Advanced Prompt Engineering & LLM Workflow Design

3-Day Intensive Course for Educators, Strategists, AI Leads & Product Builders
3 Intense Days
7 Hours per Day (Split into two 3.5-hour sessions)

Learning Path Visual

From single prompts to structured, scalable AI solutions:

Day 1: Deep Prompt Design & Prompt Debugging
Go beyond basic formats and master advanced prompting strategies: complex few-shot examples, role conditioning, formatting constraints, zero-shot chain-of-thought, and error diagnosis. Learn how to debug failing prompts, control randomness (temperature), and enforce structure.

Day 2: Multi-Step Workflows, APIs & Tool Integration
Design multi-stage workflows using prompt chaining, memory simulation, and conditional logic. Learn how to build AI agents that call tools (via APIs, functions, or external actions) and return usable outputs for end-users or business systems.

Day 3: AI Product Prototyping, Testing & Governance
Turn your LLM logic into a scalable tool or service. Use prompt libraries, evaluation metrics, feedback loops, and safety tests to deploy reliable systems. Learn how to structure an AI assistant, chatbot, or writing assistant with user-focused design.

Course Overview

This course is for professionals who want to do more than just “use” AI — they want to design intelligent LLM-powered systems that are scalable, safe, and reliable. You’ll learn how to chain prompts into full workflows, evaluate them under real conditions, and prepare them for team use, deployment, or productization.

Whether you’re automating a research process, building a sales assistant, or embedding AI into a service, this course gives you the architecture, strategy, and tools to deliver.

You’ll learn how to:

• Build expert-level prompts with layered instructions and constraints

• Chain multiple prompts into coherent logic flows

• Simulate memory, role-switching, and decision-making

• Use APIs and external tools in prompt-driven workflows

• Evaluate outputs with structured rubrics and user feedback

• Design and test real-world AI assistants or writing tools

What’s Inside Each Day

Day 1 — Deep Prompt Design & Prompt Debugging

• Prompt architecture: layered role prompts, multi-modal framing

• Token economy and prompt compression techniques

• Debugging: diagnosing hallucination, misalignment, output errors

• Consistency tuning: structure enforcement and response targeting

• Workshop: Rewrite and stabilize a faulty prompt flow
  Toolkit: Prompt debugger checklist + complexity planner
  Focus: Mastery • Precision • Consistency

Day 2 — Multi-Step Workflows, APIs & Tool Integration

• Prompt chaining: sequential prompts and decision forks

• Simulated memory and tool-call emulation

• Connecting LLMs to outside tools: APIs, spreadsheets, calculators

• Modular workflows for teams and business operations

• Workshop: Design an LLM system that interacts with a real or simulated database
  Toolkit: Prompt stack template + input/output flowchart
  Focus: Logic Design • Interoperability • Automation

Day 3 — AI Product Prototyping, Testing & Governance

• Designing for users: prompt-as-product thinking

• Usability, clarity, fallbacks, guardrails

• Evaluation metrics: fluency, factuality, safety

• Governance: audit logs, oversight, transparency

• Workshop: Build and present a working prototype with eval logic
  Toolkit: Prompt performance scorecard + policy doc template
  Focus: Productization • Trust • Delivery

Course Goals

By the end of this course, you will be able to:

• Engineer prompts with advanced structure and precision

• Build multi-step AI workflows with tool integrations

• Prototype and test reliable, user-friendly LLM systems

• Evaluate and improve prompts with metrics and feedback

• Understand the governance and deployment considerations for LLM applications

Who Should Take This Course?

• Instructional designers or curriculum developers building AI tools

• Startup founders and product teams launching LLM-powered services

• Researchers, data scientists, and analysts needing structured workflows

• Consultants building internal LLM systems or automations

• Professionals managing or scaling AI tools inside organizations

Class Reference: LLM/20
Form Updated on: 06/19/2025 (Version 1)
Last Modified on: 06/19/2025

Program Note
Participants will receive downloadable prompt chain templates, debug checklists, system diagrams, prompt evaluation rubrics, and a prototype planner pack.