What You'll Learn

By the end of this course, you'll be able to:

Design effective LLM pipelines

Implement RAG systems

Build multi-agent frameworks

Optimize LLM performance

Handle LLM limitations

Deploy and scale LLM applications

Course Modules

A comprehensive curriculum designed for practical application.

1

LLM Fundamentals

  • How LLMs work
  • Model architectures
  • Training processes
  • Capability assessment
2

Prompt Engineering Deep Dive

  • Advanced prompting
  • Prompt templates
  • Output parsing
  • Error handling
3

LLM APIs and SDKs

  • OpenAI API
  • Anthropic API
  • Open-source models
  • SDK best practices
4

Retrieval-Augmented Generation

  • Vector stores
  • Embedding models
  • Chunking strategies
  • Hybrid retrieval
5

Memory and Context

  • Conversation memory
  • Summary memory
  • Entity tracking
  • Long-context handling
6

Tool Use and Plugins

  • Function calling
  • API integration
  • Custom tools
  • Tool selection
7

Multi-Agent Systems

  • Agent design
  • Role-based agents
  • Collaboration patterns
  • Conflict resolution
8

Evaluation and Testing

  • LLM evaluation metrics
  • Benchmark datasets
  • A/B testing
  • Human evaluation
9

Safety and Alignment

  • Content filtering
  • Jailbreak prevention
  • Output validation
  • Ethical guidelines
10

Cost Optimization

  • Token optimization
  • Caching strategies
  • Model selection
  • Batch processing
11

Production Deployment

  • API design
  • Scaling
  • Monitoring
  • Incident response
12

Capstone Project

  • Full LLM application
  • Integration testing
  • Performance optimization
  • Documentation

Enroll in LLM Engineering

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