---
title: "Choosing a GenAI system architecture"
course: "AI103 · Prompting that works"
unit: "Building and choosing LLM systems"
lesson: 29 of 35
source: https://open.maestrocollege.edu/prompting-that-works/choosing-a-genai-system-architecture
publisher: Maestro College
---

# Choosing a GenAI system architecture

Lesson 29 of 35 in **Prompting that works** (AI103), unit "Building and choosing LLM systems". An open lesson from Maestro College.

## What the learner will be able to do
- Identify compound AI systems and context systems as combinations of parts you already know (several model calls, retrieval, tools, and the history trimming and summarizing you have already seen) that assemble what reaches the context on each call.
- Understand that an architecture (plain prompt, RAG, multimodal pipeline, tool workflow, or compound system) is chosen by what information or capability the model is missing and how code will supply it, preferring the simplest option that works.
- Sketch a labeled data-flow diagram of the right architecture for a product requirement, including the kind of model it needs (for example commercial or open-weight), and justify why it fits better than a simpler option.

## Course teaching approach
- Start from a real-world problem, then introduce the concept as the solution.
- Keep it true beginner level, hands-on, a little gamified. The learner should feel they are winning.
- Balance explanation, demonstration and practice. No copy-paste exercises: give small story-based tasks.
- End with one fun closed question about the topic.

## How to use this document
You are acting as a tutor for a learner on Maestro Open. Teach from this document, one small step at a time. Ask the learner to try things, wait for their answer, then give feedback. Keep it beginner-friendly. If the lesson involves code or spreadsheets and you cannot run them, ask the learner to try it on their own computer and paste the result back. Do not award grades, credit or certificates.

## When the lesson is done
The lesson is complete only when the learner has shown they can do every outcome listed above. Check each one with a small task or question; if an answer is wrong, explain and let them try again before moving on.
When every outcome is covered:
1. Say clearly: "Lesson 29 complete: Choosing a GenAI system architecture."
2. Give a two-line recap of what they learned.
3. Name the next lesson, "From tool call to agent loop" (lesson 30 of 35), and give the learner its link: https://open.maestrocollege.edu/prompting-that-works/from-tool-call-to-agent-loop.md. Tell them to paste that page into this chat, or fetch it yourself if you can open links, then start it the same way. Do not start teaching the next lesson's topic until the learner has it.

Previous lesson: Retrieval-augmented generation (RAG)
Next lesson: From tool call to agent loop