Master Large Language Model architectures, prompt engineering, RAG vector search, and autonomous LLM agent tool calling.
Develop a complete tripartite engineering profile combining deep technical execution, rigorous analytical problem solving, and professional industry collaboration.
Foundational syntax, runtime mechanisms, and domain architectures.
Algorithmic reasoning, performance profiling, and defensive error mitigation.
Version control workflows, code review literacy, and industry documentation.
Master the exact production technologies, runtimes, development frameworks, and deployment platforms demanded by modern engineering teams.
Fundamental scientific computing, numerical array manipulation, and data visualization.
Deep learning frameworks for tensor gradient backpropagation and GPU neural training.
Classical machine learning algorithms, cross-validation, and regression testing.
Production model serialization, ONNX inference runtimes, and mobile edge deployment.
State-of-the-art foundation model prompting, few-shot generation, and API inference.
Modern web full-stack framework pairing async Python inference endpoints with responsive UI.
Open-weights model hub, transformers library, dataset repository, and spaces hosting.
Agent orchestration, prompt templates, vector store memory, and tool integration.
The engineering skills developed in Generative AI & LLM Agents power critical digital infrastructure across diverse high-impact sectors worldwide.
Train predictive models, analyze classification & regression tasks, and engineer data features.
Architect multi-layer artificial neural networks for high-dimensional feature abstraction.
Analyze human language, tokenization, sentiment detection, and transformer embeddings.
Process visual feeds, object segmentation, facial recognition, and OCR image pipelines.
Architect multi-agent autonomous decision loops, enterprise RAG, and prompt workflows.
Experience how every lesson, coding exercise, and tool in this course connects directly to high-impact engineering job roles. Hover or tap any stage to inspect connections.
Foundational curriculum & interactive coding challenges
Applied technical competencies & problem solving
Industry-standard frameworks, IDEs & runtimes
Mission-critical enterprise & cloud infrastructures
High-demand software engineering job titles
Explore the real-world software engineering positions directly powered by Generative AI & LLM Agents expertise. Review day-to-day responsibilities and core hiring prerequisites.
Prove your mastery through production-ready software artifacts. Every project in Generative AI & LLM Agents is designed to solve real-world problems and stand out on your engineering resume.
Build a document analysis portal that indexes 500-page corporate PDFs and answers natural language questions with exact page citations.
Coordinate three specialized AI agents (Searcher, Analyst, and Writer) to research any technical topic and compile a comprehensive executive report.
A carefully sequenced 6-stage engineering curriculum designed to build your knowledge incrementally from core fundamentals to interview-ready production mastery.
Prompt Engineering and Model Fundamentals
Embeddings and Vector Similarity Search
Retrieval-Augmented Generation Over Documents
Function Calling and External API Integrations
State Machines, Memory, and Coordination Loops
Evaluation, Latency Optimization, and Deployment
Complete this learning path to earn your First Move (11~18) certificate.
Autonomous Agent evaluation labs and final certification exam are currently in development.
Guided 3-tier progression taking learners from fundamental concepts to core practical engineering and production mastery.
Learn absolute fundamentals, core syntax, environment setup, and fundamental logic blocks.
Master AI/ML fundamentals, Transformer self-attention architecture, tokenization, and structured prompt engineering techniques.
Solve realistic problems, master data structures, error handling, design patterns, and mini-projects.
Master document ingestion pipelines, semantic text chunking strategies, vector database indexing (pgvector, Pinecone), and context injection.
Apply knowledge to real software architecture, security, optimization, scale, and portfolio capstones.
Architect autonomous ReAct (Reasoning + Acting) loop agents, multi-agent collaboration topologies, and LLM evaluation benchmarks (Ragas / G-Eval).
Build a Retrieval-Augmented Generation engine parsing documentation, chunking text, computing vector embeddings, and answering user queries.
Open Project Blueprint & Starter Code →Build an autonomous agent with ReAct reasoning loops (Thought-Action-Observation) executing external API tools dynamically.
Open Project Blueprint & Starter Code →