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Mind the Product

Artificial intelligence (AI)

Synthesia becomes the UK’s largest AI product: Lessons for product teams

Guest Post

Does product management only work for tech and startups?

B2B product management

Artificial Intelligence (AI) & Machine Learning (ML)

Building Product Teams

Business Strategy

Data Driven Product Management

Digital Transformation

Diversity and Inclusion

Minimum Viable Product

OKRs

Prioritisation

Product Design

Product Research

Product Development Process

Product Ethics

Product Discovery

Product Leadership

Product Management Career

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Stakeholder Management

🌎 <a href="https://www.mindtheproduct.com/world-product-day/" style="color:#FFFFFF;text-decoration:underline;">Happy World Product Day!</a> Be sure to celebrate product, yourself, and your peers today! 🎉

The best resources for product managers in 2025

Three amazing speakers to learn from at #mtpcon London 2025

Should you specialize or stay versatile in product?

How GenAI has improved GoDaddy’s product experience – Laka Sriram (VP Generative AI, GoDaddy)

Meta scraps third-party fact checking in favour of community notes

Julie is a producer and digital experience design lead at Wells Fargo based in San Francisco. She unites leadership and communications skills that help teams run smoothly with a deep love for creating user-centric products that improve organizations' relationships with their customers. Before joining Wells Fargo, Julie led marketing efforts at a design agency and a handful of nonprofits.

Julie Celia

<p>In this talk, <a href="https://twitter.com/salomshoh">Shakhina Pulatova</a> gets deep on the art of searching. She takes us through search basics, to what powers search products, and ends with why you should give creating search products a try.</p>
<h2>On Intent</h2>
<p>The act of searching means the user has a question that needs answering, so the first key principle in building a search tool is to understand the intent. There are many facets that comprise an intent, and they may shift based on use cases and requirements. One example Shakhina shares is <em>specificity</em>. How specific is your user’s intent? Do they have a vague notion of what they need or do they have a clear demand? When you can better understand your searcher’s intent, you can provide better results.</p>
<h2>Finding and Discovering Opportunity</h2>
<p>In the second part of her talk, Shakhina shares how she approaches the challenge of finding and discovering opportunity and the machine learning models she uses to create solutions.</p>
<h3>Understand or Predict Answers</h3>
<p>To help users find answers faster, Shakhina recommends using query logs from past searches to personalize their results. Query tagging helps to deliver more precise results by breaking a query down into logical units (such as title, company, location) and matching against these existing entities.</p>
<p>LinkedIn also helps searchers by expanding queries to include synonyms, abbreviations, and name clusters. So say you’re searching for your former coworker Jeff, you’ll still be able to find his profile under his full name, Jeffery. If you’re looking up Kaitlin, you’ll also see results for Caitlin and Kaitlyn.</p>
<h3>Find and Present Answers</h3>
<p>In order to present the most relevant answers, Shakhina proposes ranking as a technique to bubble up the freshest results and minimize the spammy ones. She also suggests casting a wider (yet personalized) net by displaying blended result to include people, jobs, and posts, particularly for autocompleted searches.</p>
<h3>Inspiring More Questions</h3>
<p>Finally, the exploration doesn’t have to stop when results are returned. To give searches opportunities to drill deeper, LinkedIn uses tags to help users navigate through content.</p>
<h2>Why you should work on Search and Discovery products (at some point)</h2>
<p>Search and discovery products challenge you to think outside of your product and connect the dots between and across product lines. You’ll spend time in the nitty, gritty, micro-experiences and exercise your systems thinking skills. You have metrics galore to guide your product and can make a major impact on your business. Maybe it’s not for everyone, but Shakhina would tell you to explore and see what happens.</p>

core/freeform

AI ethics advice from former White House technologist – Kasia Chmielinski (Co-founder, The Data Nutrition Project)

How Google makes AI work at Enterprise scale – Miku Jha (Director, AI/ML and Generative AI, Google Cloud)

LLM workflows for product managers: 3 key takeaways (Niloufar Salehi, Assistant Professor at UC Berkeley) – ProductTank SF

The future of product management: Insights from ProductTank San Francisco

Product lessons learned making early moves with AI in media: Lindsey Jayne (CPO, Financial Times)

A year with ChatGPT and product innovation: Navigating the AI landscape

How to keep your head about generative AI (when everyone is losing theirs) by Claire Woodcock

What we get wrong about technology by Tim Harford

Product management in the age of ChatGPT by Yana Welinder

How Canva uses AI-powered features to drive PLG

Building Search Products With Machine Learning – Shakhina Pulatova

ProductTank Ahmedabad

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