AI UX Design Mistakes: Fix the Flow Before the Prompt
Most AI UX design mistakes start before the prompt. Learn how to validate the scenario, map product states, and design the next useful AI action.
AI Product Design Services
Design AI features people actually rely on — clear about what the model knows, honest about what it guesses, and easy to correct when it is wrong.
- Designed for wrong answers
- Confidence, sources, corrections and fallbacks
- Feedback your model uses
- Ratings and edits shaped for evaluation
- AI products, not AI logos
- Interfaces for products with AI inside
- Custom scope
- Fixed or phased, set in the proposal
In short
AI features people understand, check and correct, so a wrong answer costs a click, not their trust.
What's included
- Where AI enters the workflow
- Outputs and sources
- Confidence and uncertainty
- Feedback and corrections
- Human control
- Fallbacks and recovery
Answers you'll have before development
- Where does AI help, and where does it get in the way?
- How sure should the product look?
- What does a person do when the answer is wrong?
Where it stops
AI Agent & Conversational UX Design
Agents that act: setup, permissions and approval before each action.
AI Product Design
The whole AI product: outputs, confidence, feedback and control.
Not included
- Model selection, training or tuning
- Accuracy, adoption or revenue figures
The AI shown as it really behaves
A demo shows the best answer. The design has to hold the average one, the slow one and the wrong one — so the work starts from real outputs, not a mock-up of a perfect model.
-
AI opportunity map
Where AI helps in the workflow, where it does not, and what each use case risks when it is wrong.
-
Output and confidence patterns
How answers, generated content, sources and uncertainty appear, screen by screen.
-
Feedback and correction flows
Ratings, edits and reports designed so your model team can use what people tell it.
-
Control and fallback design
Override, regenerate, undo, switch off — and a way to finish the job without AI.
You receive
- Flows for each AI use case
- A state and feedback matrix
- UI and a clickable prototype
- What the model must expose
Designed for the answer that is wrong, not only the one that is right.
Good AI UX design starts where the model is unsure. Every screen is drawn for what the AI knows, what it guesses and what it gets wrong — and for the person who decides what to do next.
Every screen is designed for
- Generating, answer streaming in
- Confident, with sources
- Unsure, and saying so
- No answer found
- Corrected by the user
- Rated or reported
- Blocked by a data rule
- Slow or over its limit
- AI switched off
AI products we have designed
An AI assistant inside a learning platform, and agentic AI for corporate teams.
-
EdTech · SaaS
Moka
Wrapped complex AI in an interface a stressed student trusts at once, beside the dense analytics educators expect — one calm product for four roles, from a blank canvas.
- 200+ Screens designed
- 4 Experts on the team
-
AI · B2B SaaS · Web app
Nexus
Designed how corporate teams build, trust and supervise AI — a calm interface where the AI's work stays legible, with the powerful configuration one layer below.
- 200+ Unique screens
- 3 Experts on the team
When AI product design is the right frame
It fits when the product's value depends on what a model produces, and people have to judge it.
-
AI is the product
Its value depends on what the model writes, finds, predicts or recommends.
-
Outputs can be wrong
People have to judge an answer before they rely on it.
-
Trust is the barrier
People try the feature once, then go back to the old way.
-
A model team exists
Engineers own the model; design shapes what people see, check and correct.
Let's make your AI worth trusting
Tell us what the model does and where people hesitate. We will come back with the service that fits and a realistic next step.
Scope, access and who does what
AI features are usually designed one use case at a time, under one of our services. Its proposal sets the terms.
- Scope
- Per service fixed or phased, set in the proposal
- Timeline
- Agreed after scope, access and dependencies
-
You provide
- The product
- The product or a prototype of the AI feature, with a login for each role.
- Real outputs
- Sample answers, good and bad, and the failure modes you already know.
- Decisions
- A product owner who can decide where AI is used and where it is not.
- The model team
- What the model can report — confidence, sources, latency, limits.
-
Who does what
- ANODA
- Maps where AI fits the workflow, designs the outputs, feedback, controls and fallbacks with every state, and documents what the model must expose.
- Your team
- Builds and evaluates the model, wires feedback into your evaluation, reviews each step and ships the product.
-
Boundaries
- Outside AI product design
- Model selection and training, prompt engineering, evaluation pipelines, data labelling and development.
- After the design
- Your team builds and evaluates the AI. Reviews of the working product are scoped separately when needed.
Where do people stop trusting your AI?
Tell us what the AI does, who uses it, and what happens when it gets something wrong.
Message received.
Within 15 minutes, we’ll email you initial feedback and follow-up questions.
What AI Can't Replace in Your Product Team (And What It's Quietly Breaking)
Learn what AI genuinely can't do in design and engineering before you make expensive budget decisions based on a demo.
AI Product Design: common questions
What separates a real AI product design agency from a studio that just uses AI for visuals?
First, check the job. Many studios that call themselves an AI design agency use AI to make logos and visuals; you need a team that designs products with AI inside them. Ask to see an AI feature designed with its awkward cases — a slow answer, an unsure one, a wrong one — and the feedback and controls around it. Ask whether the work shipped in a real product, and what the designers did when the model got it wrong. Our Moka and Nexus cases show that work.
We're adding AI to our product. Which parts need designing, and what changes the size of the job?
A map of where AI fits the workflow, the output and confidence patterns, feedback and correction flows, controls and fallbacks, the UI and a prototype to click through, and a note on what the model must expose. Cost and timeline depend on the number of AI use cases and roles, how new the product is, the platforms, and how settled the model is. They are set in the proposal for the service you start with.
Who touches an AI feature besides the end user, and what does AI UX design cover for them?
The people who use AI output to do their job, the experts who check or approve it, admins who decide where AI is switched on and what data it may use, and the product and model teams who learn from feedback. The workflows are asking and prompting, reading and comparing answers, checking sources, editing or regenerating, rating and reporting, and finishing the task without AI when it fails.
Which ANODA services fit an AI product, and where do we start?
Product Discovery when it is not yet clear AI is the answer; a UX Audit when an AI feature is live and people ignore it. UI/UX & Product Design takes on a new AI product whole, Web App Design and Mobile App Design its surfaces, and Design Systems the patterns once several teams ship AI. When the AI acts on people's behalf, AI Agent & Conversational UX Design is the closer fit.
How do you design for wrong answers, uncertainty, permissions and data?
From real outputs, not ideal ones. Every screen is drawn for an answer streaming in, a confident answer with sources, an unsure one that says so, no answer, a correction, a rating or report, a data rule that blocks a request, a slow or rate-limited model, and AI switched off. Permissions set who can use which AI feature on which data, and a fallback lets people finish without it.
What do you need from our product and model teams?
Access to the product or a prototype, sample outputs — good and bad — and the failure modes you already know. A product owner who can decide where AI is used, and time with the engineers who own the model, so the design shows only what the model can really report: confidence, sources, speed and limits.
Which AI products have you designed?
Moka, an AI academic assistant for four roles, where complex AI had to feel calm to a stressed student while educators still got dense analytics — 200+ screens designed by 4 experts. Nexus, where corporate teams build and supervise agentic AI in an interface that keeps the AI's work legible, with the configuration one layer below — 200+ unique screens by 3 experts. Both case studies show the work around the AI's output: how people read it, trust it and stay in control of it.
The AI feature has to fit a product we already ship. Can you design it in?
Yes. Most AI features are added to a product that already exists. Your designers, product managers and model engineers review the work in your own files, and we hand over the AI patterns so the next feature matches the first.