RotoBot AI的动态

Two weeks post-launch and we're already dropping game-changers. Our latest? An AI-powered Trade Analyzer that's designed to help users evaluate trades. What makes it special? For the first time, users get clear, AI-powered explanations behind trade recommendations - not just numbers. We're bringing a new level of transparency to fantasy football decisions that hasn't been seen before. The reception of the app since launch has been nothing short of spectacular, with over 2,000 users joining us in just two weeks (DM me and I'll share how we achieved this with practically no spend). As the App Store's first sports chatbot, we're going to keep pushing boundaries and revolutionizing how sports fans conduct research using AI technology. This space is about to explode, and we know we're not alone in seeing its potential. Whether you're building in sports tech, working on AI applications, or know of others in this space - let's connect. Drop a comment if you're working on something similar or know of other AI sports apps out there. The future of sports tech is being written right now, and we'd love to be part of that conversation. #SportsAI #SportsTech #FantasyFootball #Startup

For Generative AI to work effectively, Large Language Models (LLMs) need the right context in real-time. Retrieval-Augmented Generation (RAG) is a powerful architectural pattern that retrieves data from a datastore, often a vector database, and combines it with other information to provide rich context for LLMs. However, traditional RAG processes can be slow, leading to hallucinations and inaccurate responses. This is where data streaming comes in. Real-time data streaming is crucial for RAG to deliver timely and accurate context. Confluent Cloud ensures that up-to-date, domain-specific information is available instantly by providing LLMs with immediate access to fresh data, enhancing the precision and reliability of AI responses. Great video!

Retrieval-Augmented Generation (RAG) is a must have for any generative AI application. The first step in the process is creating and maintaining a Vector Database. Most companies are using batch ETL to accomplish this task. This is a mistake as Real-Time data is becoming more and more essential for many applications. This seems like a difficult task but its made very easy with Flink SQL and the Confluent Cloud. See how it can be done in just a few minutes with this GitHub Example here: https://lnkd.in/gv4BHtwN You will learn how to be able to populate and maintain a vector database by using the built in ML_Model function to vector encode your data through #confluent's fully managed Flink SQL SaaS offering. Read the link below for an excellent summary on what RAG is and why you need it. #ConfluentCloud #VectorDatabase #RAG #SemanticSearch #RetreivalAugmentedGeneration

5 Boring But Effective Actions to take Before Building an AI Agent 1 - Align stakeholders on the business value of your use case 2- Clean up / expand the amount of quality data to support your use case 3 - Audit your current infrastructure costs 4 - Estimate the costs associated with deploying your use case in Production 5 - Validate that your model(s) can generate a quality output Otherwise get ready for lots of fun surprises :)

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Reviewing M&A contracts—employment agreements, vendor deals, customer contracts—can be a time-consuming slog. With DealRoom’s AI Analysis tool, you can speed up the process and stay focused on the bigger picture. In this quick demo, we walk through how DealRoom AI simplifies contract analysis with automatic summaries that extract key information. Click the link to start your free trial and see how easy it is to handle even the most document-heavy deals:?https://hubs.ly/Q030dZ3f0

Demo! How to boost PaLM 2 accuracy with Snorkel Flow In this example, see how a bank can build a chatbot intent classification model that delivers high-accuracy results across a long tail of customer intents. ?? https://lnkd.in/gS6jc2u4