Building Simple LLM Applications
APIs, chat flows, memory, orchestration, and developer patterns
APIs, chat flows, memory, orchestration, and developer patterns
Simple LLM applications rely on predictable building blocks: prompt calls to APIs, structured chat flows, optional memory for continuity, and orchestration patterns that help developers build scalable and maintainable systems.
Most applications are built on simple request-response interactions using model APIs.
Multi-message conversations help structure interactions predictably.
Store conversation state or domain-specific data to maintain context across calls.
Manage multi-step workflows, tool usage, and logic branching.
Templates, function calling, and modular architectures streamline development.
Systematic validation ensures correctness and reliability of model outputs.
User messages, system prompts, and requirements.
Send prompt to the LLM and receive structured output.
Inject previous context or stored data.
Chain steps, tools, and conditional logic.
Deliver final answer or next action.
Use memory and flows to guide customers with personalized responses.
Multi-step orchestration for tasks like code generation or data extraction.
Augment LLMs with stored domain-specific memory and retrieval.
Map user input to structured data reliably using templates and tools.
No, many applications work with stateless prompts.
Use it when multiple steps or tools are involved.
Not for most applications—simple flows often suffice.
Use simple APIs and patterns to prototype powerful AI features quickly.
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