# knowledge-management Published articles for knowledge-management. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## How to move documentation from Google Docs to Document360 DevFeed: [How to move documentation from Google Docs to Document360]() Original publisher: [Read original article]() Author: Janeera Published: 2026-08-04T05:34:23Z Content type: tutorial Language: en Sources: [Knowledge Management Tips, Best Practices and More]() Topics: [Documentation](), [migration](), [knowledge-management](), [Google]() Tags: [docs](), [google](), [knowledge-base-software](), [knowledge-management](), [migration]() ### AI overview This article explains why growing documentation becomes difficult to find, version, maintain, and trust in Google Docs. It presents moving to Document360 as a way to organize documentation in a knowledge base and describes signs that a migration may be needed. ### Source excerpt It always starts the same way One Google Doc? Completely manageable. You create ... The post Stuck in Google Docs? Make the move from Google Docs to Document360 appeared first on Document360. ## Your trusted knowledge layer: Introducing Stack Internal's new platform experience DevFeed: [Your trusted knowledge layer: Introducing Stack Internal's new platform experience]() Original publisher: [Read original article]() Author: Phoebe Sajor, Caroline Thomas Published: 2026-07-30T15:10:00Z Content type: release Language: en Sources: [Stack Overflow Blog]() Topics: [Artificial Intelligence](), [data](), [systems]() Tags: [agents](), [ai](), [ai-agents](), [autonomous](), [data](), [efficiency](), [enterprise](), [infrastructure](), [knowledge-engineering](), [knowledge-management](), [platform](), [release](), [se-stackoverflow](), [se-tech](), [stack-internal](), [tokens]() ### AI overview Stack Internal is presented as an AI-native knowledge platform and enterprise memory layer. It ingests knowledge from distributed sources, evaluates its reliability and currency, and delivers trusted information to people and AI agents across existing infrastructure. ### Source excerpt Introducing new Stack Internal capabilities as part of our upcoming platform experience. Our latest release turns your existing foundation of knowledge into enterprise memory that your people, teams, and AI agents can act on. Learn how we're building the trust layer for enterprise AI. ## What Is Enterprise Knowledge Management (EKM) and How Do You Actually Build It? DevFeed: [What Is Enterprise Knowledge Management (EKM) and How Do You Actually Build It?]() Original publisher: [Read original article]() Author: Janeera Published: 2026-07-30T12:23:25Z Content type: tutorial Language: en Sources: [Knowledge Management Tips, Best Practices and More]() Topics: [knowledge-management](), [decision-making]() Tags: [enterprise-knowledge-management](), [how-to](), [knowledge-management](), [knowledge-management-system](), [practical]() ### AI overview A practical guide to enterprise knowledge management (EKM), covering the knowledge organizations hold, how to assess program maturity, the components of an effective system, common failure points, and audits before investing in tooling. ### Source excerpt A senior engineer gives two weeks' notice. She mentions, almost offhand, that she's ... The post What Is Enterprise Knowledge Management (EKM) and How Do You Actually Build It? appeared first on Document360. ## Knowledge Management Trends in 2026: What's Actually Changing DevFeed: [Knowledge Management Trends in 2026: What's Actually Changing]() Original publisher: [Read original article]() Author: Janeera Published: 2026-07-28T15:44:41Z Content type: article Language: en Sources: [Knowledge Management Tips, Best Practices and More]() Topics: [knowledge-management](), [Artificial Intelligence](), [Generative AI](), [Graphs]() Tags: [2026](), [agentic-ai](), [ai](), [consistency](), [data](), [documentation](), [generative-ai](), [governance](), [graphs](), [knowledge-graphs](), [knowledge-management](), [knowledge-management-software](), [knowledge-management-system](), [maintenance](), [metrics]() ### AI overview This article examines 2026 knowledge management trends, arguing that structured, governed knowledge is becoming foundational infrastructure for AI systems. It discusses semantic layers, knowledge graphs, agentic AI, conversational discovery, governance, and self-healing maintenance, while noting a shift toward metrics such as answer quality, search success, freshness, and consistency. ### Source excerpt According to Mordor Intelligence, the knowledge management software market is on track to ... The post Knowledge Management Trends in 2026: What's Actually Changing appeared first on Document360. ## How to make the case for giving your AI Agent system access DevFeed: [How to make the case for giving your AI Agent system access]() Original publisher: [Read original article]() Author: Dawn Perrott Published: 2026-06-11T12:04:14Z Content type: tutorial Language: en Sources: [The Intercom Blog]() Topics: [AI Agent](), [Back end](), [knowledge-management](), [AI Platforms/Deployment]() Tags: [2026](), [agent](), [ai-agent](), [ai-agent-blueprint](), [ai-automation](), [backend](), [customer-service](), [deployment](), [how-to](), [integration](), [knowledge-management](), [management-tools]() ### AI overview The article explains why AI Agents need access to backend systems to move from answering customer questions to completing actions. It argues that connecting support agents to CRM, billing, and order-management systems can enable end-to-end resolution, and describes how Intercom's Procedures with system access handled complex workflows better than fixed, scripted Tasks in some cases. ### Source excerpt Without access to your backend systems, your AI Agent can answer questions, but it can't take action. A customer asks to change their payment plan, they get a clear explanation, but a support rep still has... ## Reciprocal knowledge elicitation: when the interview goes both ways DevFeed: [Reciprocal knowledge elicitation: when the interview goes both ways]() Original publisher: [Read original article]() Author: fidelitas.io Published: 2026-05-18T21:30:00Z Content type: opinion Language: en Sources: [fidelitas.io]() Topics: [Artificial Intelligence](), [Matter]() Tags: [agent](), [ai](), [human-ai-collaboration](), [interview](), [interviews](), [knowledge-management](), [tacit-knowledge]() ### AI overview The article explains reciprocal knowledge elicitation, an approach in which humans and AI jointly extract, refine, and validate expert knowledge. It argues that targeted follow-ups, summaries, contradiction detection, and gap-finding can better capture tacit knowledge than one-way interviews or forms. ### Source excerpt Traditional knowledge elicitation is a one-way street: an expert talks, a system listens. A new wave of human-agent research is rewriting that contract. Here's what reciprocal knowledge elicitation actually means, why it matters for capturing tacit knowledge, and how to design one without losing the plot. ## How Mintlify Replaced Its Internal Wiki With a Slack-Based Knowledge Agent DevFeed: [How Mintlify Replaced Its Internal Wiki With a Slack-Based Knowledge Agent]() Original publisher: [Read original article]() Author: Ani Bruna Published: 2026-03-22T00:00:00Z Content type: article Language: en Sources: [Mintlify Blog]() Topics: [Slack](), [Bot](), [Documentation](), [knowledge-management]() Tags: [agent](), [announcements](), [documentation](), [knowledge-management](), [slack]() ### AI overview Mintlify replaced its internal wiki workflow with a knowledge-base agent in Slack. The agent turns conversations into documentation, enabling the team to migrate pages and contribute updates without leaving Slack. ### Source excerpt Mintlify tripled in size and institutional knowledge was dying in Slack threads. So we built a KB agent that lives in Slack, reads conversations, and turns them into version-controlled documentation without anyone leaving the tools they already use. ## Knowledge management systems for technical teams DevFeed: [Knowledge management systems for technical teams]() Original publisher: [Read original article]() Author: Peri Langlois Published: 2026-03-06T00:00:00Z Content type: tutorial Language: en Sources: [Mintlify Blog]() Topics: [knowledge-management](), [systems](), [Documentation](), [Code](), [Git](), [GitHub](), [GitHub Issues](), [Wiki](), [Google]() Tags: [best-practices](), [code](), [developer](), [documentation](), [engineering](), [git](), [github](), [github-issues](), [google](), [how-to](), [knowledge-management](), [slack](), [technical](), [wikis]() ### AI overview This guide explains developer knowledge management systems as connected ecosystems of code, documentation, architecture records, tests, and operational guides. It describes how these systems help engineers find accurate information and remain productive, and why they must evolve alongside code rather than rely only on static documents. ### Source excerpt Learn what makes developer knowledge management systems successful, and how to build systems that engineering teams will actually use and get value from. ## Mintlify + Claude Opus 4.6: Powering AI-native knowledge management DevFeed: [Mintlify + Claude Opus 4.6: Powering AI-native knowledge management]() Original publisher: [Read original article]() Author: Patrick Foster Published: 2026-02-05T00:00:00Z Content type: article Language: en Sources: [Mintlify Blog]() Topics: [Claude](), [Artificial Intelligence](), [knowledge-management](), [Conversational AI](), [anthropic]() Tags: [ai](), [ai-trends](), [api](), [claude](), [code](), [context-window](), [conversational-ai](), [debugging](), [knowledge-management]() ### AI overview Mintlify describes early testing of Claude Opus 4.6 in its documentation assistant. It reports more concise responses, improved search and tool calling, deeper code understanding, better bug identification, and support for a 1M-token context window. ### Source excerpt How Mintlify is using Claude to deliver more concise, accurate documentation assistance ## Steuerrecht.com delivers client-ready legal analysis with ChatGPT DevFeed: [Steuerrecht.com delivers client-ready legal analysis with ChatGPT]() Original publisher: [Read original article]() Published: 2025-10-27T00:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [ChatGPT](), [AI Chat](), [knowledge-management](), [data]() Tags: [ai](), [article](), [artificial-intelligence](), [chatgpt](), [knowledge-management](), [legal](), [linkedin](), [tax](), [workflows]() ### AI overview Steuerrecht.com uses ChatGPT Business to expand a small law and tax advisory firm's capacity. The team applies it to tax research, marketing, contract drafting, professional publications, and knowledge management, reducing routine work and producing faster client-ready legal analysis. ### Source excerpt Steuerrecht.com uses ChatGPT Business to streamline legal workflows, automate tax research, and deliver faster, client-ready analysis for law firms. ## Using Generative AI to Streamline internal IT Support: A Comprehensive Demonstration DevFeed: [Using Generative AI to Streamline internal IT Support: A Comprehensive Demonstration]() Original publisher: [Read original article]() Author: Davy Costa Published: 2025-05-23T20:50:16Z Content type: tutorial Language: en Sources: [Avenue Code]() Topics: [Generative AI](), [Google](), [Conversational AI](), [AI Agent](), [knowledge-management](), [Documentation](), [pdf](), [browser](), [email]() Tags: [ai](), [ai-agent](), [browser](), [conversational-ai](), [documentation](), [email](), [generative-ai](), [google](), [knowledge-management](), [pdf]() ### AI overview This demonstration describes using Google's Vertex AI platform and Vertex AI Agent Builder to improve internal IT support. A conversational assistant processes internal PDF documentation and provides employees with natural-language guidance for routine tasks involving browsers, email clients, and software settings. ### Source excerpt In the contemporary corporate landscape, technology plays an indispensable role in daily operations, yet managing IT support efficiently remains a persistent challenge. Employees frequently encounter difficulties when searching for precise instructions on routine IT tasks such as clearing browser history, configuring shared micro mailboxes, or adjusting software settings. Traditional documentation methods--often scattered across different locations, outdated, or inconsistent--result in significant inefficiencies, employee frustration, and operational errors. ## Building an AI-Powered Team Knowledge Base with Cloudflare AutoRAG and Next.js DevFeed: [Building an AI-Powered Team Knowledge Base with Cloudflare AutoRAG and Next.js]() Original publisher: [Read original article]() Author: Pavlos Petros Tournaris Published: 2025-04-28T16:02:54Z Content type: tutorial Language: en Sources: [Blueground Engineering blog]() Topics: [Artificial Intelligence](), [Cloudflare](), [Next.js](), [API](), [Front end](), [knowledge-management](), [Code](), [coding](), [Streaming](), [ui](), [Markdown](), [Vibe coding]() Tags: [ai](), [api](), [article](), [cloudflare](), [code](), [coding](), [frontend](), [knowledge-management](), [markdown](), [next-js](), [streaming](), [ui](), [vibe-coding]() ### AI overview The article describes how Blueground's Platform team built an AI-powered internal knowledge base using Cloudflare AutoRAG, Next.js, and targeted in-house development. It integrates Terraform documentation and content from Outline, with streaming, context-aware responses and a responsive frontend. ### Source excerpt Introduction Building an AI-powered knowledge base for your teams is faster, easier, and more cost-effective than you might expect. You don't need to engage expensive knowledge base vendors or invest in heavy bespoke solutions--instead, with emerging tools like Cloudflare's AutoRAG, Next.js, ## My Simple Knowledge Management and Time Tracking System DevFeed: [My Simple Knowledge Management and Time Tracking System]() Original publisher: [Read original article]() Author: Henrik Warne Published: 2024-11-09T16:10:58Z Content type: opinion Language: en Sources: [Henrik Warne]() Topics: [knowledge-management](), [time tracking](), [Shell](), [Command-line interface](), [Git](), [Script](), [cURL](), [Python]() Tags: [commands](), [curl](), [files](), [git](), [how-to](), [knowledge-management](), [notes](), [python](), [script](), [shell](), [simple](), [time-tracking](), [work]() ### AI overview The author describes a simple personal system using notes.txt to record commands, procedures, and other important information, and worktime.txt to track daily hours and tasks. A Python script calculates daily and weekly hours. The notes provide reusable instructions and command examples, while repeated use helps the author remember them. ### Source excerpt I am using a very simple system for remembering commands and procedures, and for tracking what I work on. I have two plain text files called notes.txt and worktime.txt. In the notes file, I write down things that are important ... Continue reading -> ## Build an AI roadmap that actually delivers value DevFeed: [Build an AI roadmap that actually delivers value]() Original publisher: [Read original article]() Author: jesalg Published: 2024-10-15T00:00:00Z Content type: article Language: en Sources: [Jesal Gadhia]() Topics: [AI Strategy](), [AI Development](), [Large Language Model](), [knowledge-management]() Tags: [agents](), [ai](), [ai-assistants](), [business-value](), [career](), [code](), [knowledge-management](), [leadership](), [llms](), [software-development]() ### AI overview An opinion article explains how to build an AI roadmap around customer needs, business value, and technical feasibility. It recommends prioritizing projects with clear return on investment and quick wins, and discusses creative AI tools and systems that synthesize information. ### Source excerpt What a year in the trenches building with LLMs taught me about delivering value with AI ## From Chaos to Clarity: Achieving Personal Knowledge Mastery DevFeed: [From Chaos to Clarity: Achieving Personal Knowledge Mastery]() Original publisher: [Read original article]() Author: jesalg Published: 2024-01-09T00:00:00Z Content type: tutorial Language: en Sources: [Jesal Gadhia]() Topics: [knowledge-management]() Tags: [how-to](), [insights](), [knowledge-management](), [leadership](), [practical](), [productivity](), [tips]() ### AI overview The article shares practical tips for personal knowledge management, beginning with low-friction information capture and describing the author's use of daily notes, journaling, and cross-platform tools to organize ideas, links, code snippets, and tasks. ### Source excerpt We explore practical tips on how to master PKM in order to start getting insights out of information to drive impact ## How Fibery will transform product companies work and knowledge management processes DevFeed: [How Fibery will transform product companies work and knowledge management processes]() Original publisher: [Read original article]() Author: Michael Dubakov Published: 2022-05-25T11:31:57Z Content type: opinion Language: en Sources: [Stories by Michael Dubakov on Medium]() Topics: [Product Management](), [knowledge-management](), [Software as a service](), [Software](), [Development]() Tags: [analysis](), [article](), [future-of-work](), [knowledge-management](), [product-development](), [product-management](), [productivity](), [saas](), [software-development](), [startup]() ### AI overview The article presents a vision for how a future version of Fibery could support product companies. Using a 50-100-person B2B SaaS startup as its context, it describes fragmented tools, disconnected discussions, weak customer-feedback handling, ad hoc feature prioritization, and poor links between specifications and work items. ### Source excerpt We're working on Fibery for 5 years already and are still far from our main goal. What is our main goal? How do we envision the near future of work? Last year I wrote a long article about these topics -- Augmenting Organizational Intelligence, but it's quite abstract and hard to apply to usual use cases. Here I want to narrow the context and show you how a product company can operate having a "future" Fibery. The future is hard to show and explain, but I'll do my best to be as concrete as possible. Context: Product B2B SaaS startup, post-round A, 50-100 people. 🦐 Now -> Many tools Let' me briefly focus on the "now". How this startup most likely operates and what unsolved problems does it have? Typical product development company core toolset: Notion as a company wiki/intranet and product management Jira/Linear for software development Slack as a chat Asana for various projects in marketing and other non-IT teams Intercom: chat + user guide HubSpot as a CRM Canny as a feedback portal Miro as a diagram/whiteboard tool Problems There are many problems, but I just want to highlight several of them as examples. Knowledge lives in many tools (Notion, Miro, Canny, Slack, Jira) Discussions out of context Customers feedback handling process is poor Features prioritization process is ad-hoc and mostly based on gut feelings Connection between features specification and features as work items is weak Problem 1: Knowledge lives in many tools (Notion, Miro, Canny, Slack, Jira) It makes invention much harder since you have to extract it from many tools and put it into some digestible format for the analysis phase. Then you have to jump between these tools: brainstorm something in Miro -> document results in Notion -> have a sudden discussion in Slack -> jump back to Notion to add some ideas and options, etc. Many tools... People tend to seriously underestimate this problem, since reflection about working processes is not something they do often. Status quo is always there and people get ## One Year Working with Guilds DevFeed: [One Year Working with Guilds]() Original publisher: [Read original article]() Author: Tauan Zimmermann Published: 2017-01-05T00:00:00Z Content type: article Language: en Sources: [Trivago]() Topics: [knowledge-management](), [Software Engineering](), [Agile](), [engineering-culture]() Tags: [best-practices](), [collaboration](), [engineering](), [engineering-culture](), [knowledge-management](), [product-development](), [software-engineering](), [teams](), [work]() ### AI overview The article shares lessons from trivago's first year using guilds, or communities of practice, within its Software Engineering department. It explains how guilds help employees with similar expertise exchange knowledge, develop best practices, solve problems, and prevent knowledge silos across multidisciplinary teams. ### Source excerpt It has been about a year since we introduce the idea of guilds in trivago Software Engineering department in Düsseldorf. Here we share some of our learnings with it.