Course Objective & Scope
Forcing a complex multi-stage business process—such as analyzing an RFP, cross-referencing internal capabilities, auditing compliance risks, and drafting a tailored proposal—into a single monolithic prompt causes models to skip instructions and produce shallow results. This course teaches how to engineer reliable Multi-Step Prompt Flows (prompt chaining).
Participants learn to decompose complex workflows into sequential, parallel, and branching prompt stages, design clean data handoffs between steps, and insert automated verification and human approval gates. The resulting workflows can be run manually in workspaces or deployed directly into automation and agent platforms.
Who Should Attend?
- Specialists and team leads managing multi-stage research, reporting, proposal, or operational workflows
- Business analysts and process designers preparing workflows for Copilot Studio, n8n, Make, or internal AI agents
- Experienced AI users hitting accuracy ceilings with single-turn prompts
- Familiarity with basic prompt writing is recommended; no coding required
Key Takeaways
- Recognize when a task exceeds single-prompt reliability and decompose it into discrete sub-tasks
- Design sequential pipelines, conditional routers, and parallel synthesis chains
- Structure intermediate handoff artifacts (tables, JSON, structured summaries) to prevent context rot and error cascading
- Insert self-critique steps and human-in-the-loop review gates between critical workflow stages
- Document an end-to-end multi-step AI workflow blueprint ready for team execution or automation tooling
Curriculum
Task Decomposition: Why Single Prompts Hit a Ceiling
- Diagnosing monolithic prompt failures: instruction dropping, shallow reasoning, and hard-to-debug outputs
- Breaking workflows into discrete stages: Ingestion & Extraction → Analysis → Drafting → Audit & Formatting
- Comparing multi-turn conversational stepping against stateless prompt chaining with clean context windows
Core Chaining Patterns: Sequential, Router, and Parallel Flows
- Sequential chaining: passing structured intermediate outputs from Step A directly into Step B
- Conditional routing: classifying an incoming request first, then dispatching it to a specialized prompt branch
- Parallel perspectives and aggregation (Map-Reduce): running independent analyses and synthesizing findings
Clean Handoffs, Error Containment, and Review Gates
- Preventing cascading errors where an early hallucination corrupts every downstream step
- Designing compact handoff schemas so downstream prompts receive only verified facts, not noisy chat history
- Embedding Evaluator-Optimizer loops and human-in-the-loop sign-off gates at critical transitions
Blueprint Workshop: Designing Portable AI Workflows
- Structuring prompt chains so they can transition smoothly from manual workspaces to Copilot Studio, n8n, or agents
- Documenting workflow specifications: trigger inputs, step prompts, routing rules, and final deliverables
- Workshop: Building and testing a 4-stage corporate prompt pipeline on a real departmental use case
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