Building Simple LLM Applications

APIs, chat flows, memory, orchestration, and developer patterns

LLM Slide 78

Overview

Simple LLM applications revolve around predictable patterns: calling APIs, managing chat interactions, storing memory, orchestrating tool calls, and using reusable developer patterns. This page summarizes the fundamentals for designing functional, maintainable AI-powered workflows.

Key Concepts

APIs

Core interface to send prompts and receive responses from language models.

Chat Flows

Structured message sequences forming interactive conversations.

Memory

Short-term or long-term retention that improves contextual understanding.

Orchestration

Coordinating models, tools, and data to accomplish tasks.

Developer Patterns

Reusable strategies: loops, agents, routing, tool calling, and pipelines.

How It Works

1. Define Inputs

User message, system rules, data.

2. Call LLM API

Send structured messages via API.

3. Apply Logic

Memory, tools, or branching flows.

4. Return Output

Display text, actions, or structured results.

Common Use Cases

Chat Assistants

Conversational agents with memory and context.

Data Extraction

Structured JSON results from unstructured text.

Automated Tools

Models orchestrating APIs and code execution.

Simple vs. Advanced LLM Apps

Simple Apps

  • Single API calls
  • Linear chat flows
  • Optional short memory
  • Minimal logic

Advanced Apps

  • Tool calling and plugins
  • Dynamic routing
  • Long-term memory systems
  • Multi-agent orchestration

FAQ

Do I need a framework?

Not for simple apps; raw APIs are enough.

How do I add memory?

Store previous messages or use vector search for retrieval-augmented memory.

When do I use orchestration?

When the model needs tools, logic, or branching workflows.

Start Building LLM Apps Today

Use simple patterns to create powerful AI-driven workflows.

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