LLM Engineering
Production-Grade Language Model Systems
This intensive workshop covers everything engineers need to build production-grade LLM applications. From understanding model architectures to implementing RAG systems and optimizing for cost and latency, you'll gain hands-on experience with real-world patterns.
What You'll Learn
Full Curriculum
Detailed breakdown of workshop modules and timing
LLM Foundations
Deep dive into transformer architecture, tokenization, context windows, and model comparison. Understand when to use GPT-4, Claude, Llama, and others.
Prompt Engineering Deep Dive
System prompts, few-shot learning, chain-of-thought reasoning. Build a systematic approach to prompt development and testing.
RAG Architecture
Vector databases, embedding models, chunking strategies, and retrieval optimization. Design patterns for production RAG systems.
Hands-on RAG Build
Build a production-ready RAG system with your data. Implement semantic search, reranking, and hybrid retrieval.
Production Considerations
Caching strategies, rate limiting, fallback patterns, and monitoring. Build resilient LLM applications.
Cost & Performance Optimization
Token optimization, model routing, latency budgets, and cost attribution. Make informed tradeoffs for your use case.
Workshop Outcomes
What you'll walk away with
Who This Workshop Is For
Software engineers, ML engineers, backend developers building AI features
Interested in LLM Engineering?
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