Summa Linguae Technologies’ 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

#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

🚀 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  

#FineTuning vs #TransferLearning Fine-Tuning: 1) Adapt pre-trained model to a specific new task. 2) Train the entire model with new data. 3) Typically requires more data specific to the new task. 4) Use when task-specific data is available and computational resources allow full retraining. 5) More complex as it involves retraining the entire model. Transfer Learning: 1) Leverage knowledge from a pre-trained model to enhance performance on a related task. 2) Often freeze some layers of pre-trained model and train specific layers on the new task. 3) Can be effective with smaller datasets due to leveraging pre-trained knowledge. 4) Use when limited labeled data or computational resources are available, and tasks share similarities. 5) Less complex as it often involves freezing some layers and training only specific layers. 🔻🔺🔻🔺🔻🔺🔻🔺🔻 #ailearning #tune #machinelearning #ai #deeplearning

Hey Connections🤗... (I've to Repost this post because of some text Editing issues.) I want share with you all that Recently I had attended amazing workshop of AI tools which is conducted From GrowthSchool by such amazing mentor and Founder of GrowthSchool who's Vaibhav Sisinty ↗️ I realized that I utilized my time at right place in case of learning about AI tools ,it's having powerful and time savings uses. You can imagine what I've got to learn by going through the following image. If you guys want to attend same workshop for free then Here’s their platform on which you can attend this workshop of AI tools for free of cost :- https://lnkd.in/gdfWp9Su #ai #aitools2024 #newera #artificialintelligence #prompting #promptEngineering #reposted #reposting #gained #Upgrade ##aitools

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

I'm quite a fan of ai to help me research topics and find sources to support my evidence based practice. But today something went wrong...... I asked ai to "provide me with some studies that show tangible benefits from "time to think" methodology in smes in engineering or manufacturing. provide me with three bullet points with data points and sources referenced" What came back looked amazing at first glance, in fact almost too good. As always I like to check the articles and have a read to make sure the quality of the info is good. And I found that none of the articles actually existed! The ai tool had made them up! An interesting learning point and shows the need to not yet trust ai completely. I have now trained it never to make up articles but nonetheless, I'll always be checking its sources!! #ai #learning #evidence

The cost of training AI is on a sharp upward trajectory as the big players try to achieve artificial general intelligence (AGI). I wonder if it would be more beneficial to allocate resources towards building models that directly enhance human productivity right now? For example, how easy is it to create an infographic quickly through verbal commands compared to manual creation? I cannot do this, if you can explain what AI tools you used? Additionally, the accuracy and impartiality of training data pose significant concerns. Will OpenAI's utilization of Time's content accurately represent facts or merely reflect the authors' viewpoints? Such an interesting time we are all living in! This is probably a big reason why the candid photos of Sam Altman always convey a lot of concern and hyper thinking. #AI #AGI #OpenAI #ArtificialIntelligence

𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐒𝐮𝐦𝐦𝐚𝐫𝐲: 𝐏𝐫𝐨𝐦𝐩𝐭 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝐓𝐞𝐜𝐡𝐧𝐢𝐪𝐮𝐞𝐬 Have looked into this a bit lately, and there are a lot of techniques that can improve how language models perform for different tasks. There are far more advanced techniques but I just thought to share some of the ones I’ve learnt about recently: you might be using these already! Role-based Prompting: Tailoring your prompts based on the role or task you want the AI to perform. Prompt Chaining: Build a sequence of prompts that guide AI through a series of logical steps, refining its understanding at each stage. Chain of Thought: Encouraging AI to ‘think aloud’, breaking down its reasoning into steps we can follow. Zero-shot Learning: You ask AI to perform a task without prior examples, relying on its built-in knowledge. Few-shot Learning: You provide a handful of examples (around 2-5) within your prompt to steer AI in the right direction. This can be combined with the first 3 techniques to further increase the accuracy of results. #generativeai #promptengineering #learning

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