Overview

A structured monthly learning plan designed to build foundational AI skills through hands-on lessons, practical deliverables, and self-assessment. This planner tracks 8 core lessons covering LLM basics, prompting techniques, embeddings, RAG (Retrieval-Augmented Generation), and evaluation methods—helping you progress from theory to application systematically.

What You'll Learn

The curriculum is organized into focused sessions spanning key AI topics:

• LLM Fundamentals: Understanding tokens, context windows, and how models process information
• Prompting & Temperature: Crafting effective prompts and controlling output randomness
• Embeddings & RAG: Vector similarity, chunking strategies, and retrieval workflows
• Safety & Evaluation: Identifying hallucination risks and building evaluation frameworks

Each lesson includes materials, time estimates, and confidence tracking to monitor your growth.

Key Features

• Progress Tracking: Checkboxes for completed lessons and deliverable status indicators
• Reusable Outputs: Create prompt libraries, evaluation rubrics, and reference notes
• Quality Ratings: Self-assess deliverables and confidence levels with star ratings
• Resource Library: Curated articles, videos, and documentation linked to specific lessons

How to Use

Follow the monthly plan sequentially or adapt based on your learning priorities. Complete lessons, produce the specified deliverables, and conduct a monthly review to identify skill gaps and plan next steps. Use the materials table as a reference hub while working through each topic.