Course Specialization

Generative AI & LLM Engineering

Progress from Python and machine-learning foundations to transformer concepts, retrieval systems, evaluation, integration, and responsible deployment.

  • Python
  • LLMs
  • Prompt Engineering
  • RAG
  • Evaluation
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Generative AI and LLM Engineering course poster with AI brain artwork and curriculum highlights

Skills & Tools

Python

Machine Learning Foundations

Transformers & LLMs

Embeddings & Vector Search

RAG

Evaluation

Model APIs

Course Curriculum

Module 1: Python & Machine-Learning Foundations

Build the programming, data-handling, probability, and model-evaluation foundations needed for later generative AI work.

Module 2: Transformers & LLM Concepts

Understand tokens, embeddings, attention, context, training stages, inference, limitations, and common model families at a practical level.

Module 3: Prompt & Context Engineering

Structure instructions, examples, context, output formats, and safety boundaries, then test prompts systematically.

Module 4: Embeddings & Vector Search

Prepare content, create embeddings, compare retrieval strategies, use vector stores, and evaluate search quality.

Module 5: Retrieval-Augmented Generation

Build RAG pipelines with source attribution, access-aware retrieval, grounding checks, and clear failure behavior.

Module 6: Evaluation & Responsible AI

Define test sets and quality criteria, inspect bias and failure modes, protect sensitive data, and design meaningful human oversight.

Module 7: Model & API Integration

Connect model providers and supported open models through resilient APIs, structured outputs, tool calls, caching, and cost controls.

Module 8: Deployment Capstone

Deliver a documented generative AI application with evaluation evidence, monitoring signals, access controls, and an operational handoff.

Capstone Projects

Grounded Knowledge Assistant

Build and evaluate a retrieval-based assistant that cites approved sources and communicates uncertainty clearly.

Generative AI Deployment Capstone

Ship a scoped generative AI workflow with testing, observability, responsible-use controls, and documented limitations.

Course Specifications

Duration

6–12 months

Format

Guided, project-based learning; available modes are confirmed by the Education team.

Career Support

Portfolio, resume, and interview guidance without a placement guarantee.

Generative AI & LLM Engineering FAQ

This program teaches Python-based engineering of large language models (LLMs), covering how generative AI systems are built, fine-tuned, and applied, over a 6 to 12 month track depending on depth chosen.

Ready to start learning?

Share your preferred batch and learning mode with the Education team.

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