7 lessons
What is Context Design? Going from theory to production
Introducing Context Design — the systems-architecture discipline of actually building the pipeline this course has taught the theory behind, tech stack and all.
Designing the context data pipeline
How raw, messy, constantly-changing source content becomes clean, chunked, retrievable knowledge — ingestion, cleaning, chunking at scale, and staying up to date.
Choosing an embedding model for production
The real trade-offs between OpenAI, Cohere, and open-source embedding models — dimensionality, cost, latency, domain fit, and the MTEB benchmark.
Choosing a vector database
FAISS, pgvector, Qdrant, Weaviate, Milvus, and Pinecone compared honestly — what each is actually good at, and how to pick for your specific scale and constraints.
Caching and latency design for context-heavy systems
Prompt caching, prefix caching, embedding caching, and semantic caching — how production systems keep context-heavy LLM applications fast and affordable.
Guardrails and observability
Defending a production context-engineered system against prompt injection and misuse, and instrumenting it so a bad answer is always debuggable.
Capstone: designing a production context system end to end
The closing case study for the whole course — walking through every design decision for the internal knowledge assistant used throughout Module 9, tied back to every earlier module.