Asier Pereda Jayo’s Post

Is Writing Prompts for LLMs a Waste of Time? 🤔💡 The answer is both simple and complex: ✅ Prompts can guide LLMs to deliver more accurate results. But not all prompts are created equal, and relying on inefficient ones wastes time and resources. ⏳ 🔍 What’s the solution? A more strategic approach is needed to optimize AI training and enhance performance. At Summa Linguae, we’ve developed a 🔧 5-step LLM prompt tune-up process that’s designed to: 📊 Make AI training more data-driven and targeted to real-world applications 🌍. 🛠️ Address key challenges like prompt ambiguity and scalability, ensuring stronger and more precise models. 🤖💪 🎯 Maximize efficiency in training LLMs so that every prompt counts. 📘 Want the full breakdown of our 5-step process? Head over to the blog for all the details! Link: https://lnkd.in/grZNknpK #AI #LLMs #ArtificialIntelligence #SummaLinguae #AITraining #DataScience #PromptOptimization

Translation was considered a craft decades ago. The new form of Translation for Artificial Intelligence is creating #AI training #data, which requires new skills, like data management, and an understanding of tools and purpose. https://bit.ly/41UW0r3

Translation was considered a craft decades ago. The new form of Translation for Artificial Intelligence is creating #AI training #data, which requires new skills, like data management, and an understanding of tools and purpose. https://bit.ly/41UW0Hz

Translation was considered a craft decades ago. The new form of Translation for Artificial Intelligence is crearing #AI training #data, which requires new skills, like data management, and an understanding of tools and purpose. https://bit.ly/4iOLYOt

🚀 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  

#AI #Dall-e is still on the level of guessing what user wanted and cannot produce proper output. The level of imagination is really reduced to what it was put into it with training #data. Let see how it would work in next years, but for now AI is more a source of quick informations that still needs to be taken with some distance. #AI #artificialinteligence #CGI #training #progress #development

Excited to share my insights on developing and implementing RAG pipelines with various LLMs in the agentic AI field! 💡 Prompting is crucial as LLMs generate responses based on it. Here are key steps for writing effective prompts for any LLM: - Specify the role or perspective. - Define your goal clearly. - Provide necessary context. - Format the output/tone. - Include examples of one/few-shot learning. Additionally, some personal hacks for prompt writing: A. Always use quotes to emphasize important points (facts, statistics, or exceptions). B. Divide prompts into multiple parts with clear instructions. For a detailed breakdown of these points, check out the link below: 🔗 https://lnkd.in/dG2zYcgY #AI #PromptWriting #LLM #ArtificialIntelligence #Technology #Innovation

Discover the step-by-step process of machine learning (ML) development, from data collection and preprocessing to model training, evaluation, and deployment, ensuring robust and effective AI solutions. 𝐊𝐧𝐨𝐰 𝐌𝐨𝐫𝐞 👉 https://lnkd.in/gsjp6-mQ #MachineLearning #MLDevelopment #ModelTraining #ModelEvaluation #AI #ArtificialIntelligence #AIAlgorithms #MLDeployment

Unlock the Power of AI with Prompt Engineering Techniques! 🔑🤖 Mastering prompt engineering means learning to craft precise inputs to get accurate AI outputs. Here are key techniques to excel: Clear & Concise Prompts: Direct, simple, and to the point. Contextual Prompts: Provide enough context for the AI to understand the task. Iterative Refinement: Fine-tune prompts for optimal results. Use of Examples: Guide the AI with specific examples for better output. Prompt Templates: Streamline responses with reusable templates. Start mastering these techniques to leverage AI's full potential! 📞 +1-929-672-1814 | 🌐 www.genai-training.com | ✉️ info@genai-training.com #PromptEngineering #AI #GenerativeAI #AIFuture #LearnAI

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