Building Simple LLM Applications
APIs, chat flows, memory, orchestration, and developer patterns
APIs, chat flows, memory, orchestration, and developer patterns
Modern LLM applications use a combination of APIs, prompt design, chat flows, memory components, and orchestration layers. Understanding these fundamentals makes it easier to build apps that reason, remember, and follow user intent.
Connect to LLMs for text generation, embedding, structured responses, and tools.
Define turn-by-turn interactions between the user, system prompts, and model outputs.
Short-term and long-term storage for context, improving continuity and personalization.
Combining models, tools, and logic into structured pipelines or agent flows.
Reusable techniques such as RAG, tool calling, guardrails, and modular prompts.
User asks a question or triggers an action.
Prompts, rules, and context assembled.
Model generates reasoning or results.
Store relevant info in short‑term or long‑term memory.
Cleaned, formatted response sent back to user.
Adaptive agents that remember user preferences.
RAG systems for summarization, search, and analysis.
Tools that call APIs, take actions, and orchestrate workflows.
Only if your app has multiple steps, tools, or memory components.
Simple apps rely on context windows; advanced apps use vector or structured memory.
Use direct LLM API calls with a clean system prompt and user message.
Use simple patterns first, then scale to memory and orchestration.
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