As content becomes increasingly fragmented and modularized, its context gets lost. Many people advocate for understanding the context in which content is used, but they have different ideas about what the context of content represents. Current technological approaches to managing content pieces don’t address the full range of contextual dimensions.
Distinguish the delivery and the discovery contexts
Much discussion of the content context revolves around what the user is seeking. The concern is to get the right content to the user when they seek it. I refer to this as the delivery context.
The delivery context is about matching what is known about the user with the dimensional variables of the content. For example, we ideally want to know who the user is, what they know and have done already, their goal, and so on. With that information, a publisher can select appropriate content according to the topic, level of detail, formats, and perhaps even messaging.
An entire industry of content orchestration has emerged to develop insights and practices to address the user’s delivery context. It’s by no means a simple problem to solve, but it is one that promises great profits to marketers and others who want to push content to customers.
But another contextual dimension of content gets less attention: the discovery context. Users aren’t always waiting for the right content to find them. In many cases, a stream of predecided content is antithetical to what they seek. They need to define their own journey to discover what they should know more about.
Discovery supplies the context of meaning
Content does not always convey its meaning clearly, especially when statements are skimmed or read in isolation. Users may not understand something about the content, or they may read meanings into the content that aren’t explicitly stated. The discovery context concerns what users may want to know beyond the statement seen.
As content is increasingly decontextualized — appearing as snippets rather than as long-form articles — users must discover the missing context themselves. They must supply explanations beyond what is conveyed by the string of text.
The discovery context consists of three dimensions:
- The context of collective understanding
- The authorial context
- The associative context
Collective understanding is the layer of knowledge the author might presume the reader knows, whether or not that is true for a specific individual reader. A statement might refer to people, places, dates, metaphors, or other things that are not described, only referenced. The reader is expected to understand these items and look them up if they don’t.
The authorial context refers to the broader context from which a statement was lifted. Authors can be quoted out of context. Bots pull snippets or will paraphrase the source material. The danger is that such text selections end up vague or misleading.
Even when a text snippet is an honest summary of what the author wished to convey, it may not be clear what point they were trying to make with the statement. Was the statement a claim or assertion, a warrant or evidentiary rationale, or the grounds or justification for their argument? In other words, what was the role of the statement, and what did the author assume the reader already knew when they made it?
The associative context concerns the broader context in which the user evaluates statements. What else is similar to this statement? How does it fit together with other statements?
The associative context becomes important as users rely on content that’s abstracted from its original source. They utilize snippets of content that have been compiled by others or by themselves. The associative context is a defined layer of curation, collecting related items together. Such curation provides meaning to users by allowing them to recontextualize the content fragments.
A simple example of an associative context is the highlights from a book. These highlights can be kept together to recall the key points of a book, but they can also be combined to compare how different books and authors discuss similar or related topics. The snippets by themselves convey limited information, but collectively they tell much more.
Discovery remains undersupported
Although there is an evident trend in content toward providing direct “answers” to users, it is also clear that such approaches can’t be “zero-shot” ones, where users must settle with the predefined answer the bot offers. Digital content is becoming more conversational and dialogical, allowing users to ask follow-up questions and get clarification.
Bots offer opportunities for discovering information, but the situation currently remains fragmented. Generative AI seems split into global solutions that can supply information relating to collective understanding (but not specific sources), and localized solutions that can answer questions about specific sources (but not general knowledge). To a large extent, the split seems driven by vendor advocacy of preferred models and technologies (big vs small models, and KG vs vector RAG) — debates that interest only engineers, not ordinary users.
Users have limited opportunities to build their own knowledge base and define their own associative context with these tools, which largely lack memory of what users have told them in the past.
Rather than expect a single technical solution to solve everything, we would be better served by having the freedom to compose our own suite of tools. Ebook devices provide one inspiration: they allow users to add their own dictionaries, notes, and highlights, and export snippets elsewhere. Google’s NotebookLM paradigm also points to ways to bridge local and global capabilities. Eventually, we may have many AI capabilities built into our browser.
Personal knowledge management may eventually succeed the list-making technologies of personal information management. Before we feel comfortable delegating tasks to AI agents, we will need to be confident we understand what we want and what’s available.
— Michael Andrews