Introduction
The Three-Layer Design for AI Context
A Practical UX Framework for Organising the Library, Conversation, and Memory in LLM Interfaces
For a Large Language Model (LLM) to be truly useful, everything starts with context management.
Context is not just one good prompt.
Context is the complete accumulation of conversations, inputs, responses, and assumptions that form over time.
Every interaction adds a new layer of meaning, just as human memory works.
In fact, context in AI systems is very similar to human memory:
Organised, multi-layered, and directly affects the quality of understanding and decision-making.
From Human Memory to AI Context
Cognitive psychology typically divides human memory into three layers:
Semantic Memory:
General knowledge, concepts, and facts — what I know.
Working Memory:
Temporary information needed for the current task — what I am dealing with now.
Long-term Memory:
Experiences, preferences, behavioural patterns — how I usually act.
LLM systems reflect this model almost directly, and their context can be understood through three clear UX layers:
🧩 Layer One: The Library
Semantic Memory — how external knowledge becomes the basis for understanding
The library is the static knowledge provided to the system by the user:
Files, links, documents, videos, or any external content.
The value here is not in storage…
but in how knowledge is constructed and retrieved.
The Design Challenge
Most products deliver the entire content for artificial intelligence all at once, without any real control from the user.
The result?
Ambiguity, loss of trust, and unexpected use of resources.
Successful design principles
Sources must be:
Clearly readable by the model
Visible and explorable by the user
Transparent upon invocation
Essential UX features
Multimedia Analyzer:
Transforms files, links, and videos into structured content with metadata and keywords.
Context Invoker:
Allows the user to specify certain sources during the conversation, displaying them as clear chips in the interface.
Why is this important?
Because trust does not come from the model's "intelligence"...
But from the user's control over what the model knows about them.
💬 Layer Two: Conversation
Working memory — when dialogue becomes a thinking environment
Conversation is where real-time thinking happens.
But the major problem here is vertical expansion:
Long conversations, infinite scrolling, and loss of essential context.
The solution is not a longer conversation...
But a more organised conversation.
Effective UX principles
Instead of letting the conversation accumulate over time,
meaning should be condensed and key ideas extracted.
Essential UX features
Semantic Aggregator:
Divides long conversations into collapsible topics, keeping only the active topic open.
Content Notebook:
Allows the extraction of AI outputs to an independent workspace, instead of getting lost in the chat log.
The true value
Transforming chat from a temporary experience…
to a product workflow that can be built upon.
Read also: User interface design trends that will shape 2026
🗓️ Layer three: Memory
Long-term memory — continuity and personalisation
Memory is what makes the experience personal over time.
When the system remembers:
Your preferences
Your experience
Your decision patterns
It doesn't just become smarter…
but more human.
The sensitive challenge
The balance between:
Personalisation
User control and transparency
Ethical design principles
The user must know:
What is being saved
When it was recalled
And why it affected the response
Essential UX features
Show the memory used:
Each response relies on stored memory displayed as a clear Memory Chip.
Memory management:
Empowering the user to edit, disable, or delete any saved item.
Contextual control:
Turning memory on or off for each conversation individually.
The goal
Is not to collect more data…
But to empower the user to shape how the system remembers them.
Context is king… but design is the throne.
The library, conversation, and memory are not separate units.
They are three ways to organise intelligence itself.
The library organises knowledge
The conversation organises thought
Memory organises the relationship over time.
Context is not the background of interaction.
Context is the structure that allows meaning to emerge in the first place.
🎯 With Echo Media
If you are:
Designing an AI product
Or working on UX for LLM interfaces
Or building a system based on conversation and memory
📩 At Echo Media, we help you to:
Design intelligent Context Architecture
Transform chat into a real business experience
Build trustworthy AI interfaces… not just use them
Let’s build intelligible intelligence, not ambiguous.