This comprehensive workshop is designed for beginners to transition from basic AI users to proficient prompt engineers capable of maximizing productivity with Large Language Models (LLMs) like GPT-4. Participants will explore the intersection of linguistics and machine learning to understand how these models process information, moving beyond simple queries to structured, high-level interactions. By balancing the theoretical "why" of model behavior, including mechanics like text embeddings and hallucinations, with intensive hands-on sessions, participants will master specific strategies such as few-shot prompting and persona adoption. Ultimately, this course empowers professionals to build a robust prompt library and leverage AI as a sophisticated thought partner in various industry contexts.
- Build a foundational understanding of how LLMs work, from predictive patterns to transformer architecture.
- Apply linguistic principles to craft clearer, more precise prompts that align with how models process language.
- Master core prompting techniques including persona, iterative, zero-shot, and few-shot approaches for real business scenarios.
- Understand the technical mechanics behind tokens, embeddings, and hallucinations to use AI more accurately and cost-effectively.
- Module 1
The Landscape of Large Language Models
Traces AI evolution from early pattern-matching to modern LLMs. Introduces the Prompter's Mindset and the role of the Transformer architecture in today's generative models.
- Module 2
The Science of Language in Prompting
How linguistics — syntax, semantics, and pragmatics — directly shapes prompt quality. Covers standardized grammar and computational language processing for more accurate outputs.
- Module 3
Practical ChatGPT Mastery
Navigating the GPT-4 interface, managing conversation threads, and understanding tokens and billing. Participants learn to build multi-step results while keeping usage costs in check.
- Module 4
Best Practices & Business Case Study
Persona technique, output formatting, and iterative prompting applied to a real corporate communication challenge. Participants transform a single brief across Creative, Legal, and Executive audiences.
- Module 5
Zero-Shot vs Few-Shot Prompting
When to rely on the model's base knowledge versus feeding it custom examples. Hands-on exercises teach participants to provide in-session contextual training for niche or private data.
- Module 6
Vectors, Embeddings & AI Hallucinations
Why hallucinations happen and how embeddings capture semantic meaning through vector math. Covers strategies to detect model errors and an intro to building custom LLM-powered platforms via API.