Building Simple LLM Applications

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

Overview

Simple LLM applications are built by combining a model API with structured flows, memory handling, and orchestration logic. Developers rely on repeatable patterns to ensure reliability, clarity, and good user experiences.

Key Concepts

API Integration

LLM APIs provide text generation, embeddings, and tools for building intelligent capabilities.

Chat Flows

Structured message exchanges maintain context and help shape predictable AI responses.

Memory

Short-term and long-term memory store user data, improving personalization and coherence.

Orchestration

Logic, routing, and decision layers coordinate model calls and system behavior.

Process

1. Define Inputs

User prompts, context, system rules.

2. Add Memory

Store or retrieve relevant facts.

3. Orchestrate Calls

Manage workflow and logic branches.

4. Return Output

Deliver actionable answers.

Use Cases

Comparison

Traditional Apps

  • Procedural logic
  • Deterministic outputs
  • Manual rule creation

LLM Apps

  • Natural language interface
  • Adaptive reasoning
  • Reduced rule-writing

FAQ

Do I need memory for every app?

No, only if context persistence improves responses.

What API features matter?

Models, embeddings, structured outputs, and rate limits.

Is orchestration required?

It’s optional but helpful for multi-step reasoning.

Start Building Your LLM App

Use APIs, workflows, and memory to create powerful experiences.

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