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Api integration

As explained in The Art of AI Product Development

API integration is the incorporation of functionality or data from a software service through its application programming interface.

API integration incorporates functionality or data from a software service into an application through its application programming interface. In AI products, it connects pretrained models and existing components to user-facing systems, workflows, and interactions. section 12.1.2

AI engineering commonly uses API integration to:

AI-powered product integration

A team can begin with pretrained models and existing components, using tools such as the OpenAI API, LangChain, Hugging Face, or vector databases before considering custom model training or fine-grained optimization. section 12.1.2

How The Art of AI Product Development covers it

How deep it goes
Positions integration as a practical AI-engineering task but stops short of detailed implementation code or deployment procedures.
Examples
Uses the OpenAI API, LangChain, Hugging Face, vector databases, production systems, and AI-powered workflows as concrete integration contexts.
What it assumes
Assumes software-development ability and distinguishes integration work from machine-learning model training and optimization.
What you can do after
Apply it via named AI tools to build production applications and optimize model interactions.
How it connects
Links API integration with AI engineering, pretrained models, prompt engineering, model deployment, and user experience.

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Related concepts

This explainer was generated by Manning's knowledge graph from the text of The Art of AI Product Development. The section links point to the parts of the book it draws on.