5. Our AI journey

Enhanced search

How GenAI can help you ask the right questions and find the right answers

Chances are, GenAI is already helping your employees solve problems they encounter at work, from automating repetitive tasks to surfacing a tricky bit of code. The next generation of AI-powered search and Q&A won’t just help your employees find answers to their questions; it will help them ask better questions.

As we’ve mentioned, you can’t talk about finding knowledge without talking about search, and search has been core to the Stack Overflow experience from the beginning. It was among the first user features we built. From the start, most of our visitors arrived via search engines. Users of Stack Overflow’s public site encounter a massive store of questions and answers, including plenty of duplicates; they have to navigate to the answer they need, using community-contributed comments and votes to find the best solution.

Now, imagine an AI model steeped in the vast store of knowledge on Stack Overflow. That system that could quickly determine what answers users were looking for, help them refine and improve their questions in natural language, and learn from the whole process to improve itself going forward.

Keep reading to understand how AI-powered search works, how it can help with your business, and best practices for implementation.

Ask the right questions

Often, the hardest part of solving a problem is knowing which questions to ask. People who are brand-new to a topic or technology may not know enough to ask the community for help. Getting oriented and up-to-speed enough to ask relevant questions takes time, and employees still have to parse answers they don’t fully understand. They might bounce between several questions and answers before landing on the right solution. Naturally, all of this takes time away from other work these employees could be doing.

The good news is that AI-powered search can cut down the time it takes people to understand and articulate their problem, then guide them in finding the solution they need. A GenAI system trained on your organization’s internal data can provide users with near instant solutions. Users can then ask follow-up questions in a chat format to get additional detail, context, or insight, just as they might work through an issue with a human colleague. That’s where semantic search comes in.

Semantic search, personalized

With recent advancements in GenAI, semantic search technology has taken center stage. Semantic search converts content into numerical vectors based on meaning assigned by machine learning. The search function can then traverse the numerical vectors like a physical space. Semantic search enables faster, higher-quality results and more efficient storage of search data. More importantly, it allows users to search using natural language instead of keyword manipulation.

Semantic search can draw knowledge from a wide array of accurate, trustworthy, community-vetted sources and quickly offer possible solutions. Your employees might need more detailed or personalized answers, depending on the context they’re working in, so semantic search allows them to ask follow-up questions in a natural, conversational fashion. This format also allows employees to clarify and refine their questions as they go.

Garbage in, garbage out

An AI trained on your company’s data streamlines and speeds up onboarding for new employees as well as upskilling/reskilling for existing staff. But as we’ve mentioned, AI can’t make something from nothing. Models trained on outdated, incomplete, or just plain inaccurate information will tend to “hallucinate,” providing nonsensical, incorrect, or irrelevant answers. The old computing adage of “garbage in, garbage out” pretty much sums it up.

For an AI to provide your employees with high-quality answers, it needs access to accurate, up-to-date, and well-organized data. That’s why a knowledge-sharing and collaboration platform like Stack Overflow for Teams is critical to the success of AI-enhanced search and Q&A tools.

Features to look for

In deciding which knowledge sharing and search/Q&A tools to adopt at your organization, there are certain features and capabilities you should look for:

  • Trusted sources: Ensure your GenAI system is trained on high-quality data and implement a system that provides attribution and sources if possible.
  • Personalizable: Configure preferences like length of answer and level of technical detail.
  • Shorter time-to-solution: Find solutions faster without bouncing between answers; solutions can be summarized within a single search prompt.
  • Conversational interface: Easily ask the system for more information; get suggested follow-up questions to continue the conversation or get deeper insights.

Introducing the next generation of search

The next generation of AI-assisted search technology will understand not just what users are asking, but what they actually need to know—and where to find it. At Stack Overflow, where our platform helps technologists search for and find the knowledge they need, we’re now working to build a new, AI-powered Community Search. This new search technology will shorten the time it takes you to articulate and summarize your question and then comb through possible solutions to find the one most relevant to your situation.

  • Instead of a time-consuming process of searching for and parsing information, you’ll get an answer sourced from a wealth of community-validated sources.
  • Solutions can be summarized within a single search prompt.
  • You can ask follow-up questions to work toward a more personalized solution, refining your question as you go.

Enhanced Search on Stack Overflow for Teams is also in the works. Users will be able to enter their questions directly into the search box, with GenAI returning summarized answers based on the combined results of other users’ questions, answers, and long-form content. Responses will include sources and citations, so users can validate the quality of the results. Feedback is a form of reinforcement learning, ​​in which humans apply their judgment and expertise to AI-generated output, coaching the model to improve itself.

If we can help you ask better questions, we can help you find better answers.