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Let me build upon your notes by sharing my experience. I started with Generative AI about 2.5 years ago—doing the homework, so to speak, by studying LLMs and learning Prompt Engineering. In truth, since these were tools released as public betas, there was little to study and much to experiment with. Just consider how prompt engineering is going to become obsolete (?) now that new models from OpenAI or Perplexity autonomously apply the "chain of thought" without our instruction. So, I'll attempt to report my experience in bullet points (for work, I create content and handle communication, so my experience is less tied to development and more to storytelling—occasionally data storytelling as well).

1. RESEARCH So far, more frustration than enlightenment. In the past, PERPLEXITY.AI gave me factually incorrect or crude answers. However, even just to initiate a search—to be rigorously continued by other means—I'd say generative search should be kept on the radar, awaiting whoever will make it take the real leap forward.

2. COPY EDITING Now indispensable, both with the LLMs included in my Notion Notebook —currently Claude and ChatGPT — and with Gemini (less). I tidy up notes, adjust style, check spelling and grammar, and, like you, use it for brainstorming, especially with formats. For example, when I ask ChatGPT to help convert an article into a format compatible with a narrative podcast, providing detailed specifications of the desired result, the output is valid (much more than Gemini does today, even with its brand new NotebookLM app.

3. TRANSLATION Where I've seen the greatest progress, and where, in truth, we could see the light at the end of the tunnel even five years ago. In Germany, where I live, I was among the first users of DeepL, which has a proprietary model and was born vertically, developing around complex knowledge domains. So, in addition to the triad of ChatGPT, Claude, and (to a lesser extent) Gemini, DeepL is part of my daily experience. Here, however, a different approach is needed for each language: it's evident that the so-called minor languages (minor means: less spoken) have had to rely on local models to achieve valid results — I'm thinking of Hebrew, which I'm familiarizing myself with now, and which brings me to the fourth point of my experience.

4. LEARNING particularly language learning: LLMs, used well and with a grain of salt, can radically change the world of learning and teaching. The examples are nearly infinite for teachers who want to experiment with developing syllabus, exercises, games and Co.. I use ChatGPT in particular as support for my learning—as a complement to the lessons I receive via Zoom from REAL HUMAN teachers. I've joyfully discarded apps like Duolingo. Why? Because just as I've grown tired of social media's manipulative design — and in fact, I'm here on Substack as a quiet harbor that has restored my pleasure in communicating — I've also grown weary of intrusive apps.

5. I also do AUDIOVISUAL communication. This topic could be extensively discussed, but I'll be brief: prosumer design tools that have implemented AI, like CANVA, and other more specialized ones like RUNWAY, don't reinvent creativity. However, for those who need to create decent content in reasonable timeframes, they've put unimaginable powers in the hands of me and my colleagues. And then, of course, there's the photographic and audio editing side. I have my small list of tools that I use almost daily—e.g., CAPCUT (yes, the one made by Bytedance!) or ELEVENLABS for voices and sounds, ADOBE FIREFLY and Runway for some image creation/editing/motion graphics.

CONCLUSION You're right. NEVER use AI to create from scratch. Never ask a black box to replace your mind or your creativity. But for those who have always had a hands-on approach and love to produce as much of their own work as possible, generative intelligence is truly a superpower. Using it to create relevant and valuable content, or junk... well, it depends, as always, on us and our free will. I hope these notes help and are the beginning of an illuminating thread.

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Thanks Evelina ! I'm wrong to be so late, but I'm still in the antechamber of generative AI. Hype always scares me (not tech). For data work, I'm not really sure it will help me to save time. Generative AI outputs seems to require too much fine-tuning. For the moment, I prefer to devote this time to python script adjustments.

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Thanks! I really enjoyed this. I'm late to AI too. I need to use it more but feel overwhelmed with all the hype and choices. It's hard to cut through the noice. Others have suggested Claude too. What's weird to me is now I'm wondering what percentage of what I'm reading throughout my day, everywhere, has been written by AI? Even personal notes from colleagues — it could be AI! Kind of disconcerting. But does it matter? Lots to process.

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