Dr. Michael Fröhlich’s Post

When you ask an AI assistant for something simple… You often get: - Extra stuff you didn’t ask for - Overcomplicated outputs - Or even a “Pro plan” surprise This is the gap between human intent and AI interpretation. The challenge isn’t the AI’s capability; it’s alignment and clarity. The best engineers don’t just prompt. They debug, refine, and iterate until the AI delivers exactly what’s needed. How do you make sure your AI outputs are what you actually want? #AIDevelopment #AIIteration #TechTips #MachineLearning #AI #ArtificialIntelligence #Innovation

Do you have AI integrated into your company’s product or internal tooling? If not, you are missing out. If you want to stay ahead of the competition, AI is a must. You can use it to reach unprecedented levels of speed and efficiency, be it through AI-generated insights based on real data, or workflow automation through AI agents. This goes way beyond simple automation to create intelligent systems that learn, adapt, and aid in data-driven decision-making. If it all sounds interesting, we can handle the AI integration for you. We leverage state-of-the-art models like GPT-5 or Claude 4 to set our clients up for success. #ArtificialIntelligence #AI #analytics #ai_agent #claude #gpt

🤖 AI isn't the future anymore — it's the present. From smarter workflows to data-driven insights, Artificial Intelligence is rapidly becoming the backbone of competitive advantage. This post from Canduit captures how integrating AI into products and internal tools can unlock efficiency, agility, and innovation. The question isn't “Should we adopt AI?” — it's “How fast can we scale with it?” 🚀 #ArtificialIntelligence #AI #analytics #ai_agent #Automation #Innovation #DataDriven #AIagents #GPT #Claude #BusinessGrowth

The rapid adoption of AI tools is creating a fragmentation challenge for businesses, as individual teams pick different models, datasets and AI architectures to work with.  In turn, this introduces risks due to inconsistent a quality control, oversight and accuracy levels in AI responses.   Learn how AI Knowledge Bases can addresses these challenges, by providing a shared memory for agents & AI chat: https://ow.ly/8sNu50WUryq #AI #TungstenAutomation #Blog #AIAdoption #BusinessIntelligence #AIEthics #DataManagement #MachineLearning

Explore these amazing books on #AI Systems, #LLMs, #GenAI, and AI Agents, by Valentina Alto from Packt Publishing: (1) AI Agents in Practice — Design, Implement, and Scale Autonomous #AI Systems for Production [JUST PUBLISHED]: https://amzn.to/4p98LYl (2) Practical Generative AI with ChatGPT: https://amzn.to/4oXSXHI (3) Building LLM-Powered Applications: https://amzn.to/4iNimjQ (4) Modern Gen AI with ChatGPT and OpenAI Models: https://amzn.to/4cmEm2U

Hot AI take #1: The next great move in AI will not be an improvement in the power of the LLM itself. There is already evidence that “bigger is better” is meeting its limitations — smaller models are being trained which are matching the capabilities of big models. Properly created ML anomaly detection pipelines are a great example of that. The next great move is properly wrapping the LLM in ways that treat it like a child. ReAct and similar wrappers which give an LLM a limited set of options are going to provide a great tool set in the near future. If you feel the urge to think of LLMs as thinking, then view them like the 6 year old you left unattended in a kitchen and being surprised they can’t cook. #AI #MachineLearning #Innovation

The rapid adoption of AI tools is creating a fragmentation challenge for businesses, as individual teams pick different models, datasets and AI architectures to work with.  In turn, this introduces risks due to inconsistent a quality control, oversight and accuracy levels in AI responses.   Learn how AI Knowledge Bases can addresses these challenges, by providing a shared memory for agents & AI chat: https://ow.ly/9nhR30sPvYM #AI #TungstenAutomation #Blog #AIAdoption #BusinessIntelligence #AIEthics #DataManagement #MachineLearning

Interesting points about enterprise & AI by The AI Exchange (great newsletter, link in comments). "We’ve noticed a few of the same repeating patterns stopping teams in their tracks, regardless of company size and industry: >Random acts of AI >Shiny tool syndrome >No clear ownership with AI adoption >The “we must get our processes perfect before using AI” trap Notice how none of these are tech or tool problems? They’re operational problems. The exciting thing about operational problems is that having the right people and the right processes usually solves them." #AI #enterprise #business

3 checks for building Human-First AI: 1️⃣ Diverse data → Bias starts at input 2️⃣ Edge-case testing → Who gets excluded when systems fail? 3️⃣ Continuous audits → AI needs monitoring, not one-time fixes Safer AI → Trusted AI. #HumanFirstAI #AITrust #AIEthics #InclusionInTech

I found a cheat code for working with AI. Everyone talks about using automation and AI for efficiency, but I've learned that completely relying on it can sometimes backfire. I used to let the AI do the first pass on a document and I'd spend more time double-checking generic feedback than I would have by just doing the work myself. My process is different now. I do a thorough manual check first. Then, I give the AI my initial findings as a brief. This teaches the AI what to look for and helps it deliver specific, non-generic feedback. It's a complete game-changer. My time is used more efficiently, and the final result is far more accurate. The biggest lesson? A machine can't replace a careful eye, but it can absolutely supercharge it. #DataAccuracy #AI #Automation #DataIntegrity #Research