Large Language Models (LLMs) & Prompt Engineering
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⁕ Technical Depth

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.

Technical14 hours · Self-pacedLifetime access
$697 · One-time · Lifetime access
⁕ The Curriculum

What 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.

  • 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
⁕ The Curriculum Arc

How the course is structured

01
Transformer Foundations
Attention, embeddings, and what the architecture actually does.
02
Prompt Engineering
Chain-of-thought, few-shot, and the patterns that produce reliability.
03
Evaluation & Testing
Build the test suite before you trust the output.
04
RAG Architecture
Retrieval, chunking, and grounded generation at scale.
05
Fine-Tuning
LoRA, PEFT, and domain adaptation without destroying the base model.
06
Production Deployment
Latency, cost, observability, and the on-call mindset for LLM systems.
⁕ Who This Is For

Engineers and technical leads building production LLM applications — not just experimenting with APIs.

Begin a considered
practice.

One course, your pace, lifetime access.

$697One-time · Lifetime access

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