This opening slide sets the stage for why front end design is a critical part of building successful
LM applications.
While back end architecture and model choice receive much attention, users never interact directly
with those components.
They experience AI exclusively through the interface in front of them.
As the title suggests, this section focuses on front end design, not as decoration, but as a core
system layer.
The interface determines whether AI feels helpful or frustrating, intelligent, or unreliable.
A well-designed front end can make a modest model feel powerful, while a poorly designed interface
can make even the most advanced model feel broken.
The visual on this slide reinforces the idea that AI is embedded in everyday workflows.
Users expect clarity, responsiveness, and feedback, not mystery.
Throughout this section, we will explore practical design patterns that help engineers and designers
build interfaces that communicate what the AI is doing, why it is doing it, and how users can interact
with it confidently.
This slide explains a foundational truth.
The front end layer fundamentally shapes how users perceive AI intelligence, as described on page two.
No matter how sophisticated your language model is, poor UX can make it feel slow, unreliable, or
confusing.
The front end determines three critical outcomes.
First, trust whether users believe the system's outputs.
Second, adoption whether users choose to rely on the product regularly.
And third, perceived intelligence how capable the AI appears in practice.
A powerful insight on this slide is that the front end is not just presentation, it actively shapes
mental models.
A thoughtful interface can guide users set expectations and surface uncertainty.
Honestly, A careless interface, hides state, masks errors, and encourages overtrust.
The closing statement on the slide is worth emphasizing.
A great LM back end fails without a great front end.
In AI products, the interface is the product.
This slide introduces three common front end patterns used in LM applications.
Chat based UIs mirror familiar messaging experiences and work well for open ended interaction and exploration.
Search plus answer interfaces combine traditional retrieval with AI generated summaries, making them
ideal for research and knowledge tasks.
Copilots and side panels embed AI assistants directly into existing workflows without disrupting the
user's primary task.
Regardless of which pattern is chosen, the slide highlights three non-negotiable front end responsibilities.
First, the interface must accurately capture user intent with minimal friction.
Second, it must display partial results as they become available.
Third, it must handle errors gracefully without breaking the user's mental model.
The goal, as stated clearly on the slide, is to reduce friction between the user and the AI while
maintaining clarity about what the system can and cannot do.
Pattern choice matters, but execution matters more.
This slide explains why chat interfaces have become the dominant pattern for LN applications.
They leverage an existing mental model conversation.
Users already know how to chat, which dramatically lowers the learning curve and makes advanced AI
capabilities feel approachable.
The slide highlights three essential components of effective chat design.
Message history provides continuity and allows users to reference past exchanges.
Clear attribution visually distinguishes system messages from user messages, preventing confusion about
who said what.
Input affordances clearly signal what actions are possible, such as typing text, uploading files,
or using voice input.
The best practice callout is critical.
Show conversation context.
Clearly make system actions visible and avoid hidden state.
When users lose track of context or system behavior, trust erodes quickly.
Good chat design makes AI behavior legible, not magical.
This slide dives into the complexity of managing chat UI state.
Unlike traditional Crud interfaces, LM applications must coordinate multiple states simultaneously.
Loading indicators, streaming partial responses, and error conditions.
The slide lists common mistakes that plague AI interfaces, blocking the entire UI while waiting for
a response.
Makes systems feel frozen.
Accidentally resetting conversation context confuses users.
Losing user input during errors creates frustration.
Failing to handle async race conditions leads to unpredictable behavior.
Recommended approaches include optimistic updates for immediate feedback, clear and unambiguous status
indicators, and preserving context across all state transitions.
The front end must also be designed for concurrency and interruption, not linear workflows.
The golden rule at the bottom of the slide is decisive.
Never leave users guessing about what the system is doing or why.
Transparency is the foundation of trust.
This slide explains why streaming responses are essential for LM applications.
LM outputs are inherently slow and variable.
A response that takes ten to 30s can feel unbearable in a modern interface.
Streaming transforms this weakness into a strength by showing progressive results.
Streaming improves perceived performance by 40 to 60%.
Users see that the system is working, which builds trust.
Incremental responses also feel more intelligent, mirroring how humans think and speak.
The slide introduces common streaming techniques such as server sent events, WebSockets and fetch streaming,
each with different trade offs.
The key insight is highlighted clearly.
Perceived speed matters more than raw speed.
A long response that streams feels faster than a shorter response that appears all at once.
This slide provides a practical breakdown of how streaming should be implemented on the front end.
The process begins by initializing the stream and preparing the UI for incremental updates.
As tokens arrive, they are appended smoothly to the message.
The system must handle interruptions gracefully, whether caused by network issues or user cancellation.
Finally, the response is finalized and follow up actions are enabled.
The slide also highlights critical UX considerations.
Typing indicators help set expectations.
Cancel buttons give users control.
Partial message recovery preserves work if the stream fails.
Auto scroll behavior keeps new content visible without fighting user intent.
The best practice statement is essential design for interruption, not perfection.
Streaming will fail sometimes.
Your UX should handle those failures elegantly rather than collapsing.
This slide focuses on trust and safety in AI interfaces.
Llms can be confidently wrong, making transparency essential.
Interfaces must communicate uncertainty and encourage verification rather than blind acceptance.
Trust building patterns include showing citations and sources, highlighting uncertainty, and allowing
users to edit AI generated outputs.
These patterns frame AI responses as starting points, not final answers.
The slide also lists anti-patterns to avoid presenting outputs with absolute certainty, hiding failures,
obscuring limitations, and making verification difficult.
These design choices actively undermine trust.
The critical rule at the bottom is clear confidence does not equal correctness.
A responsible AI interface never implies otherwise.
Trust is built through honesty, not polish.
The final slide summarizes the core lessons of front end LLM integration.
The front end shapes AI perception entirely.
Users judge intelligence through the interface, not the underlying model.
Chat interfaces are powerful but require discipline.
Streaming responses are no longer optional.
They are essential for usability and uncertainty must be designed for explicitly, not hidden.
The closing message is powerful.
Great AI products feel helpful, responsive and honest, not magical.
The best front end work makes sophisticated technology feel simple, trustworthy, and human centered.
This mindset is what separates successful AI products from short lived demos.