Getting Started - Build AI Platforms From Scratch #1

Source: Dev.to

Build AI Platforms from Scratch

Learn to build powerful AI‑powered applications and platforms from scratch. This course focuses on design, architecture, and engineering, NOT coding.

📺 View this module with video & slides

Before We Get Started

NOTE: This is NOT a coding course. The series strictly focuses on design, architecture, and engineering choices involved in building an AI platform/app.

• A GitHub repo with common coding patterns will be provided for reference.
• The course is aimed at beginners who want to leverage LLMs and other AI technologies in ambitious, complex projects (multi‑layered AI architectures, data transformations through LLMs, agents, ambient intelligence, etc.), rather than simple chatbots.

Key Terms & Definitions

• LLM (Large Language Model) – An AI trained on massive text corpora that can understand and generate human language.
• Agents – AI systems that can take actions and make decisions autonomously to accomplish goals.
• AI Architecture – The way you structure and organize the different components of your AI system to work together.
• Data Transformation Through LLMs – Using AI to convert information from one format to another (e.g., turning messy user input into structured data).
• Prompt Engineering – Writing instructions to AI models to obtain reliable, useful outputs.
• Tech Stack – The collection of technologies and tools you use to build your platform (languages, frameworks, databases, etc.).
• IDE (Integrated Development Environment) – The software where you write your code.
• MToks (Million Tokens) – Standard unit for measuring large‑scale API usage (1 MTok = 1,000,000 tokens).
• APIs (Application Programming Interfaces) – How your code communicates with external services like AI providers.
• Industry Benchmarks – Standard metrics and performance expectations for AI systems in production.
• Input/Output (in LLM APIs) – Input = what you send to the AI; Output = what it generates back (output typically costs more).
• Context Windows – The total amount of text an AI model can process at once, including instructions and its response.
• Tokens – The basic units AI models use to read and generate text (≈ 0.75 words per token).
• AI Hallucination – When AI generates plausible‑sounding information that is actually incorrect or fabricated.
• Systems Thinking – Designing platforms as interconnected parts rather than isolated features, focusing on component interactions.

Course Specs

• Comprehensive understanding of what building an AI platform entails.
• Actionable steps to develop your own AI‑powered application.
• Real‑world examples of AI platforms and how they were built.
• Prompt Engineering for complex projects (modularity, inputs/outputs, preventing bad behavior, etc.).
• Design and user‑experience considerations.
• Multi‑layered AI workflows and data transformations.
• Tracking AI usage and limiting tokens while maintaining accurate responses.

Tech Stack

Frontend (Web): Typescript/Vite & Tailwind
Mobile: Flutter
Backend: Go (Gin)
Database: PostgreSQL, GORM
LLM API: Anthropic, OpenAI (backup), Mistral (simple tasks)
TTS API: OpenAI TTS
IDE: Cursor
Cloud Hosting: Digital Ocean
Image & Video Hosting: Cloudinary

NOTE: Architectural concepts apply regardless of stack.

Structure of Course Topics

1. Introduction/definition of a common issue or foundational concept.
2. Real‑world story involving this topic (from my projects or a well‑known platform) and its resolution.
3. Decision‑making process breakdown and specifics that should inform the build.
4. Major takeaways and how to apply them to your own project.

My Projects

Emstrata

A text‑based emergent storycraft simulator where stories unfold like lived experiences. Participants make choices that shape narrative worlds; AI systems maintain continuity and consistency. Users can interact with the simulation—altering mid‑flight, navigating AI‑generated planes, making inquiries, and correcting errors.

PLATO5 (mid redesign)

An AI‑first social engine designed to generate real‑world friendships, not screen time. It matches people based on personality (Big 5 traits), interests, and location, facilitates conversations through Zen—an AI chat manager—and guides users toward planning actual meetups, aiming to get people off the app and into real life.

Why Build AI Platforms

• Novel applications often require architecture designed specifically for the problem.
• Complex AI applications need precise control over prompt structure, state management across multiple AI calls, and data flow between system components.
• Building from scratch lets you optimize every layer for your use case—from API calls to edge‑case handling to cost management at scale.
• Architecture choices define what’s possible; e.g., Emstrata’s multi‑layer narrative system works because the platform is built around maintaining continuity and tracking story threads.

The Cost Reality of AI Apps

• AI API usage: Measured in tokens (MToks). Different models/APIs have varying charges and capabilities.
• Input vs. Output costs: Output tokens usually cost more; large responses increase expenses, especially with larger context windows.
• Context window size: Affects both price and performance. Managing token count and organizing inputs efficiently is crucial.

Apps that Evolve

• Hallucinations: AI can generate plausible‑sounding but incorrect information. At scale, hallucinations can have exponential downstream effects, especially when architecture relies on accurate AI responses. The course covers strategies to hedge against or creatively embrace these issues.

(Content truncated in the source; further details continue in the full module.)