Superlinked

Congrats to the winner of the raffle 🙌🏻 Sometimes all you need is showing up 💪🏻 If you missed Mór’s excellent session on building high-performing #RAG for semi-structured data - you can now watch the recording 👉🏻 https://lnkd.in/eFYfSacB Follow us for more exciting events and news 😎 #GENAI #LLM #DataScience

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RAG is The Future of AI. Not LLMs 💡🤔. After a year of deep diving into RAG systems, Pierre-Carl Langlais came to a surprising realization: While LLMs are undeniably powerful, they may actually be the most replaceable part of future AI systems 🤖. Here’s why: ▪️ RAG systems are more adaptable and easier to tailor for specialized use cases ▪️ RAG fills the gaps in updatability and explainability inherent in LLMs ▪️ RAG can handle bad data and legacy formats where LLMs often stumble ▪️ RAG can integrate various techniques and tools, not just the latest LLMs Pierre believes that the key to unlock AI's potential lies in the RAG system as a whole – not in any single LLM. If you're looking for a forward-looking perspective on the future of AI, this one is a must read. 👇👇

"Taking GenAI apps from an internal POC to Production is still very hard!" 🧗 Great discussion yesterday at Redis Released event in London, featuring Benjamin Renaud, Redis CTO, Santosh Takoor, Head of Digital & Application Innovation, Microsoft, and Superlinked's co-founder Ben Gutkovich 🏓 Did you miss it? Here are some of the key takeaways 👇 💡Handling complex queries that include structured and unstructured aspects of the data is out of reach for 99% of companies, e.g., "Best rated and budget-friendly hotels in London near Picadilly Circus" 💡Enterprises are thinking much more about the ROI of #GenAI applications, and it depends on internal capabilities and value unlock 💡#RAG is a great starting point for many clients, whether for internal knowledge consumption or for customer support, but GenAI models and #VectorSearch enable a variety of use-cases that could transform business results, such as #SemanticSearch, real-time #RecSys, and more Follow Superlinked to see the recording once it is available 📹

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Opportunity to see our co-founder in action! 💪

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Vector Indexing with ANN: A Practical Walkthrough ⚡🔎. Traditional search methods often fall short when handling large-scale data, relying on simple retrieval techniques 🐢. Vector indexing provides a powerful solutions to: ⚡️ Speed up similarity searches with encoded data patterns. 🌟 Handle large datasets efficiently through dedicated indexing strategies. 🔍 Improve search accuracy and relevance within large and massive datasets. In this VectorHub tutorial, Haziqa Sajid takes us through implementing vector indexing with ANN from scratch. Here’s a look at the key steps 🏗️: 1️⃣ Importing necessary libraries 2️⃣ Generating random data vectors to represent data 3️⃣ Implementing a distance calculation function 4️⃣ Performing flat indexing to find the top matching vectors 5️⃣ Increasing the data size and exploring IVF with k-means clustering 6️⃣ Querying a test vector using the IVF approach So next time you’re tackling massive datasets, skip the brute-force approach– Instead, Leverage vector indexes to supercharge your search capabilities! Link to walkthrough here 👉 https://lnkd.in/g5FwwiiJ A big thank you to Haziqa for this insightful guide! 🙌

Our employees are making waves in the #DataScience world! 🌊💪 Get the latest edition of the "Handbook of Data Science and AI" with participation from our Chief Architect here👉 https://lnkd.in/dk9akqJx