App Building Guide — Overview
1. Introduction
Most AI apps don’t fail because of the model. They fail because they’re built wrong from day one.
2. The Problem
Teams jump into:
prompts
UI generation
quick prototypes
They end up with:
fragile behavior
inconsistent outputs
apps that break when connected to real systems
👉 “It works in demo” is not the same as “it works in production.”
3. What This Guide Solves
This guide shows you how to:
build AI apps that behave predictably
connect them to real systems
move from prototype → production safely
4. The System (Builder Framework)
The methodology:
Think — define business context
Prompt — structure interactions correctly
Build — iterate in controlled steps
Connect — integrate with systems and data
Govern — enforce rules and reliability
5. Why we built Peridot
“We’ve seen this across teams: they can generate apps instantly, but they can’t make them reliable once real data and workflows are involved.”
6. Start With Context (Critical)
Before writing a single prompt:
Define:
the problem
the workflow
the inputs and outputs
👉 If this is wrong, everything downstream breaks
7. Prompting Layer
Learn:
how to control behavior
how to avoid ambiguity
Also:
why “just prompting better” isn’t enough
8. Build Phase
Execution rules:
👉 This is what prevents fragile systems
9. Integration Layer
AI apps are useless in isolation.
Connect them to:
APIs
data systems
internal tools
👉 This is where real value is created
10. Production Transition
Move from:
prototype → governed application
This includes:
reliability
consistency
control over behavior
11. Safety & Control
Build with:
data awareness
controlled flows
enforcement mechanisms
👉 Safety is not a feature — it’s a requirement
12. What’s Next
After this guide, you should be able to:
build AI apps that actually work
connect them to production systems
control how they behave over time
Start with: