Building Simple LLM Applications

Overview

Learn how to assemble core components of LLM-driven apps including API calls, conversational flows, memory handling, orchestration logic, and reusable developer patterns.

Key Concepts

APIs

Use REST or SDK layers to call LLMs and structure prompts programmatically.

Chat Flows

Design turn‑by‑turn conversation logic and state transitions.

Memory

Maintain short‑term and long‑term storage for user context and conversations.

Orchestration

Coordinate multiple tools, models, and workflows using decision rules.

Developer Patterns

Follow reusable templates for chaining calls, injecting context, and structuring LLM logic.

Process for Building an LLM App

1

Define user goal

2

Design prompt & API structure

3

Add chat flow logic

4

Integrate memory

5

Orchestrate tools & finalize

Common Use Cases

Customer Support Agents

Research Assistants

Form‑filling Automation

Data Extraction Bots

Comparison: Simple vs Advanced LLM Apps

Simple Apps

Single prompt or chat
Minimal memory
Linear flows

Advanced Apps

Multiple tools & models
Rich memory and history
Dynamic orchestration

FAQ

Do I need a framework?

No. You can start with raw API calls and add structure over time.

How important is memory?

Essential for multi-step workflows and personalized chat experiences.

Should I use multiple models?

Only when each model serves a distinct purpose.

Start Building Your LLM Application

Use simple patterns now and expand into advanced tooling later.

Begin Now