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Have you been slow to adopt #AI? Here’s a great video to walk you through som initial steps!

There you have it! New Video Alert: Understanding #GenerativeAI Context Windows I'm excited to share our latest video, "Understanding Context Windows," generated by #GenAI and orchestrated by Amanda Chaillan (Pope). 🎥 In this video, we delve into one of the most complex concepts in Generative AI: Context Windows. Here's what you'll learn: 1. Elements of the Context Window: We break down the components that make up the context window, including persona, question, chat history, ingested files, and response. 2. Model Limitations: We highlight the limitations of different models like GPT-3.5, GPT-4o, and GPT-4.0 32K, focusing on their data handling capabilities and token limits. 3. Ingesting Files: Learn how data is divided into chunks to fit within the context window and the importance of clear, specific prompts for accurate responses. 4. Writing Your First Prompt: We guide you through the process of writing your first prompt, from selecting a persona to choosing the appropriate dataset and asking your question. We also discuss the importance of understanding context window limitations when interpreting responses. This video is a must-watch for anyone looking to deepen their understanding of how large language models operate within their context windows. Don't miss out on this valuable resource! https://lnkd.in/eFYH9GFN

🚨New Video Alert 🚨 Understanding #GenerativeAI Context Windows The context window is a foundational element to successfully leveraging Generative AI. In this video, we delve into one of the most complex concepts in Generative AI: Context Windows. Here's what you'll learn: 1. Elements of the Context Window 2. Model Limitations 4. Writing Your First Prompt This video is a must-watch!

🤔 Can LLMs really help with code reviews? Spoiler alert: Yes, they can! But don’t just take my word for it. Let’s hear from the legend himself, Linus Torvalds: "LLMs can help find the obvious stupid bugs... You don't need higher intelligence to find them. But having LLMs that flag more subtle issues—where the pattern doesn’t seem right—can save time and prevent bugs from becoming bigger problems." Pretty cool, right? 💥 These aren’t just “autocorrect on steroids”—they’re like having a superpowered assistant that catches those sneaky bugs early, improves our workflows, and makes us all better at what we do! While LLMs are here to help with code reviews, not replace our human touch, their impact on the future of development is hard to ignore. 🚀 Buckle up, because tech just keeps getting more exciting! #AI #LLMs #CodeReview #TechInnovation #Automation #SoftwareDevelopment

Interested in learning more about #LLM and #AI? I was and found a great tool with GPT4ALL! This resource allows you to build your own local LLM with installers for Windows, Mac, and Ubuntu, as well as tutorials to help you get started. Plus, all of this can be done without an internet connection, ensuring your privacy. Check it out here: https://lnkd.in/gXffd-9v #gpts #artificialintelligence #machinelearning

Open sourced model is still behind the best closed models. But if you think LLMs are operating systems, then the open sourced ones like llama is like Linux systems. While they may not make the most of money, they do have the most impact in the long run.

Polydrop is Live!!! Leveraging the abuse of trusted applications, you are able to deliver a compatible script interpreter for a Windows, Mac, or Linux system as well as malicious source code in the form of the specific script interpreter of choice. Once both the malicious source code and the trusted script interpreter are safely written to the target system, one could simply execute said source code via the trusted script interpreter. MalwareSupportGroup/PolyDrop: A BYOSI (Bring-Your-Own-Script-Interpreter) Rapid Payload Deployment Toolkit (https://lnkd.in/gBEpQ-aB)

Kubernetes’ ability to deploy large-scale enterprise applications has created an opportunity for it to also become the standard for deploying AI models in the enterprise. https://lnkd.in/ebwG7C8r via SiliconANGLE & theCUBE #AI #Linux

As AI models advance, the debate between using large context windows and RAG for Generative AI continues to heat up. While large context windows offer benefits, RAG+Pinecone proves to be a more efficient and effective solution for enterprise use cases. Key advantages of RAG: ⚖️Scalability: RAG retrieves only the most relevant information, reducing computational costs and latency compared to processing massive context windows. ⚡Performance: RAG often outperforms large context windows in real-world enterprise scenarios, delivering better quality results across diverse data sources. 🦺Reliability: RAG ensures data accuracy and relevance by acting as a reliable source of truth, crucial for building trustworthy AI solutions. 🕵️♂️Agentic Capabilities: RAG enables AI agents to pull insights from multiple sources, providing comprehensive and personalized responses. Recent research further supports RAG's effectiveness. A study testing RAG with up to 1 billion documents found (study in comments): -RAG significantly improves the performance of LLMs, even on questions within their training domain -The more data available for retrieval, the more factually correct the results, with a 13% improvement in faithfulness for GPT-4 with RAG compared to GPT-4 alone -RAG enables various LLMs, including open-source models, to achieve state-of-the-art performance when provided with sufficient data. While large context windows have their merits, RAG is a superior strategy for the enterprise. #rag #vectordatabase #pinecone #contextwindow #tokens https://lnkd.in/gEJtt7e2

Just finished the course “Copilot+ PC: On-Device AI with a New Class of Computers” by Nick Brazzi! Check it out: https://lnkd.in/gprWNVmr #microsoftcopilot #windows #generativeaitools.