Why is knowledge management fundamental to AI success?

One way to improve accuracy is to provide the AI ​​with the correct context

ness and reduce hallucinations.

In all the conversations about how artificial intelligence will revolutionize work—making daily tasks more efficient, more repeatable, and increasing personal effort— It’s easy to get carried away: What can’t AI do?

Although its name is generative artificial intelligence, artificial intelligence that can create images, codes, text, music, etc. does not come from scratch. Artificial intelligence models are trained based on the provided information, especially for large language models (LLM), which usually require a large amount of text as training data. If AI is trained on accurate, up-to-date, and well-organized information, it will tend to give answers that are accurate, up-to-date, and relevant. Research from MIT shows that integrating knowledge bases into LLM often improves output quality and reduces errors. This means that advances in artificial intelligence and machine learning have not replaced the need for knowledge management, on the contrary, it has made knowledge management even more important

Quality in, quality out

LLMs trained on stale, incomplete information are prone to "hallucinations," i.e. incorrect results, ranging from slightly off-base to completely incoherent. Hallucinations include incorrect answers to questions and false information about people and events.

Generative artificial intelligence also applies to the classic computing rule of "garbage in, garbage out". Your AI model’s training data is critical to it. If this data is outdated, poorly structured, or has holes, AI will begin to produce answers that mislead users, causing trouble or even chaos for your organization

Avoiding illusions requires a body of knowledge , namely:

• Accurate and trustworthy, information quality verified by knowledgeable users
• Stay current and easy to refresh as new data/edge cases emerge
• Context, meaning it captures the context in which solutions are sought and delivered
• Continuous improvement and self-sustainability

Knowledge management that supports discussion and collaboration ( The KM) approach can improve the quality of your knowledge base because it allows you to collaborate with colleagues to review the AI's responses and refine the prompt structure to improve answer quality. This interaction is a form of reinforcement learning in AI: humans apply their judgment to the quality and accuracy of the output generated by the AI, and help the AI ​​(and humans) improve.

Ask the right questions

When using LLM, the structure of the query affects the quality of the results. That’s why prompt engineering (knowing how to structure queries to get the best results from AI) is becoming a critical skill and an area where generative AI can help both sides of the conversation: prompts and responses.

According to the June 2023 Gartner report "Solution Pathways for Knowledge Management," prompt engineering, the act of formulating instructions or problems for artificial intelligence, is quickly becoming a critical Skill. Interacting with intelligent assistants in an iterative, conversational manner will improve the ability of knowledge workers to guide artificial intelligence to complete knowledge management tasks and share the acquired knowledge with human colleagues

centralized knowledge sharing using AI

Capturing and sharing knowledge is critical for knowledge management practices to thrive. AI-driven knowledge capture, content enrichment, and AI assistants can help you introduce learning and knowledge sharing practices to your entire organization and embed them into daily workflows.

According to Gartner’s Knowledge Management Solution Path, products like Stack Overflow for Teams can integrate with Microsoft Teams or Slack to provide Q&A forums with persistent knowledge storage. Users can post questions directly in the community. Answers are voted up or down, and the best answer is pinned as the top answer. All answered questions are searchable and can be curated like any other knowledge source. This approach has the added advantage of making knowledge sharing central to the workflow

According to another Gartner report, "Assessing How Generative AI Improves Developer Experience" (June 2023), it is recommended that organizations collect and disseminate the use of generative AI by establishing a community of practice for generative AI-enhanced development Proven practices for tools such as quick engineering techniques and code verification methods. The report further recommends that organizations ensure they acquire the skills and knowledge needed to successfully use generative AI by learning and applying organization-approved tools, use cases and processes. #Generative AI tools are great for new developers and experienced developers looking to learn new skills or expand existing ones. But there’s a complexity cliff: after a certain point, AI’s ability to handle the nuances, interdependencies, and full context of a problem and its solution declines

In the recent past In an episode of the Stack Overflow podcast, Marcos Grappeggia, product manager for Google Cloud Duet, said, “LLM is very good at empowering developers to do more and move faster.” He noted that this includes testing and trying to go beyond Comfort zone language and technology. But Grappeggia also warns that LLM isn't a good replacement for everyday developers...if you don't understand your code, that's still a recipe for failure

This complex cliff is where you need humans, with their capacity for original thought and ability to exercise empirical judgment. Your goal is to develop a knowledge management strategy that harnesses the immense power of artificial intelligence by refining and validating it on human-made knowledge.

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