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You are at:Home»AI»Analytics can solve the product problem of AI applications
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Analytics can solve the product problem of AI applications

techtost.comBy techtost.com5 December 202303 Mins Read
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Analytics Can Solve The Product Problem Of Ai Applications
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Henry Scott-Green
Contributor

Henry Scott-Green is co-founder and CEO of Context.ai, the assessment and analytics platform for LLM applications.

Sundar Solai is a product manager turned technical writer. He was previously responsible for YouTube’s autoplay recommendation algorithm, and before that, worked on feature sharing for Google Photos.

Large language models (LLMs) are becoming a commodity. A year after ChatGPT’s release, there’s a simple formula for launching an AI assistant: Stick a wrapper around GPT-4, connect it to a vector database, and call some APIs based on user input.

But if that’s all you do, don’t be surprised if your app struggles to stand out.

Technology alone is not a viable moat for AI products, especially with the barrier to entry continuing to fall. Everyone has access to mostly the same models, and any leaps in technical knowledge are quickly copied by the competition.

The application layer is the real differentiator. Companies that identify and address genuine user problems are better positioned to win. The solution may look like another chatbot, or it may look completely different.

Product and design experimentation is the often overlooked path to innovation.

TikTok is more than ‘the algorithm’

Although not a productive AI application, TikTok is the perfect example of product ingenuity as the unsung hero.

Product and design experimentation is the often overlooked path to innovation.

It’s easy to attribute the app’s success entirely the algorithm. But other recommendation engines are also incredibly powerful (take it from two former YouTube product managers).

At their core, all these systems are based on the same principles. Recommend content similar to what you already like (content filtering) and recommend content that people similar to you enjoy (collective filtering).

TikTok wouldn’t be what it is without packaging its algorithm in an innovative way: an endless stream where viewers vote frictionlessly with their fingers. With an emphasis on short-form video, this product decision boosted the rate at which TikTok could learn user preferences and feed data into its algorithm.

It wasn’t just that. TikTok also led the way with best-in-class creator tools. Anyone can film and edit a video directly from a smartphone. no video production experience required.

Today, the competition for short-form video is more about the ecosystem each app offers. Having an attractive algorithm is the table bets. you need a loyal user base, creator revenue share, content moderation and other features to complete the platform to stand out.

Analytics Applications Column Column EC EC AI Generative AI LLMs pmf problem product solve
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