LLM Applications
Now that we have an LLM, how do people actually build products with it?
Build systems around a fixed language model using prompting, retrieval, tools, memory, agents, evaluation, and reliable product architecture.
1. Prompt Engineering
Design prompts from first principles using token prediction, context, and task framing
2. Retrieval-Augmented Generation
Ground model outputs with retrieval, embeddings, chunking, and re-ranking
3. Tool Use and Function Calling
Let models call external tools, APIs, databases, and structured functions
4. Memory
Store and retrieve user, task, and application state across interactions
5. Agents
Build model loops that plan, act, observe, and revise
6. Multi-Agent Systems
Coordinate multiple model roles, workers, critics, and supervisors
7. LLM Application Evaluation
Measure LLM application quality with tests, rubrics, model judges, and human review
8. Building Reliable AI Systems
Turn LLM prototypes into production systems with reliability, observability, guardrails, latency, and cost controls