Mirek Ratman’s Post

If you're working with AI models like ChatGPT, prompt crafting isn't "set and forget." Unlike code, prompts need constant re-evaluation as models evolve. At Summa Linguae, we’re blending automated evaluations with expert human insights to keep prompts effective and reliable. 🔹 LLMs change with new data and updates, so prompt management must adapt too. We’re constantly fine-tuning and running tests to ensure quality. 🔹 Human Expertise Counts: Automation aids evaluation, but skilled professionals are essential for optimal results. It's about engineering and managing prompts for sustainable, trustworthy AI outputs. Read more on why prompt writing is critical for AI success. #AI #LLM #PromptEngineering #SummaLinguaeTechnologies

Exciting AI ! 🤖 Are you interested in the cutting edge of AI? Look no further than LLMs! These powerful AI models, freely available to all, can understand and generate human-like text with amazing accuracy. 🧠What exactly are LLMs? Imagine a super-powered learning tool that has been trained on a massive amount of text data. This allows them to be customized for almost any situation! 💡How do you use them? It's all about getting specific! Fine-tuning adjusts the LLM to your exact area of interest, while RAG technology combines searching and generating text for super-accurate answers. Excited about the potential of AI? Let's connect and discover how LLMs can elevate your projects! 🚀 #AI #LLMs #Innovation #llama2 #ollama #FineTuning #RAG

🚀 Discover the Potential of Few-Shot Learning in Prompt Engineering. A few examples are all it takes for AI models to learn and adapt, did you know that? 🤯 Few-Shot Learning is useful in this situation! 🔍 Few-shot learning makes it possible for AI systems to do tasks with very little data—typically just a few examples—like ChatGPT, Gemini, and LLaMA. By employing prompt engineering, you may lead these models to provide accurate outcomes, even in novel or unexpected settings. By providing appropriate context and a few well-selected examples, you can make the most out of your AI. Ready to make AI work smarter, not harder? 📈 Want to become an expert in few-shot learning and prompt engineering? 👉Register now at https://lnkd.in/erTXQEVW to advance your AI knowledge! #AI #FewShotLearning #PromptEngineering #MachineLearning #TechInnovation #GenAITraining  

Let's discuss the art of crafting powerful prompts guys.. Join me if you can #PromptEngineering #GenAI #WebinarAlert

⁉️ When using AI The question IS the answer The quality of the output is directionally proportional to the input The question or input aka: the prompt you feed into any tool is not only the starting point, but super critical With anything that's done for the first time, it needs refinement and optimization...same applies to AI and to the prompts you use Prompt engineering is a SKILL in itself that absolutely must be mastered #artificialintelligence #aiinbusiness #aiinmarketing #promptengineering #aiprompts

Hey there, tech enthusiasts and AI explorers! 👋 Let’s chat about a skill that’s becoming increasingly crucial in our AI-driven world: prompt engineering. 🤖💡 So, what exactly is prompt engineering? Simply put, it’s the art of crafting the perfect instructions for AI to give you the results you need. Think of it as learning to speak ‘AI’ fluently! Why is this so important? As AI tools become more integrated into our daily work, knowing how to communicate effectively with them can dramatically improve our productivity and the quality of AI-generated outputs. The beauty of prompt engineering is its wide applicability - whether you’re in finance, healthcare, marketing, or any other field, these skills can enhance how you leverage AI tools. Want to dive deeper? Check out this excellent article on prompt engineering best practices: https://bit.ly/4cpIhuy. #PromptEngineering #AI #ArtificialIntelligence #TechInnovation #FutureOfWork

𝗥𝗲𝗶𝗻𝗳𝗼𝗿𝗰𝗲𝗺𝗲𝗻𝘁 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗳𝗿𝗼𝗺 𝗛𝘂𝗺𝗮𝗻 𝗙𝗲𝗲𝗱𝗯𝗮𝗰𝗸 (𝗥𝗟𝗛𝗙) RLHF is a method that integrates human feedback into the training process of AI models. AI models learn from this feedback to adjust their behavior to better align with human expectations. RLHF systems typically involve four key stages: 𝟭. 𝗣𝗿𝗲-𝘁𝗿𝗮𝗶𝗻𝗶𝗻𝗴: The model (such as LLMs) is pre-trained on large datasets to understand general behavior patterns. 𝟮. 𝗦𝘂𝗽𝗲𝗿𝘃𝗶𝘀𝗲𝗱 𝗙𝗶𝗻𝗲-𝗧𝘂𝗻𝗶𝗻𝗴 (𝗦𝗙𝗧): The model is then fine-tuned based on supervised feedback from experts. 𝟯. 𝗥𝗲𝘄𝗮𝗿𝗱 𝗠𝗼𝗱𝗲𝗹𝗶𝗻𝗴: The reward model converts human feedback into digital signals, helping the system learn how to respond better. 𝟰. 𝗣𝗼𝗹𝗶𝗰𝘆 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻: Finally, the policy is optimized using the PPO algorithm, ensuring stability in the learning process. 𝗢𝗽𝗲𝗻𝗔𝗜'𝘀 𝗜𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝗚𝗣𝗧 is an example where RLHF was used to enhance the responsiveness of GPT-3. Instead of solely relying on human-defined rules, this model learned to better interpret natural user feedback. After training with human feedback, a smaller GPT-3 model (1.3 billion parameters) was able to generate responses that users rated higher compared to the original larger version (175 billion parameters). #RLHF #MachineLearning #AI

"While AI tools 🤖 are evolving and increasingly appearing on the market, it's important to realize that they can only be helpers, but cannot replace humans (at least at this stage). Especially when it comes to creating websites 🌐 and writing texts 📝. AI tools cannot replace the fragmented and complex nature of a true expert's mindset. Who knows, there may come a time when that will happen too, but at this point, there is a huge hype that overestimates the capabilities of the various models. Our team is testing the capabilities of various LLMs, long before a user interface is added to them that also allows the general public to have "chats" with the AI. And we are convinced that they are still at a very early stage. #AI #LLM #digitalmarketing #humankreativity #artificialintelligence #digitaltransformation"

Will many of us humans lose touch with some of the skills we've learnt over the past few decades after having adopted artificial intelligence? Is AI making us dumb? For answers, read this week's newsletter "AI For Real". Just shipped. #artificialintelligencetechnology #humanresources #skillsdevelopment

Are your AI prompts only scratching the surface? 'Generated knowledge prompting' brings depth to AI responses. One challenge I often faced was getting AI to give responses with depth. Too many prompts felt like I was only getting the basics. But when I tried 'Generated knowledge prompting', I realised that building a layer of background knowledge made all the difference. It’s like building a picture from an abstract sketch to full detail. The sketch gives an outline, but only with the details does the full richness come alive. Here’s what I found: → Start with knowledge generation First, I prompt the AI to list key facts or insights on a topic before moving to the main task. This step is like gathering all the right materials before building something big—it makes the response fuller and more thoughtful. → Then, knowledge integration With the basic details in place, I then ask for the main answer. The responses become richer and far more insightful. Whether it's drafting content, solving problems, or researching, this two-step approach has transformed the quality of outputs I get. 'Generated knowledge prompting' doesn’t just make the AI answer better, it helps the AI actually understand. Have you tried this approach? Share your experience or thoughts below! #AIforEveryone #PromptTechniques #InsightfulAI #promptengineering #AI #LLM