Building LinkedIn’s GenAI Platform — Xiaofeng Wang
AI Summary
Summary of Building LinkedIn’s JI Platform
Introduction
- Presentation by Sh, Manager of JI Foundation.
- Overview of the journey in developing the JI platform, including its significance.
Journey of the JI Platform
- Initial Launch (2023)
- Launched collaborative articles using GPT-4 for content generation.
- Developed gateway for centralized model access and prompt engineering tools.
- Utilized Java for online phase and Python for backend.
- Second Generation Development
- Introduction of “Co-pilot” feature to provide job fit recommendations.
- Transitioned to a unified tech stack using Python.
- Invested in prompt management to create a source of truth for developers.
- Developed conversational memory to track interactions and enhance user experience.
- Launch of LinkedIn Assistant
- Created a multi-agent system to automate tasks for recruiters, such as job postings and candidate evaluations.
- Enhanced Python SDK for distributed agent orchestration, enabling more complex interactions.
- Implemented a skill registry for easy API publishing and invocation.
- Memory Management Enhancements
- Extended capabilities to experiential memory for knowledge extraction and analysis.
- Organized various memory types for improved agent context awareness.
- Operational Insights
- Developed in-house solutions for tracking and analyzing operational data for better agent performance.
Key Components of the JI Platform
- Four Layers: Orchestration, Prompt Engineering Tools, Skills Invocation, Memory Management.
- Ensured best practices and governance via a centralized platform to facilitate developer efficiency and responsibility.
Importance of Building the Platform
- Bridged gaps between AI engineers and product engineers, emphasizing the need for a unified approach in the evolving AI ecosystem.
- Stressed the uniqueness of JI systems over traditional AI systems, where optimization and serving phases merge.
Team Building Recommendations
- Hire strong software engineers with integration skills, prioritizing potential over experience.
- Build diverse teams to promote skill development through collaboration.
- Emphasize critical thinking due to the fast-paced nature of the field.
Key Takeaways
- Tech Stack: Recommended use of Python for better scalability and community support.
- Essential Components: Develop a robust prompt version control and memory system.
- Adoption Strategies: Start with minimal viable products, scale based on immediate needs, and focus on user experience for developer productivity.
Additional Resources
- For more technical details, refer to LinkedIn’s engineering blog posts by Sh and team.