Contextual Information Access in the Age of AI
Luc Beaudoin: CogZest
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Contextual Information Access in the Age of AI<br>Search finds information. RAG retrieves information for AI. Contextual information retrieval accesses information for you.
Luc Beaudoin: CogZest<br>Jul 03, 2026
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Search has largely solved the problem of finding information. AI has exposed a different problem: how to retrieve the information that matters because of what you are currently working on.<br>Every research project now accumulates an expanding constellation of PDFs, AI conversations, notes, email threads, meeting notes, transcripts, datasets, web pages, code, diagrams, drafts, tasks, prompts, and AI-generated reports. The challenge is no longer merely finding one of these resources. It is retrieving the information that is relevant because of the current context, which is typically defined by the foreground resource: the paper, draft, email, task, AI chat, or other item that currently has your attention.<br>I call this contextual information retrieval. More than a decade ago, in my book Cognitive Productivity: Using Knowledge to Become Profoundly Effective, I introduced the closely related concept of the meta-access problem: the problem of efficiently accessing information because of its relationship to the information you are currently viewing. Generative AI has now made that problem much more important.<br>From information access to contextual information retrieval
Suppose you were reading an important research paper yesterday and now need to get back to it. Traditional information retrieval asks, “How do I find this paper?” That is what Google Scholar, Spotlight, email search, file search, and reference-manager search are good at.<br>Contextual information retrieval begins after you have found the paper. It asks, “Now that I am looking at this paper again, what else should be immediately available because it belongs with this paper?” That might include notes (in the app of your choice), ChatGPT conversations, Claude Projects, Gemini Deep Research reports, NotebookLM notebooks, Perplexity searches, DEVONthink records, Bookends or Zotero references, OmniOutliner outlines, meeting notes, transcripts, datasets, code, email discussions, grant proposals, entries in your issue tracking system, tasks, calendar events, manuscripts, diagrams, glossaries, and related papers.<br>These resources are not necessarily connected by keywords alone. They are connected because they participated in the same line of thought, research process, design problem, collaboration, or project. That is why contextual information retrieval is different from ordinary search.<br>Three complementary kinds of retrieval
Modern knowledge work depends on at least three complementary kinds of retrieval.<br>Information retrieval answers the question, “Where is this document?” Typical tools include Google, Spotlight, email search, Finder search, and reference-manager search.<br>Semantic retrieval answers the question, “What documents discuss this topic?” Typical tools include AI search, vector search, semantic search, and embedding-based search.<br>Contextual information retrieval answers the question, “Given what I am working on right now, what other resources belong with it?” One important mechanism for this is deep relationship retrieval: retrieving resources because of their deep relationships to the current foreground resource.<br>These three forms of retrieval complement one another. None replaces the others. Search finds documents. Semantic retrieval finds conceptually related materials. Deep relationship retrieval finds the web of resources surrounding the thing you are currently working on.<br>Where RAG fits
One of the most important ideas in modern AI is retrieval-augmented generation, or RAG. A RAG system retrieves relevant information before generating an answer. That is enormously valuable, but it solves a different problem.<br>RAG asks, “What information should an AI retrieve before answering this question?” Contextual information retrieval asks, “What information should I be able to retrieve because of what I am currently working on?” Put differently, RAG retrieves information for an AI, whereas contextual information retrieval retrieves information for a human.<br>These are complementary capabilities. As AI becomes more powerful, both become more important. AI systems need better retrieval to generate better answers. Humans need better contextual retrieval to manage the growing information ecosystems that AI helps create.<br>Every foreground resource has an information ecosystem
Every foreground resource — a paper, proposal, email, software issue, AI conversation, legal document, presentation, or draft — has an information ecosystem. That ecosystem includes everything that explains it, challenges it, extends it, implements it, summarizes it, cites it, depends on it, or resulted from it.<br>For example, a manuscript may be connected to AI chats, meeting notes, transcripts,...