Building Simple LLM Applications

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

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Overview

Learn how simple LLM applications are structured, the API-driven flow behind them, how memory enhances conversational quality, and how orchestration patterns bring everything together.

Key Concepts

LLM APIs

Core interface for prompting, generating responses, and building features around model outputs.

Chat Flows

Defines input-output cycles, roles, and conversational structure.

Memory

Short-term or long-term storage that improves coherence and context retention.

How Simple LLM Apps Work

1. User Input

Collect the question or instruction.

2. Preprocessing

Optional cleanup, formatting, or metadata addition.

3. LLM API Call

Send request to the model with context and memory.

4. Response Assembly

Return or display the generated output.

Use Cases

With vs. Without Orchestration

Without Orchestration

  • Single prompt → single response
  • No memory
  • Simple but limited

With Orchestration

  • Multiple step pipelines
  • Memory injections
  • Tools, functions, chaining

FAQ

Do I need memory for every LLM app?

No, only for multi-step or context-heavy tasks.

Is orchestration required?

Not for simple prompt-response flows, but essential for complex apps.

Which API should I use?

Any provider that fits your model quality and pricing needs.

Start Building LLM Applications

Use APIs, memory, and orchestration to create powerful intelligent apps.

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