How LinkedIn Built Its Premium AI-Powered Experience

A behind-the-scenes look at how LinkedIn's engineering team incorporated AI into its Premium offering.
"The AI Economy," a newsletter exploring AI's impact on business, work, society and tech.
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Artificial intelligence (AI) is baked into most, if not all, of the social networks we use today. Some use cases are evident, especially for consumer-facing platforms such as Facebook. But what about those sites tailored more towards business and working professionals? I’m looking at you, LinkedIn!

A professional social network doesn’t need an image-generation tool, does it? How will users leverage AI to find employment, grow their network, or close a sale?

In a post, LinkedIn’s engineering team explained their thought process regarding using AI, detailing what worked and what didn’t and previewing what they’re working on next. Read on to learn more.

"The AI Economy" celebrates passing more than 1,000 subscribers.
“The AI Economy” celebrates passing more than 1,000 subscribers.

But first, a warm welcome to the more than 1,000 subscribers to “The AI Economy!” I’m honored you’ve signed up — if you haven’t, you can do that right here — and find this weekly newsletter rewarding. Reach out if there’s anything you’d like to see more or less coverage of or if you have a great story I might want to share here. 🤗

The Prompt

LinkedIn first launched its AI-powered experience for premium subscribers in November 2023. The idea was to create a copilot to help you stay ahead of your professional life, whether that involved changing careers, building a business, learning a new skill or developing your voice.

Among the capabilities promised:

  • The ability to extract salient information from your feed’s posts and tell you what you need to take action on
  • Tailored AI-powered profile writing suggestions and message recommendations to help engage with hiring managers
  • A way for job seekers to tell potential employers about a role at their company they really want

This may seem like an easy implementation, but engineers Juan Pablo Bottaro and Karthik Ramgopal said it was anything but. “We tried many ideas which didn’t really click,” they admitted. Eventually, they hit their eureka! moment, which resulted in the feature set Premium subscribers have today.

How Does It Work?

Bottaro and Ramgopal explain what happens in the background when you tap on those AI-powered questions that might appear next to a post you’ve discovered in your LinkedIn feed. These are intended to help better assess what you’re interested in relating to that post’s topic.

  • It starts with the right agent: LinkedIn examines your query and chooses which of its AI agents can best handle it. Doing so provides tailored responses based on your interests and objectives.
  • Next, intelligence gathering: Using a combination of internal APIs and Microsoft’s Bing search engine, LinkedIn’s AI agent will crawl through the dataset to find answers to your question. “We are creating a dossier to ground our response.”
  • Finally, here’s your answer: With the information in hand, the AI agent will display a response, showing data coherently and informatively. LinkedIn utilizes its internal APIs to present the information smartly, avoiding giant blocks of text and making the experience interactive.

The Wins

LinkedIn attributes its success in building its AI-powered experience to several factors. The first is choosing to utilize Retrieval-Augmented Generation (RAG) to handle user queries, which made building its framework easy to do. Doing so also reduces the odds that AI responses will feature hallucinations, which is likely bad when you’re trying to get business done. Check out my February interview with Kyndryl’s Dennis Perpetua for more information about RAG.

Not wanting to move slowly, the team opted to split tasks across multiple AI agents focused on general knowledge, job assessment, post takeaways, and more. However, doing so created fragmentation costs and made maintaining a uniform user experience difficult.

LinkedIn said it adopted a “simple organizational structure” consisting of a “horizontal” engineering team handling common components and dedicated to the holistic experience to counter this. It also implemented several “vertical” engineering teams responsible for agents handling personalized post summarization, job fit assessments, and display of interview tips.

The Struggles

It wasn’t all rainbows and sunshine for LinkedIn. Bottaro and Ramgopal laid out areas the company found the most difficult, namely when it came to developing guidelines, scaling annotations and handling automatic evaluations.

The first hurdle involved developing rules to ensure “factual but also empathetic” options and responses are displayed and consistent in detail to ensure annotator score uniformity.

The second obstacle involved establishing a better approach to annotations as demand grows. LinkedIn’s linguist team built tools and processes that allowed it to evaluate up to 500 daily conversations while also analyzing quality, hallucination rates, coherence, style, and whether they violated its Responsible AI policy.

The last challenge is considered a work in progress and involves developing a model-based evaluation system to track metrics and allow for faster experimentation.

What’s Next

LinkedIn acknowledged it’s only getting started, especially since this AI-powered experience is still limited to Premium subscribers. Bottaro and Ramgopal reveals some of the things the company is working on, including:

  • Improved automatic evaluation that enables faster iterations
  • Developing a skill registry to dynamically discover and implement AI agents and APIs across LinkedIn’s gen AI products
  • Utilizing in-house, fine-tuned models to handle simpler tasks
  • Producing predictable deployment infrastructure for large language models
  • Better budgeting of token usage

▶️ Read LinkedIn’s piece on building a generative AI product

Today’s Visual Snapshot

Zscaler ThreatLabz's 2024 AI Security Report details the rise of AI-driven phishing attacks. This diagram highlights how malicious actors are taking advantage of the technology. Image credit: Zscaler
Zscaler ThreatLabz’s 2024 AI Security Report details the rise of AI-driven phishing attacks. This diagram highlights how malicious actors are taking advantage of the technology. Image credit: Zscaler

Cloud security provider Zscaler ThreatLabz has published its 2024 AI Security Report (PDF) detailing the rise in AI-driven phishing attacks. The above chart illustrates how malicious actors might use artificial intelligence to drive a ransomware attack, starting with reconnaissance in which gen AI is used to identify vulnerabilities for exposed assets (e.g., “create a table showing the known vulnerabilities for all firewalls and VPNs in this organization.”)

From there, hackers might generate polymorphic malware and ransomware and use deepfakes or phishing attacks to try and compromise systems. Using AI to automate critical portions of the attack chain allows threat actors to develop “faster, more sophisticated, and more targeted attacks against enterprises.”

Zscaler suggests that companies implement a Zero Trust architecture with advanced AI-powered phishing prevention controls to defend their systems against intruders.

Quote This

Biggest safety risk of AI is concentration of power and I doubt this board will help fight it!

— Hugging Face CEO Clement Delangue on X responding to criticism that the new U.S. AI Safety and Security Board does not include a representative of open-source AI.

This Week’s AI News

🏭 Industry Insights

🤖 Machine Learning

✏️ Generative AI

☁️ Enterprise

⚙️ Hardware

🔬 Science and Breakthroughs

💼 Business and Marketing

📺 Media and Entertainment

💰 Funding

⚖️ Copyright and Regulatory Issues

💥 Disruption and Misinformation

End Output

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