Implementing LLM Applications

Move beyond prototypes to production-ready LLM applications. This course covers the practical skills needed to build, deploy, and maintain LLM-powered systems.

Prerequisites

• Basic programming experience (Python preferred)
• Familiarity with APIs and web services
• Understanding of AI fundamentals (or complete our beginner course first)

What You'll Build

Throughout this course, you'll build a complete LLM application including:

• A RAG-powered knowledge assistant
• Prompt templates and chains
• Evaluation pipelines
• Production deployment configuration

Course Content

Module 1: LLM Application Architecture

Understanding how the pieces fit together:

• Common architectural patterns
• When to use agents vs. chains vs. simple prompts
• Trade-offs between different approaches

Module 2: Prompt Engineering Mastery

Beyond basic prompting:

• System prompts and personas
• Few-shot learning
• Chain-of-thought reasoning
• Structured outputs

Module 3: Building RAG Systems

Implementing retrieval-augmented generation:

• Document processing pipelines
• Embedding models and vector stores
• Query strategies
• Hybrid search approaches

Module 4: LLM APIs and SDKs

Working with LLM providers:

• OpenAI, Anthropic, and open-source options
• Using LangChain and similar frameworks
• Error handling and retries
• Cost optimization

Module 5: Testing and Evaluation

Ensuring quality at scale:

• Creating evaluation datasets
• Automated testing approaches
• Measuring accuracy and quality
• A/B testing in production

Module 6: Production Deployment

Going live with confidence:

• Infrastructure considerations
• Monitoring and observability
• Security and compliance
• Scaling strategies

Hands-On Projects

Each module includes practical exercises that build toward a complete application. You'll have working code you can adapt for your own projects.

Support

Questions during the course? Our team of fractional AI engineers is here to help you apply these concepts to your specific context.