Why We Need Standardized UI for AI

[alex2saaba]

Large Language Models (LLMs) are becoming more capable every year, but the interfaces we use to interact with them have hardly changed. Most users are still typing into a single text box and getting a stream of replies in an endless chat. This simplicity is misleading — it creates false expectations. At the same time, well-known LLM limitations like hallucinations, forgetfulness, and inconsistency lead to frustration and undermine trust in AI as a thinking partner.

It’s time to recognize that the user interface is not just a wrapper — it’s part of the AI’s cognitive logic. The UI shapes how dialogue unfolds, how memory is formed and retrieved, how time is perceived, and how responsibility is shared between human and machine. Like any translator, the interface can clarify meaning — or distort it.

Based on my experience with multiple systems — ChatGPT, Grok (X and .com), Gemini (Google AI Studio), Claude, Perplexity, Qwen, Yandex Neuro and Alice — I’d like to share a case for standardizing the UI protocols we use to interact with AI.

Why It Matters

1. LLMs don’t think like humans. They have no linear memory, no native sense of time, and no idea what happened “yesterday” unless told.
2. Users expect narrative logic. We rely on coherence, causality, tone, and continuity of topics.
3. The interface can bridge the gap. If it helps structure dialogue, mark transitions, distinguish agents, and map time.

What Needs to Change

1. Topical segmentation. Dialogues shouldn’t be endless scrolls. We need “blocks” or “notebooks” with scoped memory.
2. Temporal anchors. The UI should show time markers and past references clearly.
3. Agent markers. In multi-agent scenarios, roles, tones, and identities should be visible and stable.
4. Editable bookmarks. Users should be able to label key moments, return to them, and build structure as they go.
5. Dialogue parameters. Style, depth, tone, and confidence should be adjustable and transparent.

Why Standardization Is Needed

If we want a resilient ecosystem for human–AI collaboration, we need shared assumptions about:

what “context” means, how it’s tracked, how roles are marked, how memory works, and how users can manage the rhythm of interaction.

This requires a common foundation — UI protocols — comparable to web standards like HTTP. A shared interface protocol would enable us to:

• export and import sessions across platforms;
• run consistent multi-agent conversations;
• build collaborative workspaces with AI;
• support meta-memory, reflection, and digital legacy.

If you’re interested, I can share specific examples of surprising and sometimes frustrating adventures caused entirely by human–LLM UI design. For instance: how the “regenerate” function works in Google AI Studio, how Grok’s workspace logic creates confusion, how users discover that chats are not always saved by default, and how pressing “Enter” behaves differently in web vs mobile interfaces.

AI UI Protocol Draft

This is not just a design challenge — it’s about cognitive compatibility. A proposed protocol might include:

1. Session containers – discrete blocks with memory scope and visible structure.
2. Temporal anchors – visual and logical indicators of time progression.
3. Agent roles and styles – explicit assignment and tracking of tone, intent, and identity.
4. Editable bookmarks – named, navigable markers in the dialogue tree.
5. Dialogue settings – transparent control of tone, depth, certainty, verbosity.
6. Cross-platform portability – standard formats for session import/export.
7. Contextual memory API – protocols for memory persistence, scope, and user control.

This draft is an invitation for conversation. We need the community — designers, developers, researchers, and users — to help shape a shared interface language for the future of human–AI dialogue.

[lucid.dev]

[alex2saaba]

Can you please elaborate? For me it looks like B737-800 dashboard.

[johnroy]

This is not just a design challenge — it’s about cognitive compatibility

Very interesting. Thanks for breaking that out.

I agree 100% there’s a massive ecosystem missing around AI, where AI meets “the regular world”.

This missing continent is both in apps / UI but also where ppl are at. Both things go hand in hand, ppl learn about AI from the interfaces they are exposed to. And interfaces get built only when we can conceptualize new features based on where ppl are at.

Outside of forums like this very few have exposure to AI, generally just 2 use cases:

1. chatbots - everybody understands chatbots. but this interface is way short of what AI could do -AND-
2. more advanced systems lovable/claude/ etc. which are still being digested and thus kinda welcomed as “miracles”.

[lucid.dev]

Its an application that supports three-dimensional context window management as a dataset which is fed to the LLM as a “prompt”. A string of role/user messages is not maintained, instead, a living set of data is managed as a “world state”, completely categorized, sectioned, and coming from all backend input sources related to tool calls, agents, user input, direct modification through LLM response, etc.

Simultaneously the user manages “threads” (i.e. a world state builder), “profile” (basic API parameters/LLM sourcing settings), and also has access to direct development pipelines to the backend database, on-disk or cloud filesystem, and can inject content from either the development stream or external sources directly into the world state, thus sharing it with the LLM in the next prompt.

Essentially:

[a set of all possible inputs]
→ user mapping/templating for projecting/refining/filtering that data into world state
→ world state is the single dataset (not broken into sets of assistant/user messages) sent to LLM
→ LLM response can directly or indirectly (through tool calls, spawning other LLM agents, etc.) modify the world state context
→ System can either auto-prompt LLM again with new modified world state without human user input, or settings create multiple tiers of notification/pausing of system so human user can interact, modify world state
→ set new goals/modify tasks/provide clarifying information/etc. in world state
→ prompt LLM with new world state and allow autoprompting to continue until task run is complete/goal is achieved.

Can be used for project development, research projects, application production, and once we API into other “normal” applications used on windows/linux etc., then the LLM can essentially run forever (within token limits set by user) to achieve various tasks and goals, all while the process itself is properly split out into “blocks” or “notes” available to the user in the world state management window, without any interaction necessary at the level of review of the entire data dump of the context windows change and such (though those logs continue to remain available).

Sharing because I believe it fulfills the requirements you set out in your original question.

[alex2saaba]

@lucid.dev

Sharing because I believe it fulfills the requirements you set out in your original question.

It sounds and looks very interesting, but imagine that the last time I programmed in FORTRAN in the RSX environment when I was participating in a crazy project to write an interactive AutoCAD on a PDP-11 in which the user works exclusively through the command line.

Now I am nothing more than an experienced browser user, and my main occupation is a translator and editor. So this is Greek to me.

[lucid.dev]

Well, this is a dev forum - so getting a handle on comprehension is probably the way you can help in development - sharing high-concept ideas not necessarily doing much if you don’t have the comprehension or put forward the effort to learn to at least provide some kind of insight into actual behavior - we are here to build. You might try reddit if you just want “discussion”.