LLM Engineering in Production

Most LLM tutorials stop at “call the API and print the result.” Production doesn’t. Production means handling streaming failures at 3 AM, explaining to your CFO why the OpenAI bill tripled, stopping prompt injection attacks before they leak your system prompt, and figuring out why the RAG pipeline that worked perfectly in testing returns garbage in production.

This crash course bridges the gap between “I can use ChatGPT” and “I ship reliable AI features that my team trusts.” Every lesson includes production code, not toy examples.

What This Course Covers

Fifteen lessons that take you from LLM fundamentals to shipping a production RAG system:

1. How LLMs Actually Work — What You Need to Know — Transformers, tokens, attention, and the mental model that makes everything else click
2. Choosing Between OpenAI, Claude, and Open Source — Model comparison, benchmarks, cost analysis, and a decision framework
3. Prompt Engineering Fundamentals — System prompts, output formatting, role patterns, and the basics that 90% of developers get wrong
4. Advanced Prompting — Chain of Thought, Few-Shot — CoT, few-shot, self-consistency, and structured reasoning techniques
5. Building a RAG Pipeline from Scratch — End-to-end retrieval augmented generation with working code
6. Vector Databases — Pinecone vs pgvector vs Chroma — When to use what, performance characteristics, and migration strategies
7. Chunking Strategies — What Nobody Tells You — Fixed, recursive, semantic, and document-aware chunking with real benchmarks
8. Evaluating LLM Output Quality — Automated evals, human-in-the-loop scoring, and regression testing for prompts
9. Streaming Responses in Production — SSE, WebSockets, backpressure, error recovery, and frontend integration
10. Cost Optimization for LLM Applications — Caching, model routing, prompt compression, and batch APIs
11. Securing Your LLM Application — API key management, rate limiting, output filtering, and data privacy
12. Prompt Injection — Attacks and Defenses — Direct injection, indirect injection, jailbreaks, and layered defense strategies
13. Building AI Agents with Tool Use — Function calling, tool orchestration, ReAct loops, and error handling
14. LLM Observability and Monitoring — Logging, tracing, cost dashboards, latency tracking, and drift detection
15. Real Project — Build a RAG Knowledge Bot — Tie everything together into a production-ready knowledge assistant

Who Is This For?

• Backend engineers adding AI features to existing products
• Full-stack developers building their first LLM-powered application
• Tech leads evaluating LLM integration for their team
• Engineers who’ve completed introductory AI courses and want production depth

Prerequisites

• Comfortable writing Python (all code examples use Python)
• Experience with REST APIs, JSON, and basic web architecture
• Familiarity with databases (SQL or NoSQL)
• An API key from OpenAI or Anthropic (free tier works for most lessons)
• Basic understanding of what LLMs are (you’ve used ChatGPT or Claude)