Building Simple LLM Applications
APIs, chat flows, memory, orchestration, and developer patterns
APIs, chat flows, memory, orchestration, and developer patterns
Simple LLM applications are built from predictable components: an API call to a model, a chat-like interaction pattern, lightweight memory, and a minimal orchestration layer. Slide 74 highlights the essential structure developers follow when creating functional but simple LLM‑powered tools.
Call the LLM with prompts, messages, or structured inputs.
Build turn-based interactions that feel conversational.
Maintain context using short-term buffers or vector stores.
Coordinate API calls, logic, and routing between steps.
Structure code for clarity, reuse, and predictable behavior.
Collect user input in a chat UI or function call.
Pass the message into a model API with optional context.
Apply memory (short-term or retrieval-based) if needed.
Handle the response and manage follow-up steps.
Update UI or return structured output to the user.
Conversational tools with short-term session memory.
Send text to LLMs and receive structured outputs.
Automated tasks triggered by user queries.
Combine LLM reasoning with document lookup.
LLMs interpret user input and populate structured forms.
No. Many simple apps only need short-term chat history.
Yes. Even one well‑designed model call can achieve strong results.
When your app requires multiple steps, tools, or condition-based logic.
Simple patterns let you build fast and scale later.
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