Saving is a fundamental action for those who engage deeply online. Sometimes, it is the entire thing that gets saved by curators. But more often what's saved is:

• A reference to the thing

• Information concerning what the thing is

• Insight into what the thing means to the saver

• Some metadata surrounding the thing and its saving

Unsurprisingly, better saving makes for better sharing and search.

A tale of two songs

Consider two people that have saved a song they like. One has a functioning reference to a primary listing of the track, has annotated it as a jazz audio recording by a specific band, has labelled it as a "song to cry to", and has captured the datetime of the capture. The other has saved a raw link to a social media post that contains a snippet of the same recording posted by one of the band's unofficial fan groups.

The former is much more likely to share the song appropriately with others—perhaps using a group chat with friends focused on music, or to a local community for music events. The latter will likely either never share the saved thing, or coarsely broadcast it to their main social media feed. The former is much more likely to be able to find the saved thing in the future because they can use a range of hooks to fish it out—from the metadata to the song genre or the representation of meaning it evoked. The latter is unlikely to be able to find it in the future—and if they do it'll based on a combination of remembered vibes plus unstructured scrolling and arbitrary keyword searches on what may or may not be the right platform.

This dynamic plays out with all types of things, from the songs we save to the people we meet, from the places we visit to the various textual forms we interact with online. And the trend in recent years has been, in response to this dearth of information available online, for more people to use and more systems to facilitate good saving practices. Two of the biggest enablers of this trend have been tagging and zooming.

To tag and to zoom

Tagging is the assignment of keywords or labels to things in order to improve their accessibility, organisation and management. It's distinct from classification—which usually focuses on the literal ontology of the thing, the essence of what it is—because tagging is tailored to the context of the end user. Netflix's home screen will present you with rows of thematically grouped shows. This is tagging in action. Netflix's search functions, in contrast, will enumerate the types of shows available—movies, TV series, documentaries, et cetera..

Zooming is a complementary function that controls increases and decreases in the resolution with which something appears. The progression from a show's poster art to a short description to a trailer to the first episode to an entire series is a progressive zoom from low resolution to high. An alternative example: going from a book's cover to its blurb to its contents and front matter to chapter skimming to the actual prose. Another example: moving from a continent to a country to a region to a town to a street to an address and back again in Google Maps.

In theory and in practice

This is all well and good in theory. In practice, the current state of tagging and zooming is far from optimal.

First, configuring systems that provide high quality tagging and zooming experiences is resource-intensive for providers. The bulk of the work is done upfront and then rolled out to end users, often to mixed receptions and limited effect.

Second, it's labour-intensive for end users. If they use what's provided to them, it means an excessive amount of cognitive load to navigate an essentially impersonal, foreign tagging taxonomy and operate clunky zooming mechanics. If they decide to roll their own system and processes, it requires janky coordination of numerous third party tools and a constant vigilance which eventually overwhelms and causes abandonment.

Towards RAG-tagging and sem-zooming

Fortunately, the coming state of tagging and zooming is a big upgrade. It combines:

• Local-first applications

• On-device machine learning and AI

• Retrieval augmented generation architectures

• Continual interaction with past and present end user context

The result is a fast and tailored tagging and zooming experience. And the two things driving it: RAG-tagging and semantic zooming.

RAG-tagging is retrieval augmented generation plus traditional tagging. It uses large language models to generate, shape and refine the tags allocated to saved items based on the both the user's historical context and the requirements of their interactions in the present moment, without recourse to a distant, centralised third party service and global data store.

Traditional tagging is, for the most part, a manual, one-and-done exercise. Something's tagged at point of capture and a monolothic, fragmented schema of tags emerges over time. RAG-tagging, in contrast, continually regenerates the tags applied to items in a schema that accounts for historical tagging and the needs of the present moment.

Semantic zooming (sem-zooming) complements RAG-tagging. Humans have a supposed memory span of seven things (plus or minus two). Sem-zooming is the reformulation of meaning based on a combination of the resolution of the items that are viewed and a human's inherent memory span.

Imagine you have a list of 10,000 items saved. At low resolutions, seeing thousands of pieces of distinct information isn't useful at all. But as you look for a specific type of thing, and move from lower to higher resolutions, from 10,000 things to 1,000 things to 1 thing, the meaning attached to those different view points needs to dynamically adjust. With sem-zooming, the meaning shifts alongside the resolution.

One domain to see this zooming in practice is network visualisation. Standard systems will provide like-for-like displays of networks and their properties. But the best systems will shift the meaning that can be inferred from the visualisation as one zooms in and out, from node to cluster to clusters to the full network.

Better saving practices means better sharing experiences and greater search outcomes. The state of the web right now is compelling innovations in sharing and search, and these activities are getting a lot of attention. But for many people in many domains, good saving is the foundation that good sharing and search relies upon. And it's time for us to appreciate that and innovate on how we save and manage the things we find online.

RAG-tagging and sem-zooming are just two pieces—important ones, nonetheless—of this. The traditional approaches to tagging and zooming don't work for end users that expect both speed of interaction and maximal personal relevance, that demand complex capabilities with simple interfaces and intuitive mediating abstractions. Meanwhile, their utility for providers is also declining—more resource in exchange for increasingly marginal gains in effectiveness and UX isn't the best bargain and indicates a saturation point for current tagging and zooming methodologies.

RAG-tagging and sem-zooming may not be the entire answer but they're a fundamental part of the new and emerging practices for how we save, share and search online. And we're in the process of figuring them out.