About this Library
The information contained here comes from academic research, books, blog posts and other web information, product documentation, live products, and personal experience. You are getting a distillation of this diversity of sources into meaningful pieces. The goal is to create a pattern language for conversational AI, one that captures central features of conversation, organises it into easy to digest pieces, and facilitates leveraging the patterns for quality conversation design.
What was it like making this document? Much of the academic literature is relevant, but tangentially. The public documentation and blog posts come with insight into the interface, but also motivated by the author’s and company’s needs. This is not an easy project. It’s also not a project that is ever really done in the same way that language and conversation never really stops changing. But within that forest of variability, we hope to have captured the pieces that can guide us.
The idea of a pattern language can be traced back to the architect, design-theorist, and professor at UC Berkeley, Christopher Alexander. In multiple books and essays, Alexander laid out his vision for what ails modern urban design, and how a pattern language could address some of those concerns. Simply, many designed spaces have a top-down sterility to them, because they impose a structure that does not reflect the vitality and energy of lived-in spaces. To capture that realness, Alexander wanted to observer how spaces evolve in living human ecosystems and what systematicity is inherent there. He captured this systematicity in a book named A Pattern Language.
This pattern language approach was later extended to software engineering and object-oriented programming in the book Design Patterns by Gamma et al. The book described common ways that software engineering gets done, collects those into a library of types, and presents them for others’ benefit. More recently, Moore and Arar, in the book Conversational UX Design, move towards doing this within the space of designing conversations with bots. Their proposed framework is inspired largely by the academic literature of the field of Conversation Analysis, and implemented within IBM.
This document elaborates on the mission of these prior efforts, focused on conversational AI, drawn from many sources, and focused on important patterns for the design of conversational experiences. It can help you see more, and achieve more more easily.
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