Why LLM chat-interfaces limit learning
The problem of context
“My taste runs to hourglasses, maps, seventeenth-century typefaces, etymologies, the taste of coffee, and the prose of Robert Louis Stevenson.”
― Jorge Luis Borges, Labyrinths: Selected Stories & Other Writings
LLM chatbots are a genuine breakthrough for many types of learning. For the first time, most of us can ask specific questions—at any level of detail—and get a useful explanation back in seconds.
However, it still poses the question how do I retain what I learned—and turn it into knowledge I can use later?
Learning has (at least) two phases
Most serious learning seems to alternate between two modes:
Exploration: expanding your understanding, gathering perspectives, generating questions.
Consolidation: compressing what you found into a structure you can remember and retrieve.
A book chapter can be exploration. Notes can be consolidation. A good teacher helps you do both.
Chat is excellent for exploration—and terrible for consolidation. Not because the answers are always bad, but because the format is.
The labyrinth problem
Imagine learning as traversing a labyrinth.
In exploration mode, you’re roaming hallways looking for treasure rooms. You don’t yet know what matters, or where it’s hidden. You need freedom to branch, follow curiosity, and double back.
A typical chat interface is like a single long corridor.
As you walk, you pass doors—interesting side paths, promising rooms—but the corridor only lets you do one of two things:
keep going forward (and lose the door).
step into the room (and abandon the main path).
That’s already limiting. But it gets worse: even if you do explore a room, the record of what you found is still just more corridor behind you—pages of scroll with no map.
So when you come back later, you don’t return to “the treasure room.”
You return to “somewhere in the corridor… maybe 300 messages up.”
Why chat collapses context
Linear streams work when the job is coordination and speed. But learning is not coordination. Learning is navigation.
And linear chat produces three predictable breakdowns:
1) Discoveries become hard to relocate.
Even when chat produces something genuinely useful, it’s buried inside long tracts of text. Retrieval becomes archaeology.
2) Threads tangle and drift.
In any active channel, multiple ideas interleave. People respond at different timescales. The “shape” of the discussion exists only in participants’ heads—and evaporates when they step away. Research on group chat and overload repeatedly points to noise, redundancy, and the difficulty of keeping productive discourse coherent at scale.
3) The medium nudges you away from consolidation.
Consolidation isn’t just rereading. It’s actively reorganising and retrieving what you learned—processes strongly associated with long-term retention (for example, retrieval practice / the “testing effect”).
A scrolling transcript doesn’t naturally invite that. It invites more scrolling.
What we actually need: a map, not a corridor
If the goal is long-term learning, you need three things that chat doesn’t provide by default:
A visible structure of the exploration (what led to what; which claims depend on which reasons).
Stable locations you can return to (not “scroll up,” but “go here”).
An export path into your own notes—so your learning can leave the tool and become yours.
In the labyrinth metaphor: you need a map, markers, and a way to carry the gold home.
What I built instead: MuDG
This is the bet behind MuDG: turn conversation into a navigable diagram.
Instead of a linear feed, MuDG aims to make the structure of learning explicit: ideas branch, connect, gather evidence, and stay linkable. The point isn’t to replace exploration—it’s to preserve it in a form you can actually reuse.
MuDG is designed around a few practical moves:
Every reply becomes part of a shared diagram, so the reasoning stays visible and referenceable.
Follow-ups expand the map with evidence and counter-points, without losing the original thread.
Snapshots let you “save the state” when you reach clarity, so you can revisit the reasoning, not just the conclusion.
Unique URLs for graphs and nodes mean you can share the exact point you mean.
Exports to PDF/PNG/Markdown make it easy to turn an exploration into notes, handouts, or your own knowledge system.
Chat helps you find ideas.; A map helps you keep them.
Try it for yourself - mudg.fly.dev
The real promise of LLMs for learning
LLMs are an exploration engine. Used well, they can widen your maze: more doors, more rooms, more “wait—what’s that?” moments.
But long-term learning still depends on consolidation: turning exploration into organised memory and retrieval.
So the problem of context isn’t just that chat is messy.
It’s that we’re trying to build durable knowledge inside a container designed for ephemera.
If we want LLMs to actually change education—and not just produce fleeting flashes of insight—we need interfaces that treat learning like navigation:
branch without losing your place,
mark what matters as you go,
and bring the output back into your own notes.
That’s the shift: from corridor to map.