In the midst of discussions about the revolutionary impact of AI on work, such as enhancing efficiency in everyday tasks and amplifying individual efforts, it can be easy to overlook its limitations. Contrary to its name, generative AI, which can create images, code, text, music, and more, cannot create something out of nothing. AI models are trained based on the information they receive, typically from a large body of text in the case of large language models (LLMs). When trained on accurate, up-to-date, and well-organized information, the AI tends to provide accurate, up-to-date, and relevant responses. Research conducted by MIT has shown that integrating a knowledge base into LLMs improves output quality and reduces instances of hallucinations, which refer to incorrect results ranging from slightly off-base to completely incoherent. Hallucinations can lead to incorrect answers, false information about people and events, and overall misleading responses. Just like the computing rule "garbage in, garbage out, " generative AI follows suit. The quality of the AI model depends on the quality of the training data provided. Outdated, poorly structured, or incomplete data can result in the AI inventing misleading answers that can create difficulties and chaos within an organization. To avoid hallucinations, it is crucial to have a knowledge management (KM) approach that encourages discussion and collaboration. This approach enhances the quality of the knowledge base by enabling collaboration with colleagues to evaluate the AI's responses and refine prompt structure for better answer quality. This interaction serves as a form of reinforcement learning for the AI, whereby humans use their judgment to assess the quality and accuracy of the AI-generated output and help improve it. Structuring queries effectively greatly influences the quality of results obtained from LLMs. Prompt engineering, which involves formulating instructions or questions for AI, is becoming increasingly vital. Both sides of the conversation—prompt and response—can benefit from generative AI in terms of prompt engineering. According to a Gartner® report on Knowledge Management, interacting with intelligent assistants in an iterative, conversational manner enhances knowledge workers' ability to guide the AI during KM tasks and share acquired knowledge with human colleagues. Using AI to centralize knowledge-sharing is essential for a successful KM practice.

AI-powered knowledge capture, content enrichment, and AI assistants facilitate the incorporation of learning and knowledge-sharing practices into everyday workflows throughout the organization. Stack Overflow for Teams, as highlighted in Gartner's Solution Path for Knowledge Management, is a product that can integrate with Microsoft Teams or Slack to provide a Q&A forum with a persistent knowledge store. This platform allows users to post questions, upvote or downvote answers, and maintain a curated pool of knowledge accessible via search—a solution that keeps knowledge sharing central to the flow of work. In another Gartner report on generative AI's impact on developer experience, it is recommended that organizations establish a community of practice for generative-AI-augmented development. This community should collect and disseminate proven practices, including prompt engineering tips and code validation approaches. It is also important for organizations to acquire the necessary skills and knowledge for successful use of generative AI. This involves learning and applying approved tools, use cases, and processes specific to the organization. Generative AI tools are beneficial for both novice and experienced developers seeking to learn or expand their skills. However, there is a complexity cliff where an AI's ability to handle intricate nuances, interdependencies, and full context of a problem and its solution diminishes. LLMs excel at enhancing developers' capabilities and enabling them to work more efficiently, as highlighted by Marcos Grappeggia, product manager for Google Cloud's Duet. They facilitate testing and experimenting with unfamiliar languages and technologies. Yet, Grappeggia emphasizes that LLMs cannot fully replace day-to-day developers. Understanding one's code remains a crucial factor for success. Therefore, the ultimate goal is to develop a KM strategy that harnesses the immense power of AI by refining and validating it with human-derived knowledge. Stack Overflow for Teams is designed specifically for capturing, collaborating, and sharing knowledge, covering a range of topics from new technologies like generative AI to transformations like cloud adoption. Discover how organizations are utilizing Stack Overflow for Teams to build secure and collective knowledge bases that enable scalable learning across teams at stackoverflow. co/teams.