GenAI in Practice
Building things that hold up outside the demo
Prompt patterns, retrieval, vector search, agents, structured output, evaluation and cost — the decisions you make once the toy version works.
Beginner–Advanced68 min9 topics
0 of 9 topics
Grounding in Your Data
- Retrieval-Augmented Generation (RAG)IntermediateA model has never seen your company's refund policy. So how does it answer questions about it correctly — and cite the paragraph?7 min
- Embeddings for SearchIntermediateHow does a search box find 'get my money back' inside a document that only ever says 'reimbursement'?7 min
- Vector Databases 101IntermediateSearching ten million documents in 20 milliseconds sounds impossible. It is — so what exactly did the database quietly give up?7 min
Making Models Act
- Agents & Tool UseAdvancedThe moment a model can do something instead of just say something, a 95%-accurate step stops being good news. Why?8 min
- Structured Outputs & Function CallingIntermediate'Respond only with JSON' works 299 times, then breaks your parser at 2am. What makes it impossible to break instead of merely unlikely?7 min
Shipping Responsibly
- Evaluating LLM OutputsAdvancedYour prompt change looks better on the three inputs you tried. How would you know if it wasn't?8 min
- Cost, Latency & Model SelectionIntermediateThe best model is usually the wrong default. Reordering two paragraphs of your prompt can cut the bill by an order of magnitude. Both are the same idea.7 min
- Guardrails & Safety BasicsIntermediateYou can't fix prompt injection by telling the model to ignore malicious instructions. The reason why is structural — and it changes how you build.8 min