Duration: 2-day Course

Course Description:

This hands-on, two-day course teaches professionals how to design, test, and optimize prompts to achieve consistent, high-quality results from large language models (LLMs). Participants will explore different prompt patterns, adapt prompts for specific tools (like OpenAI, Anthropic, Google, and open-source models), and evaluate prompt output for reliability and alignment. The course includes live demonstrations, guided labs, and real-world scenarios across industries like marketing, customer service, education, and software development.

Target Audience:

• AI product developers
• Data analysts and business professionals
• Educators and instructional designers
• Technical writers and content creators
• Software engineers integrating LLM APIs

Key Course Takeaways

• Understand the core principles of prompt design and evaluation
• Confidently write and adapt prompts for different tools and industries
• Apply structured prompt frameworks to solve complex, real-world tasks
• Evaluate and improve prompt output for accuracy, tone, and reliability
• Build multi-step and role-based interactions using prompt chaining
• Address safety, alignment, and bias mitigation in your prompts
• Use LLM APIs and automation tools to integrate prompt-based workflows

Module 1: Introduction to Prompt Engineering

• What is a prompt and why it matters
• Types of prompts (instructional, zero-shot, few-shot, chain-of-thought)
• The anatomy of a good prompt
• Limitations and constraints of LLMs

Hands-On Lab:
Explore differences between zero-shot and few-shot prompts in ChatGPT, Claude, and Gemini.

Module 2: Patterns for Precision

• Prompt frameworks: AIDA, TACT, REACT, Tree-of-Thought
• Role-based prompting and system messages
• Controlling tone, verbosity, and structure

Hands-On Lab:
Create prompts using role-based instructions and style guides for different industries.

Module 3: Prompting for Accuracy and Reasoning

• Chain-of-thought prompting and reasoning strategies
• Managing hallucination and overconfidence
• Encouraging transparency and verification

Hands-On Lab:
Design prompts that walk-through reasoning (math, logic, classification tasks). Evaluate and revise.

Module 4: Model-Specific Prompting

• Comparing prompts across LLMs (OpenAI vs Anthropic vs Gemini vs open-source)
• System prompts, temperature, max tokens, and other tuning settings
• When to use tools like LangChain or OpenAI function calling

Hands-On Lab:
Run identical prompts across multiple platforms and compare output quality, consistency, and bias.

Module 5: Prompt Engineering in Real-World Scenarios

• Use cases in marketing, customer support, finance, education, and software development
• Automating workflows with well-engineered prompts
• Prompt chaining for multi-turn tasks

Hands-On Lab:
Build a multi-step chatbot for customer inquiries using prompt chaining techniques.

Module 6: Prompt Evaluation and Optimization

• Prompt testing methods (A/B testing, prompt tuning)
• Metrics: accuracy, helpfulness, faithfulness
• Using GPT-4, Claude, and others to evaluate their own responses

Hands-On Lab:
Use AI tools to critique and improve the quality of initial prompts.

Module 7: Safety, Ethics, and Alignment

• Prompt design for reducing bias and toxicity
• Aligning outputs with organizational values
• Guardrails and content filters

Hands-On Lab:
Create prompts to moderate responses, test outputs under stress, and implement fail-safes.

Module 8: API-Based Prompting and Deployment

• Prompting via Python and REST APIs
• Dynamic prompts with user input and system context
• Best practices for scalable deployment

Optional Lab (for coders):
Build a simple LLM-powered app using OpenAI API with user input and feedback loop.