Your Journey to Agentic AI Engineering
Mastering modern AI development requires more than calling basic APIs. See how autonomous agents plan, invoke search databases, and solve objectives step-by-step.
Curriculum Roadmap Progress
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API & HTTP Foundations
Master the fundamental networking layer that makes all AI integrations possible.
Key Topics
- ✦Web Server vs Client communication model
- ✦HTTP Request Methods: GET, POST, Headers, and Bodies
- ✦Crucial Status Codes (200 OK, 401 Unauthorized, 429 Rate Limit, 500 Server Error)
LLMs in Practice
Transition from concepts to code by making real calls to OpenAI and Groq APIs.
Key Topics
- ✓Safe credentials management using .env files and python-dotenv
- ✓Writing raw API requests in Python using the requests library
- ✓Leveraging official Python SDKs with type safety and automatic retries
- ✓Using Groq and Llama 3 for 100% free high-speed developer testing
Tokenization & AI Cost Optimization
Deeply understand how AI models translate language into math and how providers charge you.
Key Topics
- ✓How AI reads text: ASCII limitations vs 1,024-dimensional Vector Embeddings.
- ✓Granular Tokenization Rules: spacing, punctuation, and capitalizations.
- ✓The Language Penalty: Why non-English can cost up to 9x more tokens.
- ✓Hidden Taxes & Context Windows: MCPs, tools, and history silently consuming memory.
Prompt Engineering & Structured Outputs
Evolve from simple conversational prompting to programmatically controlled outputs.
Key Topics
- ✓Mastering System vs User vs Assistant messaging roles
- ✓Few-shot in-context learning to dynamically control model output styles
- ✓JSON Mode and Structured Output schemas (Pydantic and Instructor)
State, Conversations & Memory
How to make stateless REST APIs behave as if they have long-term human memory.
Key Topics
- ✓Chat history retention, sliding context windows, and message pruning
- ✓Memory summarization agents that compress long conversations on the fly
- ✓Semantic retrieval (RAG) using vector search engine indices
Autonomous Agents & Tool Calling
Build systems that don't just write answers—they actually use computers and browse the web.
Key Topics
- ✓Understanding the ReAct loop: Reasoning → Action → Observation → Loop
- ✓Providing Python functions as native tools LLMs can call dynamically
- ✓Orchestrating agent workflows using frameworks like LangGraph and CrewAI
Take the Code Home 💻
The full workspace, including hands-on Python scripts and interactive Jupyter Notebooks, is open-source.