step
A specific point in the workflow where AI outputs are checked against trusted tools, logs, policies, or documentation.
Guardrail Prompting
Using constraints, instructions, and formatting rules in prompts to reduce unsafe, irrelevant, or low-quality outputs within a chain.
Overreliance on AI
Depending too heavily on AI-generated outputs without sufficient human review, especially risky in security workflows.
Workflow Documentation
A concise artifact that captures the purpose, steps, inputs/outputs, risks, and validation methods of an AI prompt chain so others can safely reuse it.