Building Simple LLM Applications

Overview

This guide introduces the essential components of simple LLM applications, from basic API usage to chat flows, memory strategies, and orchestration patterns used in modern AI-powered systems.

Key Concepts

API Calls

LLMs are accessed via REST or streaming APIs. Developers craft prompts, set parameters, and handle responses.

Chat Flows

Multi-turn interactions enable conversational apps, requiring structured input-output handling.

Memory

Maintains context across messages, using short-term buffers or long-term vector retrieval.

Orchestration

Manages pipelines, tool calls, validations, and structured output formats to ensure reliable behavior.

Process for Building an LLM App

1. Define Task

State what the model must achieve.

2. Design Prompt

Provide clear instructions and constraints.

3. Add Memory

Choose buffering or retrieval.

4. Orchestrate

Use tools, validations, and structured output.

Use Cases

Customer support chatbots
Interactive tutoring systems
Document assistants and summarizers
Internal workflow agents
Search and retrieval interfaces

Approaches Compared

Simple API Calls

Fast to implement; good for single-shot tasks.

Full Chat Orchestration

Best for complex workflows requiring tool use and memory.

FAQ

Do I always need memory?

No. Many tasks work with single-turn prompts.

Should I use retrieval?

Use it when information exceeds prompt limits.

When is orchestration needed?

For multi-step workflows requiring tools or structured outputs.

Start Building Your LLM App

Leverage APIs, memory, and orchestration to create intelligent experiences.

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