Building Simple LLM Applications

Overview

Simple LLM applications rely on a predictable pattern: calling APIs, handling chat flows, managing state or memory, orchestrating multiple steps, and following consistent developer best practices. This foundation makes it easier to build robust AI-powered applications.

Key Concepts

APIs

The base interaction layer for models, enabling request–response patterns to generate output.

Chat Flows

Controlled conversation loops simulate multi-turn interaction and context continuity.

Memory

Stores user data or conversation state to allow personal, contextual, or long-term interactions.

Orchestration

Coordinates multiple model calls, tools, or actions into a coherent workflow or pipeline.

Developer Patterns

Templates for structuring prompts, flows, and reliability tactics that scale across apps.

How LLM Applications Work

1

Input

User query or system event triggers the model flow.

2

Preprocessing

Optional normalization or context gathering.

3

Model Call

An API call to the LLM generates a response.

4

Postprocessing

Filtering, formatting, or additional logic applied.

5

Action / Output

Final message or action returned to the user or system.

Common Use Cases

Chatbots

Customer support, tutoring, interactive experiences.

Automation Agents

Task-oriented workflows like scheduling or research.

Data Assistants

Summaries, extraction, classification, and insights.

APIs vs Orchestration

Simple API Calls

- Single request–response
- Low complexity
- Good for basic prompts

Orchestration Pipelines

- Multi-step flows
- Tool usage & memory
- Supports advanced agents

FAQ

Do I need memory in every LLM app?

No, only when personalization or context retention is essential.

What is the simplest architecture?

A single prompt + API call + formatted output.

When should I use orchestration?

Whenever your app requires multiple steps, external tools, or dynamic decisions.

Ready to Build Your LLM App?

Start experimenting with APIs and workflows today.

Get Started