AI Fundamentals & Prompt Engineering
From "what is an LLM?" to deploying production AI applications. 6 stages covering architecture, prompt engineering, the Claude/GPT/Gemini APIs, RAG systems, and autonomous agents.
- Full access to all 6 stages
- All code examples & projects
- Every TechNodeX course included
- New content as it's released
- Community access
500 founding spots available. Price locks in forever.
Course Stages
How LLMs Work
Transformers, tokenization, self-attention, training phases (pre-training, RLHF, Constitutional AI), context windows, and major model families. The mental models you need before writing a line of code.
Prompt Engineering Fundamentals
Zero-shot vs few-shot, chain-of-thought, system prompts, CO-STAR and RTF frameworks, output format control, prompt chaining, and anti-patterns that kill quality. Practical exercises throughout.
Using Claude, GPT & Gemini APIs with Python
API setup, completions, multi-turn conversations, streaming, function/tool calling, vision, error handling with exponential backoff, token counting, cost estimation, and a reusable multi-provider client.
Building RAG Systems with Vector Databases
Vector embeddings, cosine similarity, ChromaDB and Pinecone setup, chunking strategies, full document ingestion pipelines, retrieval-augmented generation, hybrid search, and RAG quality evaluation.
AI Agents and Agentic Workflows
The agent loop, ReAct pattern, building tool-using agents from scratch, multi-agent orchestration, LangChain and LangGraph for production workflows, safety guardrails, and prompt injection defense.
Capstone — Build and Deploy a Real AI Application
Full-stack AI app with FastAPI + Claude + ChromaDB. Streaming chat, RAG over course content, authentication, Docker containerization, Railway deployment, evaluation tests. Ends with a live URL in your portfolio.
What You'll Be Able to Build
Ready to Start Building?
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