This page describes Tejj's design process — a seven-phase method for shipping AI-powered products at scale. Tejj is a Product Designer at Microsoft AI and Microsoft Windows, designing intuitive, scalable systems used by over a billion users.
The process combines rigorous UX fundamentals (user research, problem framing, journey mapping, prototyping, testing) with production-grade AI thinking (model behaviour, prompt design, hallucination handling, human-AI feedback loops, accessibility, ethical guardrails).
It is grounded in a human-first approach to technology — making AI accessible to everyone, not just experts.
Tejj is also a best-selling Amazon author of Nodes of Wisdom, host of India's #1 design podcast Nodes of Design, and ADPlist Top Mentor to 800+ designers globally. Based in India.
I design products by going deep into the problem first understanding users, business goals, systems, constraints, and the future the product needs to create.
My process blends UX fundamentals, product thinking, and AI-first design to move from confusion to clarity.
From clarity to strategy. From strategy to a shipped product people can actually use.
01 · Define the Problem
"Why does this need to exist now?"
Before opening Figma, I align with product, engineering, business, data, and leadership teams to understand the real problem behind the request.
I clarify the users, goals, constraints, risks, timelines, success metrics, and business expectations.
This helps the team avoid building fast in the wrong direction.
The outcome is a shared product north star what we are building, who it is for, why it matters, and how we will know it worked.
Output: A clear product direction before design begins.
02 · Understand the User
"Good design does not start with opinions. It starts with listening."
I use research to understand what users are trying to do, where they struggle, what they trust, what they avoid, and what they actually need from the product.
Interviews, contextual inquiry, journey mapping, analytics, competitive reviews, and heuristic analysis.
System-level understanding not just the screen, but the full context around it.
The goal is not to collect research for a deck. The goal is to find the truth that changes the product.
Output: Clear user insights, opportunity areas, and design principles.
03 · Shape the Strategy
"Once the problem is understood, I translate insights into an experience strategy."
This defines the product's structure, core journeys, information architecture, key behaviours, success metrics, and long-term experience vision.
For complex products, this step is where clarity matters most it helps teams move from scattered features to one connected experience.
I create blueprints, flows, frameworks, and north-star prototypes that make the product direction visible to everyone in the room.
Strategy without visuals is just a slide. I make it tangible.
Output: A clear experience strategy that connects user value with business value.
04 · Design with AI My Specialty
"AI should not be added because it is trending. It should be used only where it creates real user value."
I design AI experiences by defining where intelligence should support the user, where the user must stay in control, and where automation can create trust instead of confusion.
Thinking deeply about prompts, model behaviour, confidence, explainability, failure states, privacy, and human override.
Designing AI that is understandable, useful, safe, and human not just impressive in a demo.
Responsible AI practice baked in from day one: bias, escalation paths, and failure modes are not afterthoughts.
Output: AI experiences users can understand, control, and trust.
05 · Prototype to Learn
"Prototypes are not just for presentation. They are tools for learning."
I move from rough sketches to interactive prototypes that help teams test the experience before expensive engineering work begins.
For AI products, I prototype not only the interface but also the behaviour sample responses, decision logic, prompt flows, edge cases, and fallback states.
I test with users, teams, and stakeholders to understand what works, what breaks, and what needs to become simpler.
Nothing ships without at least one round of real users telling me where it breaks.
Output: Validated product direction before build.
06 · Ship with Care
"A product is not complete when the design file is complete."
I document flows, interaction patterns, and content guidelines so engineering can ship with confidence and I stay close through implementation to protect the experience as constraints appear.
I review builds, identify design drift, support trade-off decisions, and make sure the product that ships still reflects the product that was designed.
After launch, I track performance, user behaviour, adoption, and friction so the product can keep improving.
Shipping is not the finish line. It is the beginning of the next problem worth solving.
Output: A shipped product that stays true to the user and business goal.
07 · Teach the Thinking
"Design becomes more powerful when the team understands the thinking behind it."
I teach teams, designers, PMs, and leaders how to use design and AI as practical tools not as theory, not as buzzwords. Design is all about collaboration.
Through workshops, walkthroughs, talks, and internal narratives, I help teams understand the why behind design decisions.
I build confidence in using AI responsibly — moving teams from anxiety to practical, daily application.
Teaching also sharpens my own product practice. It keeps the process grounded in real team challenges, not just ideal design theory.
Output: Teams that understand the product, the process, and the decisions behind it.
AI can accelerate work. But the best products still come from human judgment, empathy, taste, and responsibility. These are the principles I carry into every product I design.
01
AI can generate options. Only humans can understand pain. The best product decisions still come from listening deeply to users and respecting the emotion behind their behaviour.
02
A powerful product does not feel powerful because it has many features. It feels powerful because it makes the next step obvious.
03
If users do not understand what the system is doing, they will not trust it. In AI products, transparency, control, and failure handling are not optional — they are part of the experience.
04
Teams that rush into solutions often spend more time fixing confusion later. A clear problem saves months of wrong execution.
05
Design is not decoration. It shapes adoption, retention, conversion, efficiency, trust, and long-term product value.
06
AI, data, systems, and interfaces are materials. What matters is what we build with them — clarity, confidence, usefulness, and dignity for the user.
I run workshops, talks, and product sessions for designers, PMs, founders, and leadership who want to understand how to use AI meaningfully in product design. The focus is practical — not theory, not buzzwords.