Large Language Models (LLMs) & Prompt Engineering
Build Reliable, Evaluable LLM-Powered Systems
Beyond chat demos. This course covers the engineering fundamentals of LLM integration — prompt design, evaluation frameworks, RAG architectures, fine-tuning, and production deployment patterns for systems that actually work.
Enroll now – $697$697 · One-time · Lifetime accessWhat you will build and carry forward
This course includes: 14 hours covering transformer architecture, prompt engineering patterns, evaluation and testing, retrieval-augmented generation, fine-tuning workflows, and production deployment with observability.
Outcomes
- Understand transformer architecture at the level needed to make good engineering decisions
- Design prompts that produce reliable, consistent outputs across edge cases
- Build evaluation frameworks that measure what your LLM system actually needs to do
- Implement RAG pipelines for knowledge-grounded generation
- Fine-tune models on domain-specific data without catastrophic forgetting
- Deploy LLM APIs with latency budgets, cost controls, and observability
How the course is structured
Attention, embeddings, and what the architecture actually does.
Chain-of-thought, few-shot, and the patterns that produce reliability.
Build the test suite before you trust the output.
Retrieval, chunking, and grounded generation at scale.
LoRA, PEFT, and domain adaptation without destroying the base model.
Latency, cost, observability, and the on-call mindset for LLM systems.
Who this course is for
Engineers and technical leads building production LLM applications — not just experimenting with APIs.
Enroll now – $697$697 · One-time · Lifetime accessSecure checkout · Lifetime access · One-time payment