Staff+ Engineer: Problem Solving Stories

We have some Staff+ case studies here (all at top tech companies like Roblox):

• [Case Study] Revamping Oncall For 20 Instagram Engineers - Senior to Staff Project
• [Case Study] Building A Meta Internal Tool To Empower An Entire Org: Staff Promotion Story
• [Case Study] Building XFN Alignment Across Multiple Orgs (w/ Staff Engineer Cat Chen)
• Rockstar Software Engineer Story: SW2 -> Principal In Just 4 Years

I also highly recommend this Staff Engineer panel with a bunch of smart people (companies represented: Meta, Google, Airbnb, Activision): Becoming A Staff Engineer In Big Tech (Panel Discussion)

During a conversational AI project, I noticed that our LLM struggled with real-time user interactions, failing to handle context shifts and ambiguous queries effectively. While it performed well in static benchmarks, live data showed high drop-off rates and negative user feedback. I quantified that improving context handling by even 10% could significantly boost user retention and engagement, translating to a multi-million dollar revenue opportunity.

Solving the Problem

I proposed a dual approach of fine-tuning the LLM using reinforcement learning from human feedback (RLHF) and dynamic prompt engineering. We built a pipeline to adjust prompts based on conversation history and captured real-time interaction data to iteratively improve model performance. This required close collaboration with data scientists and engineers to ensure adaptability and responsiveness in user interactions.

Gaining Buy-In

To secure buy-in, I framed the issue as a barrier to scaling the product. I proposed a phased pilot with a small user group to mitigate risk, emphasizing the business value of reduced churn and increased user engagement. This clear alignment with business outcomes made it easier to get leadership support.

Outcome

Our efforts led to a 40% improvement in the LLM's handling of context, driving a 20% increase in user retention. This not only enhanced our conversational AI product but also established a foundation for scaling future Gen AI initiatives. The main takeaways were the value of continuous model adaptation, strategic prompt engineering, and framing solutions in terms of business impact.