empowering Question Askers: How Stack Overflow Leveraged AI to improve Question Quality and Knowledge Sharing
For over 15 years, Stack Overflow has been the definitive resource for developers seeking answers to their coding challenges. Maintaining the high quality of questions on the platform is crucial to its success, ensuring accurate and helpful data for the entire community. Recently, we embarked on a project – Question assistant – to proactively guide users in crafting better questions, ultimately improving the overall knowledge-sharing experiance. This initiative demonstrates our commitment to continuous enhancement and leveraging cutting-edge technology to support our users. This article details the journey, from initial experimentation to full rollout, and the surprising insights we gained along the way.
The Challenge: Maintaining Question Quality at Scale
Stack Overflow receives a massive influx of questions daily. While our dedicated community of moderators works tirelessly to maintain quality,proactively assisting users before they submit a potentially problematic question presented a notable possibility. We aimed to identify questions that might struggle to gain traction – those likely to be closed, edited heavily, or simply remain unanswered – and provide targeted guidance to help askers improve them.
A Hybrid approach: Combining Traditional Machine Learning with the Power of Gemini
Our initial approach focused on building traditional machine learning (ML) models to flag questions based on established quality indicators. These indicators included factors like clarity, specificity, and adherence to Stack Overflow’s guidelines. To extract meaningful features,we employed techniques like term frequency-inverse document frequency (TF-IDF),a method for quantifying the importance of words within a document relative to a corpus. These features were then fed into logistic regression models.
However, we recognized that simply flagging a problem wasn’t enough. Users needed actionable feedback. This is where the power of Large Language Models (LLMs) came into play. We integrated Google’s Gemini LLM into our workflow to synthesize the ML-identified issues and generate personalized, helpful suggestions.
Here’s how it works: when an indicator flags a question, the question text is sent to Gemini, along with pre-defined system prompts. Gemini then leverages this information to craft feedback that directly addresses the identified issue, but is tailored to the specific context of the question. This ensures the feedback isn’t generic, but genuinely helpful to the asker.
Technical Implementation: A Robust and Scalable Architecture
To ensure reliability and scalability, we built a robust infrastructure leveraging azure services. Our ML models were trained and stored within our Azure Databricks ecosystem.In production, a dedicated service running on Azure Kubernetes downloads these models from Databricks Unity Catalog and hosts them to generate predictions in real-time. This architecture allows us to efficiently handle the high volume of questions submitted to Stack Overflow.
We meticulously tracked the performance of the system using Azure Event Hub to collect events and Datadog for logging predictions and results. This data-driven approach allowed us to continuously refine our models and improve the quality of the feedback provided.
Experimentation and iteration: A Two-Phase Rollout
We adopted a phased rollout strategy to minimize risk and maximize learning.
* Phase 1: Staging Ground Focus. We initially launched Question Assistant on Staging Ground, a dedicated area for new users to practice asking questions. This allowed us to focus on users who were most likely to benefit from assistance. We conducted an A/B test, randomly assigning eligible askers to either a control group (no assistance) or a variant group (Gemini-powered feedback). Our initial hypothesis was that Question Assistant would increase question approval rates and reduce review times.
* Phase 2: Stack Overflow with Ask Wizard. Following the staging Ground experiment, we expanded the A/B test to all eligible askers on the main Stack Overflow Ask Question page, specifically those utilizing the Ask Wizard – a tool designed to guide users through the question-asking process. This phase aimed to validate the initial findings and assess the impact on more experienced users.
Unexpected Results and a Pivotal Finding
Surprisingly, our initial metrics – approval rates and review times – did not show significant improvement in the variant group. Though, a deeper dive into the data revealed a compelling trend: a consistent +12% increase in question success rates across both experiments.
Question success, in our definition, means a question remains open on the site and receives an answer or achieves a post score of at least +2. This indicated that Question Assistant wasn’t necessarily making questions easier to approve, but rather making them more valuable to the community – leading to more engagement and ultimately, more answered questions.
This realization was a pivotal moment.
