The AI Stack: A Practical Guide to Building Your Own Intelligent Applications
This article provides a practical, architectural blueprint for the modern AI stack, guiding developers on how to move from being consumers of AI-powered products to builders of intelligent applications.
Why it matters
Understanding the AI stack and the different approaches to accessing models is crucial for developers looking to build their own intelligent applications and move beyond being just consumers of AI-powered products.
Key Points
- 1Deconstructing the AI application into four key layers: the Model Layer, Orchestration Layer, Data Layer, and Application Layer
- 2Exploring three options for accessing AI models: Cloud API, Open Source Models, and Custom Models
- 3Discussing the trade-offs between control, cost, and complexity for each model access approach
- 4Highlighting the importance of the Orchestration Layer in managing interactions with models and sequencing complex tasks
Details
The article starts by acknowledging the hype around AI and the fundamental question for developers: how to actually use this technology and build intelligent applications. It then provides a practical, architectural blueprint for the modern AI stack, breaking it down into four key layers - the Model Layer (the actual AI model), the Orchestration Layer (managing interactions with models and sequencing tasks), the Data Layer (storing and retrieving knowledge), and the Application Layer (the user interface). The article then explores three primary paths for accessing AI models: Cloud API (the fastest start but with trade-offs like ongoing cost, data privacy concerns, and vendor lock-in), Open Source Models (the flexible path of running models locally or on your own infrastructure), and Custom Models (the most control but also the most complex). The article uses a
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