Intro to LLM App Development
Grasp the basic structure of apps embedding LLMs (chat, assistants, automation). The difficulty isn't "calling the model" but designing on the premise of uncertain output.
Typical Architecture
- Input: user input + context (history, data)
- Prompt construction: system instruction + context + user input
- Model call: request to the API (streaming support)
- Post-processing: verification, formatting, tool execution, saving
Decide First
- Use (Q&A / generation / agent) and the accuracy needed
- Model selection (performance, cost, latency, data terms)
- Failure behavior (fallback, human intervention)
As of August 2026 the "smart but pricier" large models are OpenAI's GPT-5.6 (a three-tier Sol / Terra / Luna line) and Anthropic's Claude Opus 5. What matters is less memorising a model number than knowing that each provider offers both a smart tier and a fast, cheap tier.
Design Principles
- Premise: don't trust output; always add verification and guardrails
- Validate input at boundaries: prompt-injection defense (later chapter)
- Measure cost and latency from the start
This Chapter's Flow
It continues with API/SDK, framework selection, auth/billing, and operations. The standard is to proceed in the order "run small → measure → defend."
