Building Simple LLM Applications
Understand how APIs, chat flows, memory, orchestration, and developer patterns come together to create powerful language‑model applications.
Get Started
Understand how APIs, chat flows, memory, orchestration, and developer patterns come together to create powerful language‑model applications.
Get Started
Simple LLM applications typically follow predictable building blocks: calling model APIs, structuring chat flows, adding memory when needed, and orchestrating the logic that links everything together. This page breaks down these elements in an easy‑to‑understand way.
APIs provide access to models via simple inputs and structured outputs. Common operations include prompts, completions, and streaming responses.
Chat interactions follow a message‑based pattern. Developers control system instructions, user messages, and model outputs to shape behavior.
Memory allows models to keep track of previous conversations or context, ranging from simple local history to vector‑based retrieval.
Logic that coordinates prompts, tools, retrieval, routing, or multi‑step workflows. Frameworks often help simplify orchestration.
Clarify what the model should accomplish.
Plan messages, instructions, and roles.
Determine if context persistence is needed.
Connect all components and handle edge cases.
Flow‑driven chat experiences with memory for history.
Personalized learning experiences using retrieval.
Combine model reasoning with tools or APIs.
Simple, fast, stateless. Great for single‑shot tasks.
Multi‑turn interactions. Medium complexity.
Most capable. Includes memory, agents, and workflow logic.
No. Many apps work fine without memory. Add it only when multi‑turn context is required.
Start with a single API call using a well‑structured prompt or simple chat format.
Use them when your app involves multiple tools, retrieval, or multi‑step workflows.
Use these foundations to create intelligent, flexible, and powerful AI experiences.
Begin Now