AI & Prompt Engineering Roadmap

Master the fundamentals of Artificial Intelligence and the art of Prompt Engineering to harness the power of large language models.

Back to 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.

Core Mindsets for Success

1. Be an Explorer (Iterative Experimentation)

Prompt engineering is more art than science. Don't expect perfect results on the first try. Experiment with wording, structure, and context. The best results come from iteration.

2. Think Critically, Not Magically

AI is a powerful tool, not a magic oracle. Always question its outputs. Be aware of potential biases, inaccuracies ("hallucinations"), and the limitations of its knowledge.

3. Context is King

The quality of your output is directly proportional to the quality of your input. Provide clear, specific, and sufficient context to guide the AI toward the desired result.

4. Stay Curious and Adaptable

The field of AI is moving at lightning speed. What works today might be outdated tomorrow. Cultivate a habit of continuous learning to stay at the forefront.