The Cherryleaf Podcast

Cherryleaf
The Cherryleaf Podcast

Cherryleaf’s podcast on technical writing and becoming a better business communicator. www.cherryleaf.com

  1. 13 APR

    157. The Rise of Autonomous AI Agents and What It Means for Technical Writers

    🔍 Episode Overview In this episode, Ellis explores how AI agents, especially autonomous AI agents, are reshaping the landscape of technical communication. What are they? How do they differ from traditional AI tools? And crucially, what does their rise mean for technical writers? Blending two recent blog posts, Ellis walks us through emerging tools like Manus, Opera's browser AI, and AgentQL, and what these changes mean for how we create, structure, and deliver documentation. 🧠 What You'll Learn What AI agents and autonomous AI agents are — and how they're evolving The four key traits of autonomous agents: Insights from new AI tools 🧰 Impact on Technical Writers Ellis explores three main ways AI agents could change the role of technical authors: Content for Autonomous AI Consumption Structuring content for AI readability Multimodal delivery (text, audio, UI elements) Built-in accessibility for dynamic adaptation Documentation as Agent-Ready Data Writing docs as if they were APIs Emphasis on semantic structure, metadata, and clarity AI Agents as Co-Creators Personalised content generation Agent-driven feedback loops Enhanced content curation and adaptation tools 🗨️ Key Quotes "Autonomous is the key word. It signifies something that’s self-governing, that can operate independently, with little human oversight." "The real transformative potential of AI lies in autonomous AI agents." "Rather than being replaced, technical writers could become the architects of AI understanding." 🔗 Resources & Links 📄 Blog post: The Rise of Autonomous AI Agents 📄 Blog post: Meet Your Future Co-Worker – AI Agents 🌐 Manus AI 🌐 Opera’s AI-Powered Browser Agent 🎤 Rachel Lee Nabors on AgentQL The Race Is On to Redesign Everything for AI Agents 📧 Contact: info@cherryleaf.com A transcript will be posted to our blog. 📢 Stay Connected For more insights into the future of tech comms, AI tools, and professional development for technical writers, visit cherryleaf.com or follow us on LinkedIn and Twitter.

    37 min
  2. 12 MAR

    156: Privacy and AI: Risks and Solutions for Technical Writers

    Summary: In this episode, Ellis Pratt explores the critical issue of data privacy for technical writers using AI tools and chatbots. He delves into the potential risks, from data leaks and copyright infringement to compliance violations and intellectual property concerns. The episode also provides practical solutions and strategies for mitigating these risks, empowering technical writers to leverage AI responsibly and ethically. Key Discussion Points: The Promise and Peril of AI: AI offers significant productivity gains for technical writers (content creation, first drafts, automation of tasks), but introduces critical privacy risks. Potential Risks of Using AI: Data Leaks: Inputted data becoming part of the AI model, accessible to others. Copyright Infringement: AI generating content based on competitor data. Data Breaches: Risk of AI providers being hacked. Data Sovereignty: Data stored in different countries potentially conflicting with regulations. Compliance Violations: Risks related to regulated industries (healthcare, finance). Intellectual Property Rights: Ambiguity over who owns AI-generated content. Practical Solutions and Mitigation Strategies: Sanitising Content: Replace sensitive data (API keys, product names) with placeholders. Generic Examples: Use generic rather than actual customer data. Limiting Data Input: Provide only the minimum amount of data required. Review and Redact: Carefully review content before inputting to AI. Check Public Domain Status: Determine if the content is already publicly available. AI Provider Privacy Policies: Review data retention policies and opt-out options. Choosing Secure Tools: Select tools with better data deletion options (e.g., Google GeminiAI Studio, Claude). Managing Data Controls: Understand how to control data collection settings (e.g., ChatGPT). Private/Managed LLMs: Consider private, self-hosted, or managed AI models for sensitive data. Develop Policies and Procedures: Create guidelines for team use of AI, tiered approaches based on document sensitivity. Content Filters: Implement filters to check for sensitive information. Audits and Assessments: Engage IT security for impact assessments and security audits. Actionable Takeaways: Prioritise Data Sanitisation: Make it a core practice before using any AI tool. Review Privacy Policies: Understand the data handling practices of your AI providers. Implement Security Measures: Protect proprietary and confidential information through policies, technology, and human oversight. Collaborate with Security and Legal: Engage relevant internal teams to ensure compliance and minimize risk. Start Small and Stay Informed: Gradually introduce AI with low-risk documentation and keep up to date on the latest privacy risks and solutions. Quotes: "AI and chatbots offer in technical writing…a huge promise of a way to be more efficient and more effective in what we do. But…we do need to be aware that there is a privacy risk, and we need to address that." "AI…is both a powerful productivity tool and a potential risk. So we need to think about those two aspects and manage it." "So we're going to be on a tightrope, a privacy tightrope." Want Help Improving Your Documentation? Cherryleaf specializes in fixing developer portals and technical documentation. If you're struggling with user feedback, contact us at info@cherryleaf.com for expert guidance.   CC Flickr image: Stock Catalog

    22 min
  3. 12 FEB

    155. Dealing with Criticism as a Technical Writer

    In this episode of the Cherryleaf Podcast, we explore the challenges of receiving and responding to criticism as a technical writer. Documentation plays a crucial role in user experience, and receiving feedback—whether constructive or harsh—can be an opportunity for growth. We discuss practical strategies for handling feedback, evaluating its validity, and implementing improvements to enhance documentation quality. Key Topics Covered: ✅ Why receiving feedback (even negative) is better than receiving none ✅ How to separate personal feelings from professional criticism ✅ The importance of acknowledging user feedback and addressing concerns ✅ Types of criticism: Constructive vs. Unconstructive ✅ Methods for evaluating the validity of feedback ✅ Tools and techniques to measure documentation quality (e.g., IBM Quality Matrix, analytics, usability testing) ✅ Addressing common documentation challenges: clarity, findability, audience mismatch, and linking ✅ Steps for implementing improvements and tracking their impact ✅ Preventative measures for reducing future criticism Key Takeaways: Criticism is not personal – It’s about improving the documentation, not attacking the writer. Acknowledging feedback is crucial to building trust and ensuring continuous improvement. Evaluating feedback critically helps differentiate between valid concerns and personal preferences. Quality measurement techniques (analytics, support ticket trends, usability testing) can validate feedback. Structured improvements through linking, clearer writing, audience targeting, and prioritization can make a big impact. Continuous monitoring is necessary to ensure long-term effectiveness. Mentioned Resources & Tools: 🔹 IBM Quality Matrix for documentation assessment 🔹 "Every Page is Page One" by Mark Baker 🔹 Google Analytics & support ticket analysis for measuring documentation success 🔹 Usability testing tools (e.g., video session tracking) 🔹 Style guides, templates, and content governance Want Help Improving Your Documentation? Cherryleaf specializes in fixing developer portals and technical documentation. If you're struggling with user feedback, contact us at info@cherryleaf.com for expert guidance.

    20 min
  4. 11/12/2024

    154. Trends for 2025 and beyond in technical writing

    In this episode, we reflect on 2024's trends and explore what may be coming in the world of technical writing and content development in 2025. From the impact of AI to evolving policy requirements and shifting market dynamics, we look into the key factors shaping the field. Reflections on 2024 Overview of the year's major trends in technical writing and client demands. Changes in government spending and its influence on projects. Increased demand for documentation in AI, e-commerce, and SaaS sectors. AI and Technical Writing How generative AI is reshaping technical documentation workflows. Specific use cases for AI in content creation. Challenges and potential pitfalls of AI-based solutions like chatbots. Policy and Procedure Writing Emerging legal requirements influencing documentation needs. Shifting from static policy documents to active communication projects. Training and Development Future Trends and Predictions The growing importance of API documentation and developer portals. Increasing focus on user-friendly, contextually aware documentation. The interplay of economic trends and cross-border trade on technical writing projects. Get Involved Have your own ideas about the future of technical writing? We'd love to hear from you! Reach out via email or connect with us on our social platforms. Email: info@cherryleaf.com for enquiries or to share your predictions for 2025. Thank you for tuning in! We wish all our listeners a joyful holiday season and a prosperous New Year. Stay tuned for more insights in our upcoming episodes. Website: Cherryleaf.com

    25 min

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5
out of 5
4 Ratings

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Cherryleaf’s podcast on technical writing and becoming a better business communicator. www.cherryleaf.com

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