quality_assurance · saas · workflow

Stack Overflow builds Question Assistant using logistic regression and Gemini to raise question success rate by 12%

Staging Ground reviewers were repeating the same feedback comments over and over on new askers' question drafts, slowing the review process, and LLMs alone proved unreliable for rating question quality.

How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Question draft submitted
An asker submits a question draft on Staging Ground or on stackoverflow.com for all question askers with Ask Wizard.
Tools used
GeminiAzure DatabricksAzure KubernetesDatabricks Unity CatalogAzure Event HubDatadogTF IDFAsk WizardStaging Ground
Outcome

Question Assistant achieved a steady success rate improvement of +12% across two A/B test experiments, validating positive impact on question quality, and was released to all Stack Overflow askers on March 6, 2025.

What failed first

An LLM-only approach produced repetitive, category-agnostic feedback that did not change when question drafts were updated. A survey-based attempt to build a ground truth dataset yielded a low Krippendorff's alpha score, making the labeled data unusable for training reliable ML models.

Results
Volume+12%
Running sinceMarch 6, 2025
Source

https://stackoverflow.blog/2025/03/12/a-look-under-the-hood-how-and-why-we-built-question-assistant/

How we source this →

Grounding & classification
Source type: technical build writeup
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