AI & Prompt Engineering Roadmap

Phase	Main Topic	Content & Learning Activities	Objectives & Deliverables
1. Foundation	Math & Programming for AI	Review core concepts: Linear Algebra, Calculus, Probability & Statistics. Learn Python Programming: Basic syntax, data structures, and key libraries (NumPy, Pandas). Understand basic Data Science concepts and workflows.	Set up a Python development environment for AI. Write scripts for basic data processing and analysis.
2. Machine Learning Basics	Core Algorithms & Models	Learn about Supervised Learning (Regression, Classification) and Unsupervised Learning (Clustering). Practice with libraries like Scikit-Learn to build predictive models. Understand model evaluation techniques and feature selection.	Build and evaluate simple machine learning models. Solve basic prediction problems.
3. Intro to LLMs & Prompt Engineering	Understanding and Interacting with LLMs	Learn about Large Language Models (LLMs), Transformer architecture, and attention mechanisms. Basic concepts of Prompt Engineering: Zero-shot, One-shot, and Few-shot prompting. Practice writing clear and effective prompts for various tasks. Explore models like GPT, Claude, and interactive platforms.	Generate desired outcomes from LLMs through basic prompts. Understand the capabilities and limitations of current models.
4. Advanced Prompt Engineering	Sophisticated Techniques	Chain-of-Thought for solving complex problems. Role-playing prompts and output formatting. Self-consistency to improve the reliability of answers. Learn about Retrieval-Augmented Generation (RAG) to provide external knowledge to LLMs.	Design complex prompts to solve multi-step problems. Significantly improve the quality and accuracy of AI responses.
5. Generative AI & Applications	Building with AI	Introduction to Generative AI beyond text: image, code, music generation. Using LLM APIs (e.g., OpenAI API) to integrate into applications. Build simple applications using LLMs as the core. Learn about AI ethics, biases, and mitigation measures.	Develop interactive AI-based applications. Apply ethical principles in AI projects.
6. Optimization & Deployment	From Experiment to Production	Techniques for systematically evaluating and testing prompts. Fine-tuning smaller models for specific tasks. Understand MLOps and best practices for deploying LLM-based systems. Optimize cost and latency when using APIs.	Create a set of effective and tested prompts. Understand the lifecycle of a generative AI application.
7. Capstone Project	Build a Comprehensive AI Application	Apply all learned concepts to a real-world project. Ideas: Build a specialized chatbot, a document summarization tool, a creative content generator. Document your prompt design and evaluation process.	Complete a generative AI application to consolidate knowledge. Be ready for advanced topics and job roles in the AI field.

1. Foundation

Math & Programming for AI

Review core concepts: Linear Algebra, Calculus, Probability & Statistics.
Learn Python Programming: Basic syntax, data structures, and key libraries (NumPy, Pandas).
Understand basic Data Science concepts and workflows.

Set up a Python development environment for AI.
Write scripts for basic data processing and analysis.

2. Machine Learning Basics

Core Algorithms & Models

Learn about Supervised Learning (Regression, Classification) and Unsupervised Learning (Clustering).
Practice with libraries like Scikit-Learn to build predictive models.
Understand model evaluation techniques and feature selection.

Build and evaluate simple machine learning models.
Solve basic prediction problems.

3. Intro to LLMs & Prompt Engineering

Understanding and Interacting with LLMs

Learn about Large Language Models (LLMs), Transformer architecture, and attention mechanisms.
Basic concepts of Prompt Engineering: Zero-shot, One-shot, and Few-shot prompting.
Practice writing clear and effective prompts for various tasks.
Explore models like GPT, Claude, and interactive platforms.

Generate desired outcomes from LLMs through basic prompts.
Understand the capabilities and limitations of current models.

4. Advanced Prompt Engineering

Sophisticated Techniques

Chain-of-Thought for solving complex problems.
Role-playing prompts and output formatting.
Self-consistency to improve the reliability of answers.
Learn about Retrieval-Augmented Generation (RAG) to provide external knowledge to LLMs.

Design complex prompts to solve multi-step problems.
Significantly improve the quality and accuracy of AI responses.

5. Generative AI & Applications

Building with AI

Introduction to Generative AI beyond text: image, code, music generation.
Using LLM APIs (e.g., OpenAI API) to integrate into applications.
Build simple applications using LLMs as the core.
Learn about AI ethics, biases, and mitigation measures.

Develop interactive AI-based applications.
Apply ethical principles in AI projects.

6. Optimization & Deployment

From Experiment to Production

Techniques for systematically evaluating and testing prompts.
Fine-tuning smaller models for specific tasks.
Understand MLOps and best practices for deploying LLM-based systems.
Optimize cost and latency when using APIs.

Create a set of effective and tested prompts.
Understand the lifecycle of a generative AI application.

7. Capstone Project

Build a Comprehensive AI Application

Apply all learned concepts to a real-world project.
Ideas: Build a specialized chatbot, a document summarization tool, a creative content generator.
Document your prompt design and evaluation process.

Complete a generative AI application to consolidate knowledge.
Be ready for advanced topics and job roles in the AI field.