Building Simple LLM Applications

Overview

Simple LLM applications revolve around connecting to language model APIs, structuring input/output flows, adding memory, and orchestrating logic. Developers use standard patterns to keep workflows predictable and maintainable.

Key Concepts

LLM APIs

Connect using standard REST or SDKs. Send prompts, retrieve responses, adjust parameters.

Chat Flows

Structure messages as system, user, and assistant. Manage turns for predictable output.

Memory

Maintain context through conversation history, embeddings, or external storage.

Orchestration

Control flow: routing, conditions, multi-step reasoning, chaining, or tool calls.

Developer Patterns

Templates, modular prompts, retry logic, monitoring, and evaluation loops.

Typical Development Process

Define the task and user interaction pattern.

Design prompts and message roles for the chat flow.

Add memory if needed through conversation history or vector stores.

Orchestrate tool calls, routing, or multi-step logic.

Test, refine prompts, and evaluate outputs.

Common Use Cases

Chatbots and support assistants
Document summarization tools
Internal automation via LLM-driven workflows
Content generation and editing tools
Search, Q&A, and knowledge assistants

Basic vs. Orchestrated LLM Apps

Basic Apps

Single prompt → single response
Minimal memory
Ideal for simple tasks

Orchestrated Apps

Multi-step workflows
Dynamic routing and tool calls
Advanced memory and evaluation loops

FAQ

Do I need memory for all LLM apps?

No, simple tasks work without it. Use memory only when context matters.

What tools help with orchestration?

Frameworks like LangChain, LlamaIndex, and custom routing logic in your backend.

Are chat flows required?

For interactive apps, yes. For one-off tasks, a single prompt works.

Start Building Your LLM App

Use these patterns to create simple, powerful AI-driven experiences.

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