Decoding Data Science’s Post

AI Assistants Level Up: 𝐑𝐀𝐆 𝐯𝐬 𝐅𝐢𝐧𝐞-𝐓𝐮𝐧𝐢𝐧𝐠- What's Your Superpower? In the ever-evolving world of artificial intelligence, two game-changing techniques are empowering language models to deliver unprecedented levels of accuracy and domain expertise. Meet Retrieval-Augmented Generation (RAG) and fine-tuning - the dynamic duo revolutionizing how AI assistants access and leverage knowledge. RAG is like giving your AI a genius-level research assistant. With the ability to consult specialized databases and information repositories on the fly, RAG equips language models with laser-focused, fact-fueled responses tailored to any niche or rapidly evolving domain. From unraveling financial nuances to navigating medical complexities, RAG ensures your AI always has its finger on the pulse of the latest relevant knowledge. But what if your organization's needs are so unique that even RAG's extensive knowledge reserves fall short? Enter 𝐟𝐢𝐧𝐞-𝐭𝐮𝐧𝐢𝐧𝐠 This technique takes a general AI model and trains it intensively on your specific data and use cases, essentially turning it into an industry expert. By immersing the language model in a massive relevant dataset, fine-tuning molds it into a tailor-made solution, delivering pinpoint accuracy and unparalleled subject-matter mastery. So, which superpower is right for you? If you need an AI assistant that can deftly navigate ever-changing, knowledge-intensive landscapes, RAG is your go-to hero. But if your requirements are highly specialized or proprietary, fine-tuning offers the ultimate customized solution. In today's data-driven world, the choice between RAG and fine-tuning could be the difference between an AI assistant that merely meets expectations and one that shatters them. Unlock your language model's true potential and experience a whole new level of intelligent, contextually relevant, and precisely tailored responses. Let us know your thoughts in the comments. Decoding Data Science Infographics Credit: Rajdeep Saha #ai #tech #data

Let's explore data analytics with Snowflake Cortex AI and Cortex Search! 1. Cortex AI: Bring AI to your fingertips with natural language query generation, model fine-tuning, and safe LLM usage. 2. Cortex Search: Quickly search structured and unstructured data with lightning-fast, accurate hybrid search capabilities. The integration of Cortex Search with Cortex Analyst is particularly powerful, improving SQL accuracy and simplifying complex queries. This combination enables businesses to unlock the full potential of their data, making it more accessible and actionable than ever before. Whether you're in finance, sales, R&D, or any data-driven field, these tools can significantly boost productivity and insights. The best part? They're designed with ease of use in mind, allowing even those with minimal technical expertise to leverage advanced AI capabilities. The future of data analytics is smarter and faster. How will you leverage these tools? #SnowflakeCortex #AI #DataAnalytics #BusinessIntelligence #CortexAI #CortexSearch

Our Latent Space Readout (LSR) technology is incorporated into Log10’s AutoFeedback product to deliver LLM accuracy comparable to fine-tuned evaluation models trained on 20x more data. LSR surpasses LLM-as-a-judge and provides faster time-to-value. Read our technical report on measuring LLM accuracy and detecting hallucinations that has been read by over 10k+ data scientists and AI engineers already! https://bit.ly/3ZLldDo #LLM #AI #DataScience

🔍 Dive deeper into the world of AI with enhanced insights on crafting the perfect prompts! Building on Michael Phillips's "Creating Templates For AI Prompts", I found some invaluable resources and tips to level up your prompt game: 1️⃣ For practical, detailed guidelines, check out OpenAI’s guide on writing effective AI prompts [here](https://lnkd.in/eXEyNcGQ). 2️⃣ Discover the art of prompt crafting with insights from Hugging Face's blog [here](https://lnkd.in/esMpfQ5b). 3️⃣ Master prompt engineering with tactical examples from Towards Data Science [here](https://lnkd.in/eatpd4W4). 4️⃣ MIT Technology Review sheds light on designing impactful prompts for language models [here](https://lnkd.in/ey6cCCTN). 👉 Elevate your AI interactions by including real-world examples, exploring prompt types, or adding visual aids to clarify your strategies. Always remember to weave in feedback mechanisms to refine and perfect your approach. Stay ahead by addressing these elements and more. The potential is immense, and the right prompts can unlock it! #AI #Technology #Innovation #PromptCrafting

Do you think LLMs are just about text generation? Think again. 👉 Function Calling 👈 So, what is function calling in LLMs? In the world of AI, function calling in large language models (LLMs) is the ability of the AI to run custom code! Function calling allows models to do more than just generate text, they can now trigger external functions to solve complex tasks or fetch real-time data. So, how does it work? 🔹Input Detection: The model identifies when it needs external data or a specific task to be completed. 🔹Function Invocation: It calls the right function (e.g., weather API, calculation tool). 🔹Execution: The function runs externally. 🔹Result: The model uses the function’s output to generate a complete response. If you ask, “What’s the current weather in New York?”, the LLM will call a weather API to fetch the latest data and respond with something like, “It’s 20°C and partly cloudy in New York.” This capability makes LLMs more practical, dynamic, and context-aware, empowering us to solve real-world problems more effectively. #AI #LLM #FunctionCalling #Innovation

🌟 Unlocking Efficiency in Large Language Models with Grouped Query Attention (GQA) 🌟 In the rapidly evolving landscape of AI, understanding and processing large amounts of text efficiently is crucial. That's where Grouped Query Attention (GQA) comes into play, revolutionizing how LLMs interpret and manage information. GQA is an innovative method that strikes a perfect balance between multi-query attention (MQA) and multi-head attention (MHA). By bundling similar pieces of information together, GQA allows models to focus on groups of words rather than individual ones, resulting in enhanced speed and smarter processing. Key Features of GQA: 1. Interpolation: GQA serves as a middle ground, addressing MQA's drawbacks—like quality degradation and training instability—while retaining the robust quality associated with MHA. 2. Efficiency: This method optimizes performance without sacrificing quality by utilizing an intermediate number of key-value heads, making it a powerful choice for resource management. 3. Trade-off: GQA successfully balances the swift performance of MQA with the rich quality of MHA, delivering a favorable compromise that enhances overall functionality. Understanding Queries Better GQA excels at modeling hierarchical relationships within queries. By grouping query terms and applying varied attention to different groups, it improves the understanding of complex queries and boosts the performance of information retrieval systems. This capability is invaluable for applications such as search engines, question-answering systems, and document summarization, where accurately grasping user intent leads to more relevant results. Performance Gains Additionally, GQA enhances the efficiency of LLMs by significantly reducing memory bandwidth required during decoder inference, all while maintaining the quality of output. This optimization is vital in making LLMs faster and more capable, allowing businesses and developers to leverage AI more effectively. As GQA continues to be integrated into various machine learning models, it represents a significant advancement in our ability to process and understand text, paving the way for more intelligent and responsive AI systems. #AI #MachineLearning #NaturalLanguageProcessing #TechInnovation #DataScience #LLMs #InformationRetrieval #GroupedQueryAttention #GQA #SearchEngines #QuestionAnswering #DocumentSummarization More details... 🌐 www.hattussa.com 📧 contact@hattussa.com ☎️ +91 9940710411

Data is fuel for AI. An organization’s data is essential for harnessing AI's potential across numerous use cases. We are all familiar with conversational AI using large language models (LLMs) to generate natural language responses. Now, imagine doing the same with your relational and unstructured data within your existing systems without moving it. Your organization’s data is precious, likely sensitive, and should not be shared indiscriminately. Therefore, keep it where it is, maintain your existing security and governance posture, and still reap the benefits of conversational AI. https://lnkd.in/e876D6WJ

🚀 Enhancing AI Responses with Retrieval-Augmented Generation (RAG) 🚀 Ever wondered how AI can stay up-to-date with the latest information? 🤖💡 This image brilliantly illustrates the difference between using pre-trained Language Models (LLMs) with and without Retrieval-Augmented Generation (RAG). 🔍 Scenario: You ask an AI which country won the Women's World Cup 2023. Without RAG: 🛑 The AI, based on its last update in January 2022, can’t provide the current answer. With RAG: ✅ The AI accesses an external database, retrieves the latest information, and accurately responds: "Spain won the Women's World Cup 2023." 🔧 How it works: Without RAG: The AI relies solely on its pre-trained knowledge, leading to outdated responses. With RAG: The AI queries an external, up-to-date database, integrating new, domain-specific information into its responses. 🌟 Benefits of RAG: Up-to-date Information: Ensures responses reflect the latest data. Domain-Specific Knowledge: Accesses specialized databases for more accurate answers. Enhanced Context: Combines the strengths of pre-trained LLMs with real-time data retrieval. 💡 Conclusion: RAG transforms AI from static knowledge holders to dynamic information seekers, greatly enhancing their utility and accuracy. #AI #MachineLearning #RAG #Innovation #Technology #DataScience #ArtificialIntelligence

Join Benjamin A. Corwin, Jason Bishop, and me on October 29, 2024, 3:00 pm – 4:00 pm CT for a wonderful webinar on "Supercharge Your AI Capabilities." Learn how your organization can quickly and seamlessly implement practical and useful AI with Qlik's revolutionary product, "Qlik Answers." https://lnkd.in/g55vhmCy Many thanks to Courtney Hastings for all your help setting up this event! #Qlik #AI #GenAI #Data #AIValue

Refining AI with the prompt before the prompt! Explore this #GenAI training video by Don Woodlock, where he explains how adding instructions, context, or relevant data to queries enhances response accuracy and relevance. Learn from real-world applications like healthcare chatbots and natural language #SQL conversions, showing how these strategies make AI systems more intelligent and dependable. 🎬 Watch here 👉 https://bit.ly/4hAFWQK