CHAPTERS
Why AI product design is hard: moving beyond chat UX
Aakash introduces Elizabeth Laraki’s background on Google Search and Maps and frames the core challenge: AI features are non-deterministic and shouldn’t default to a linear chat interface. The episode’s goal is to show practical design thinking, pitfalls, and real workflows for building AI-powered products.
How Google Search evolved without constant redesign
Elizabeth explains how early Google Search work focused on integrating new content types (images, video, maps) while preserving a familiar structure. The conversation highlights how strong products often improve through nuanced, continuous iteration rather than visual overhauls.
Designing AI inside Google Search: benefits, backlash, and hallucinations
They discuss Google’s AI search integration as a strategic response to ChatGPT’s rise. Elizabeth is positive on embedding AI where users already are, while Aakash emphasizes confidence thresholds, evals, and when not to show AI answers.
Rethinking image/video answers: from linear chat to “show me” interfaces
Elizabeth critiques the limitations of linear chat when solving visual, hands-on problems (e.g., adjusting a bike seat). They propose interfaces where the image/video stays central and the conversation becomes a supporting layer, enabling more effective guidance.
AI image expander disaster: unintended consequences in real workflows
Elizabeth shares a cautionary story where an image expansion tool generated inappropriate fabricated details during a conference promo workflow. The incident illustrates how seemingly reasonable human+AI pipelines can produce reputational and safety risks.
Safeguards and human-in-the-loop: model, evals, and UI transparency
They discuss practical mitigation: improving training data, adding evals for sensitive cases, and designing UI that clearly distinguishes original vs AI-generated regions. The key is combining backend guardrails with frontend clarity so humans can effectively review.
A 3-step process for designing AI features: define → design → build
Elizabeth outlines a simple but rigorous product approach: clarify what you’re building, design the experience, then build with the right constraints and validations. This frames the rest of the discussion about what “good” AI products get right.
What well-designed AI products do right: ChatGPT, Descript/Riverside, Midjourney
Elizabeth reviews AI products she finds highly useful and why: ChatGPT’s simplicity and layered power, Descript/Riverside’s workflow-level assistance, and Midjourney’s strong output despite UX tradeoffs. Aakash emphasizes “baked in” AI—supporting end-to-end jobs rather than bolted-on features.
Designing AI voice experiences: context-first, conversational, not a screen reader
They explore voice UX through examples: ChatGPT voice in the car feels like “another person,” Limitless enables conversation summaries and coaching, and Meta Ray-Bans reveal pitfalls when voice behaves like a literal screen reader. The chapter emphasizes designing for context and dialogue control.
Designing beyond chat: canvases, co-creation, and deterministic outputs
Elizabeth explains why chat struggles for tasks requiring stable artifacts (e.g., travel itineraries). The future is interfaces where AI is a tool inside a more structured workspace—like a canvas—so users can co-create, edit, and maintain control.
AI design tools for designers: useful at the edges, taste still matters
Elizabeth is skeptical that AI design tools can meet high-quality expectations for experienced designers, though they can accelerate drafts and support non-designers. They discuss using AI for specs, first-pass layouts, and prototyping—while relying on human taste for final quality.
Live design exercise: decomposing “LinkedIn for AI” into a real product strategy
In a live session, Elizabeth demonstrates how to tackle an ambiguous prompt by clarifying the objective and exploring product directions (matchmaking, certification/training, networking, content). They zoom into matchmaking and map the marketplace, attributes, onboarding, and the “AI magic” in the middle.
Google Maps redesign: from tab clutter to a single search box architecture
Elizabeth recounts how early Maps became cluttered with multiple tabs and search boxes for different tasks. The redesign consolidated into one search box, informed by clear information architecture and controversial at the time because directions were Maps’ core value.
Launching Google Maps directions in India: landmark-based navigation through research
They close with a landmark case study: turn-by-turn directions didn’t work well in India due to navigation norms, lack of street names, and pre-smartphone constraints. Research in-country led to landmark-based directions that help users verify they’re on track and navigate culturally appropriate cues.
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