Is conversation still a useful metaphor for human-machine interaction?
A collection of thoughts & ideas on alternative metaphors for human-GenAI-interaction.
Hoihoi! I'm Maaike Groenewege: natural language interface designer, linguist, and owner of about 50 fountain pens. This space is where I think in public about language, human machine interaction, AI and stuff.
Stream
A collection of thoughts & ideas on alternative metaphors for human-GenAI-interaction.
Reading notes on cognitive assemblages, anthropocentrism, and the case for extending cognition beyond human consciousness, from bacteria to AI.
Extracting meaningful entities and relations, combining thematic analysis with TAO topic mapping in three passes.
On the importance of precise terminology and definition, and a brief exploration of dialectic thinking. It's humans that need to learn to reason, not LLMs
Some kind of reading log for Bacteria to AI, and perhaps even a reminder to actually read.
Being PO, scrum master, prompt designer, tester, eval writer and UX designer in a team of 3 means rethinking roles and processes on a daily, almost hourly basis.
Claude Code is reintroducing structured, constrained input: buttons, numbered options, limited choices. Which is actually IVR. And that might be exactly right.
On the tension between cultivating a tidy digital garden and letting the chaos of ideas coexist. How AI-assisted growth pulls at the time and attention I'd rather spend writing.
On the joy of finding a book that speaks to you
Testing the 1M context window by loading the entire garden and running thematic analysis, comparing fresh analysis against graph-augmented analysis.
Testing Claude Code's new scheduled tasks feature by automating weekly garden health checks.
Seven themes and one emerging pattern discovered by loading all 244 garden files into a single context window with no prior structure.
What happens when you start from embeddings instead of reading? Comparing cluster-based discovery against the fresh thematic analysis.
What if we modeled human-AI interaction on thematic analysis rather than conversation? And what if we used that same method to discover themes in a knowledge garden?
Task-based reference for maintaining this digital garden: writing, tagging, linking, mapping, sharing, publishing.
A lifecycle metaphor for how content enters, matures, decays, and recycles in the garden.
How the explore map got stable positions and pinned territories, so the landscape doesn't shift on every rebuild.
How hub/develops relations replace tag-based project structure with an explicit ontology, and what that means for the three kinds of connections in the garden.
Why embedding models cluster documents by format instead of topic when content lengths vary, and what to do about it.
Running LLM-based key phrase extraction on all 92 garden items. What the results reveal about phrase overlap, short-text limits, and the role of key phrases in the knowledge graph.
What key phrase extraction is, how it evolved from word counting to LLM prompting, and which approach fits a small multilingual knowledge garden.
How to go from "everything is related to everything" to a useful set of link candidates. Data analysis, filtering strategies, and the research behind them.
A non-coder's guide to text embedding models, from word counting to semantic understanding, surveyed for use in a personal knowledge garden.
Reading notes from Apple's Saga papers on building knowledge graphs at scale, and what transfers to a personal digital garden.
Can a chatbot-style UI work without generative AI, using decision trees, hyperlinking, and serendipity instead?
Standard quantization damages non-English languages disproportionately. A Dutch-first approach to model compression shows there's a better way.
Academic research on embeddings hasn't caught up with how PKM tools actually use them. There might be something worth writing here.
A living list of ideas and plans for this garden.
Should a digital garden be searchable and useful, or is there beauty in letting people explore?
Apple's hybrid batch-incremental knowledge graph platform. Key inspiration for building smarter connections in this garden.
Language rules for how I talk about AI in my work.
When did we stop caring about who actually wrote the words?
A mechanism that catches your train of thought. Something that responds in another space.
What's the right metaphor for AI's role in my writing process?
Why I built a digital garden: non-linear thinking, early web nostalgia, and the need for a personal space on the internet.
What if I wrote a comprehensive guide on how I built this site?
Before 2022, it was pretty clear whether you were talking to a bot or a human. With the arrival of ChatGPT, this changed – radically. With a user interface that combines the gift of the gab with information that's not necessarily accurate, designers are faced with a novel challenge. How do we design for interfaces that are so convincing that people instinctively drop their guards and trust them more than might be good for them? And do our traditional design paradigms still serve us here?
An update on my favorite AI-tools...a never ending overview
So...should we all go and explore context engineering? Yes, we should. And chances are that you've been doing it forever already.
Designing for negative space
A technical exploration of DeepSeek's content moderation and reasoning capabilities through testing its responses to sensitive geopolitical questions about Taiwan's political status. I discover that prompting DeepSeek in Dutch triggers the model's reasoning mechanism, revealing internal moderation instructions. By using linguistic workarounds like misspellings that exploit tokenization differences, I expose DeepSeek's internal guidelines for handling Taiwan-related questions, which include instructions to balance factual information with political sensitivity while avoiding language implying support for independence. This demonstrates that censorship occurs at the application level rather than within the model itself, similar to how ChatGPT handles restricted content. The model's reasoning shows it is capable of nuanced discussion about sensitive geopolitical topics, but the application layer prevents users from accessing these capabilities. I plan follow-up testing via API access, which may operate under different moderation policies than the web application, to further investigate the technical architecture of AI content moderation.
A demonstration of Google's NotebookLM interactive podcast feature, where I participate as a guest in an AI-generated podcast discussion about the Coconut research paper on latent reasoning in large language models. NotebookLM now features an interactive podcast mode (beta) that allows users to join AI-generated podcast discussions about uploaded documents. The Coconut paper introduces a breakthrough method where AI can reason in continuous latent space rather than through explicit chain-of-thought language, potentially improving reasoning for complex problems. I raise philosophical questions about verification of hidden reasoning, and critical concerns about transparency, compliance, and explainability. Latent reasoning creates a black box problem: we see inputs and outputs but cannot observe the reasoning process. I also test the feature's limits by requesting humor, making Monty Python references, and asking the hosts to switch to Dutch, revealing important insights about inclusivity, turn-taking, and accessibility in AI-generated conversations. Despite current limitations where AI hosts can become condescending or defensive, the feature represents a significant step toward more natural, bidirectional conversations with documents and AI systems.
The strawberry problem, and how one model gets it right.
A rant about LinkedIn's AI buttons.
Exploring research papers with Claude artefacts during a lunchbreak.
Building an interactive tutorial with Claude on a Sunday afternoon.
A comparative demonstration of how Claude, ChatGPT, and Gemini respond to a web app development request, showcasing Claude's superior capability to generate interactive prototypes and code. Claude outperforms ChatGPT and Gemini by directly generating interactive web app prototypes with a single prompt, rather than just providing plans or design suggestions. Claude creates functional React-based applications with visual mockups, progress bars, and interactive quiz elements in response to simple one-line prompts. The AI demonstrates impressive iterative capabilities, enhancing initial suggestions with more detailed content, interactive elements, and SVG graphics upon request. Claude seamlessly converts React components into static markdown files for alternative platforms, showing flexibility across different deployment methods. I am seriously considering switching from ChatGPT to Claude as my primary LLM due to Claude's superior practical implementation capabilities.
New roles, skills and tools for content professionals in the age of generative AI.
Episode 3 of the Maai & AI series, on how LLMs work and some of their risks.
Episode 2 of the Maai & AI series, exploring the different types of AI.
Episode 1 of the Maai & AI series, on the different types of prompting.
A practical guide to designing better AI interactions with documents using a systematic four-step approach that prioritizes content analysis, user needs, and task design. Content-driven interaction moves beyond simple Q&A patterns to create dynamic, purpose-driven AI interactions that leverage the intentional design embedded in documents. Analyzing your source content is the critical first step often overlooked: understanding what is in your documents, how they are structured, and what information is relevant prevents hallucinations and improves AI accuracy. The four-step methodology (analyze content, analyze users and tasks, design role and task, design interaction) creates a systematic approach to building RAG systems that actually serve user needs. Practical implementation includes welcome messages, rephrase-and-expand techniques, chain-of-thought reasoning, and step-by-step workflows that guide users through complex information.
So if it's not a case of LLM hallucination, what did happen?
Will we ever solve hallucinations in LLMs?
Stress-free preparation...could it really work?
A hands-on exploration of Microsoft Copilot for PowerPoint, testing its capabilities for presentation creation and editing, with humorous comparisons to the legacy Clippy assistant. Copilot has significant limitations in understanding context and user intent, particularly with non-English content and specific editing requests. The AI assistant struggles with co-reference resolution, making it difficult to understand which slide or element I am referring to. Copilot cannot directly access or interact with PowerPoint Online, limiting its functionality for cloud-based workflows. The fallback responses are repetitive and lack conversational variety, making the user experience frustrating. Despite being positioned as an AI assistant, Copilot performs worse than the legacy Clippy assistant from the 1990s in practical usability. Dutch language support is officially unavailable, creating barriers for non-English users even when prompting in English. My experience raises questions about the readiness of Copilot for PowerPoint as a production-ready tool for content creators.
An analysis of Chevrolet's GPT-based chatbot vulnerability and how the company successfully addressed the security issues through proper monitoring and design improvements. The initial vulnerability was not a sign of stupidity, but a necessary part of the testing process that all chatbots must undergo. The real measure of competence is not whether vulnerabilities exist, but how quickly and effectively they respond. I test the updated bot and demonstrate practical prompt engineering techniques for controlling chatbot behavior, including defensive prompting, topic maintenance, and firm responses to abuse. GPT-based solutions alone are not sufficient for high-risk transactions: a hybrid approach combining multiple design techniques is more effective.
Our human-centered way of thinking about the writing process might not necessarily be the most logical for prompting LLMs.
A critical analysis of Google Bard failing to adhere to conversational principles by withholding relevant information about Gemini model availability in the EU. Bard violates Grice's Cooperative Principle by failing to proactively communicate that Gemini is unavailable in the EU due to regulatory reviews. The issue is not a hallucination but a failure of the maxim of quality: Bard does not provide necessary information unless explicitly asked. Users must already know the answer to their question before asking it, creating a paradoxical and unhelpful interaction pattern. Bard in Europe still runs on the older Lambda/PaLM model, not the newer Gemini model available in other regions. This violation of conversational principles is arguably worse than generating false information because it presupposes user knowledge.
Join me in a fun and interactive prompt session! We'll de-bias Dall-e, and create images that are more like us.
Do we really want US moderators to rewrite Dall-e's prompts?
DALL-E 3 is still digitally challenged.
There's a prompt for that! A quick tutorial for designing conversational system prompts.
A tutorial on three essential prompting techniques to make ChatGPT more conversational and natural. I demonstrate the problem with ChatGPT's default responses, which are too long, impersonal, and unengaging for conversational interfaces. The three tips: use OpenAI Playground instead of ChatGPT for better control over LLM behavior and access to system prompts, define a specific expert role in the system prompt rather than the generic helpful assistant since research shows expert roles result in higher quality answers, and prompt for specific behaviors like conciseness, staying on topic, and ending messages with questions to drive conversation forward. I use structured delimiters to organize prompt sections and demonstrate how each behavioral instruction refines the bot's personality and interaction style. The process is like chipping away at a block of marble: each prompt chips away at behavior, domain, interaction models, and persona until you get the conversational bot you want.
Did I get this right? I can only use the ChatGPT browse feature when I let OpenAI collect my data?
Usability challenges of writing with AI: let people write!
A candid reflection on the challenges of course creation, AI tools, and the mismatch between linear workflows and my non-linear thinking style. I struggle with completing online courses due to procrastination, changing circumstances, and self-doubt about content value. AI writing tools like Jasper.ai can be counterproductive for non-linear thinkers, requiring extensive prompting that defeats the purpose of automation. Linear workflows imposed by course creation platforms and AI tools conflict with my chaotic, organic thinking process. System prompts have become invaluable personal tools for managing rumination and decision-making in my daily life. I am actively seeking to design custom system prompts that accommodate my unique thought patterns rather than conform to existing frameworks.
The top 5 challenges that conversation designers face, based on 300 real answers.
An onboarding checklist for conversation designers.
A comprehensive walkthrough of Perplexity AI, a generative AI search platform that combines web search capabilities with language models to provide verified sources and contextual follow-up queries. I explore the clean, minimal interface, the two distinct modes (Knowledge Retrieval for answering questions and Co-pilot Mode for task execution), semantic search facets (Academic, Wolfram Alpha, YouTube, Reddit, Wikipedia), and the AI Profile personalization feature. The platform maintains transparency by consistently referring to itself as Perplexity rather than using first-person pronouns. Integration with Wolfram Alpha provides computational power that traditional language models lack. I use this daily across multiple devices and it is quickly becoming my favorite generative AI search tool.
Blind prompting vs. prompt engineering — and what it means for conversation designers.
Wow, that went down fast!
You built a conversational interface and then expected people to use it just for search?
My favorite resources for building a curious and critical frame of mind.
Are you a writer scared that you might soon be without a job? Fear not! We need more books for AI to work properly!
Both can access the internet and retrieve recent information. Let's take them for a test drive.
Why does ChatGPT seem to 'know' things it shouldn't? The reason might be much more mundane than you think.
Surprisingly few people mention the fact that ChatGPT is, in the end, one big word-string-generator on steroids.
Can ChatGPT take over some of my work as a conversation designer? Can it create an intent model for me?
Let's build that classic hello world of conversation design: the Pizza Bot! Can we get ChatGPT to play along?
Part 1: you can call me Al. A conversation designer's first encounters with ChatGPT.
As a conversation designer, should I know how to code? Some pros and cons.
My personal highlights from the European Chatbot and Conversational AI summit.
Just started as a conversation designer? Doubting whether you're doing things right? No worries, you're not alone!
Looking to fill up your convo design toolbox? Here are 7 skills to take your conversational AI career to the next level.
Looking back at 2 years of freelance work as a conversation designer and community builder.
From now on, conversation designers, linguists, bot builders and language lovers can discuss their favorite topics on Clubhouse!
5 checks to keep your content on track — and your clients happy.
Relax, it's not about you :-) How to handle feedback on your content without losing your mind.
Adopting Agile methods? Avoid the pitfall of copying IT scrum to content teams.
Quickstart SSML les 2: de SSML-simulator.
Quickstart SSML les 3: de basiselementen.
Quickstart SSML les 4: SSML invoeren in Dialogflow.
Quickstart SSML les 5: moeilijke woorden.
Conversation design is a career on the rise! Do you want to know how to become a chatbot or voice designer? Read on!
Voor taalkundigen, conversation designers en iedereen die iets heeft met taal!
Sketchnotes of the Voicelunch session with Brian Roemmele — the best voicelunch I never attended.
Conversation design is een vak met toekomst! Maar wat doet een conversation designer precies? En welke skills heb je ervoor nodig?
Quickstart SSML les 1: intonatie.
Facebook's AI chatbot might just be saying what you want it to say…
A presentation exploring my transition from technical writing to conversation design, discussing how communication professionals are evolving their roles in the digital landscape. I explain that conversation design goes beyond simple chatbot scripting or dialogue trees, involving understanding user needs, communication patterns, and designing interactions that feel natural and helpful. Technical writers are well-positioned for this transition because their core skills, clarity, user focus, and structured thinking, are exactly what conversation designers need. The shift is not about learning entirely new skills but about applying existing expertise in a new context, leveraging experience with documentation, user guides, and information architecture while expanding into dialogue design, tone and voice development, and interaction patterns. The future of technical communication is conversational, and the intersection of technical writing and conversation design represents how organizations will interact with their users in an increasingly digital world.
An intro to the Natural Conversation Framework for modeling conversational agents.
Zuigt Zoom je leeg? Zo blijf je overeind en wakker in een online vergadering.
Want to know what you can do to make your voice action sound more speech-like? Part 2 covers backchannelling, discourse markers, and deixis.
How to build Coronabots responsibly — if you want to build one at all.
The original woman in voice. On international women's day, a tribute to the first named composer in history.
A primer of voice and speech terms for conversation designers. Part 1 covers breath, prosody, pitch, tone and intonation.
Klinkt jouw Google Assistant ook zo buiten adem? SSML helpt!
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