1. Foundations of LLM Applications
An LLM application combines a language model with product logic, data, tools, prompts, and user experience to solve a specific workflow. Rather than treating the model as a standal...
Read full chapter →The essential LLM Application Design cheat sheet: 6 concise chapters you can read in minutes, distilled from the full deck. When you're ready, drill the flashcards or test yourself under exam conditions.
An LLM application combines a language model with product logic, data, tools, prompts, and user experience to solve a specific workflow. Rather than treating the model as a standal...
Read full chapter →At the heart of any RAG system is the retrieval pipeline, and chunking is its starting point. Chunking splits documents into smaller passages that can be indexed and retrieved effe...
Read full chapter →Structured output asks the model to return data in a predictable format such as JSON, making it easier to validate and consume by downstream software. To make this reliable in prac...
Read full chapter →LLM applications often span many turns or sessions, which makes conversation memory important. Conversation memory stores relevant user or task context across turns or sessions, en...
Read full chapter →Tool calling introduces real side effects, so tools should have narrow permissions, exposing only the actions and data needed for the task. This limits damage from mistakes or prom...
Read full chapter →Once an LLM application is in production, observability becomes essential. Observability tracks requests, model choices, tools, latency, cost, and failures, making production LLM b...
Read full chapter →Done reading?
Test yourself with the LLM Application Design practice exam — timed questions, instant score, full review of wrong answers. Free.
🎯 Take the Practice Exam →