Pavan Belagatti’s Post

🔍𝐈𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐢𝐧𝐠 𝐏𝐢𝐧𝐞𝐜𝐨𝐧𝐞, 𝐚 𝐜𝐮𝐭𝐭𝐢𝐧𝐠-𝐞𝐝𝐠𝐞 𝐀𝐈 𝐝𝐚𝐭𝐚 𝐦𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭 𝐬𝐨𝐥𝐮𝐭𝐢𝐨𝐧 𝐟𝐨𝐫 𝐑𝐀𝐆 & 𝐋𝐋𝐌𝐬! 🚀 Are cumbersome data management processes holding back your large language models (LLMs) and retrieval-augmented generation (RAG) tasks? Pinecone is here to revolutionize your AI data processing! 𝐇𝐞𝐫𝐞'𝐬 𝐰𝐡𝐚𝐭 𝐬𝐞𝐭𝐬 𝐏𝐢𝐧𝐞𝐜𝐨𝐧𝐞 𝐚𝐩𝐚𝐫𝐭: 🔹𝐄𝐟𝐟𝐨𝐫𝐭𝐥𝐞𝐬𝐬 𝐕𝐞𝐜𝐭𝐨𝐫 𝐒𝐞𝐚𝐫𝐜𝐡: Simplify vector similarity search with Pinecone's intuitive API. No more tedious indexing and retrieval! 🔹𝐒𝐜𝐚𝐥𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐟𝐨𝐫 𝐆𝐫𝐨𝐰𝐭𝐡: Pinecone scales seamlessly with your data volume, ensuring your AI models remain high-performing even as datasets expand exponentially. 🔹𝐔𝐧𝐦𝐚𝐭𝐜𝐡𝐞𝐝 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞: Enjoy lightning-fast query speeds for real-time responses and a seamless user experience. ⚡️ 🔹𝐅𝐨𝐜𝐮𝐬 𝐨𝐧 𝐈𝐧𝐧𝐨𝐯𝐚𝐭𝐢𝐨𝐧: Spend more time pushing the boundaries of AI with Pinecone's user-friendly platform, and less time on data wrangling. 𝐏𝐢𝐧𝐞𝐜𝐨𝐧𝐞 𝐞𝐦𝐩𝐨𝐰𝐞𝐫𝐬 𝐲𝐨𝐮 𝐭𝐨: 🚀𝐁𝐮𝐢𝐥𝐝 𝐏𝐨𝐰𝐞𝐫𝐟𝐮𝐥 𝐑𝐀𝐆 𝐒𝐲𝐬𝐭𝐞𝐦𝐬: Craft state-of-the-art RAG models for efficient information retrieval and coherent text generation, taking automation to new heights. 🚀𝐒𝐮𝐩𝐞𝐫𝐜𝐡𝐚𝐫𝐠𝐞 𝐋𝐋𝐌 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞: Fuel your LLMs with efficient vector search, enabling them to process massive datasets with speed and accuracy. 🚀 𝐀𝐜𝐜𝐞𝐥𝐞𝐫𝐚𝐭𝐞 𝐀𝐈 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡: Dive deep into ground breaking research and innovation without worrying about data infrastructure headaches. Ready to unlock the true potential of your AI projects? Join the Pinecone revolution and experience the future of AI data management! ➡️ https://www.pinecone.io/ #Pinecone #AIData #LLMs #RAG #VectorSearch #AIRevolution

Building a production-grade Retrieval Augmented Generation (RAG) system powered by LLMs is challenging. Success hinges on practicing Systems Thinking, just as you would with any other complex software system. Here are some critical components of RAG-based systems that require careful consideration and continuous optimization to achieve the desired outcomes: **Retrieval** 1️⃣ Chunking How you segment your data for external context matters: * Small vs. large chunks. * Sliding vs. tumbling window chunking. * Retrieving parent/linked chunks vs. only the originally retrieved chunks. 2️⃣ Embedding Model Selecting the right model for embedding and querying external context in latent space. 3️⃣ Vector Database * Choosing the right database. * Deciding where to host. * Storing metadata alongside embeddings. * Optimizing the indexing strategy. 4️⃣ Vector Search * Selecting a similarity measure. * Deciding the query flow: metadata-first vs. ANN-first. * Considering hybrid search approaches. 5️⃣ Heuristics * Applying business rules to the retrieval process, such as: * Handling time-sensitive data. * Reranking retrieved results. * Ensuring diversity (avoiding duplicate context). * Source prioritization. * Conditional preprocessing of documents. ** Generation ** 6️⃣ LLM Selection * Choosing the appropriate Large Language Model: * The gap between open-source and proprietary LLMs is narrowing. * The main decision is now between proprietary options or self-hosting. 7️⃣ Prompt Engineering Simply having context doesn’t eliminate the need for robust prompt design. Efforts should focus on: * Aligning outputs with system goals. * Guard-railing against jailbreak scenarios. * Carefully crafting inputs and outputs to produce reliable, accurate results. What's Missing? What other critical components of a RAG system have we overlooked? Let’s discuss in the comments! 👇 #LLM #GenAI #LLMOps #MachineLearning #AI

Langchain vs. Llama Index These 2 powerful frameworks continue to make waves in the AI space. From my experience, 𝗟𝗮𝗻𝗴𝗖𝗵𝗮𝗶𝗻 stands out as the ultimate tool for developers. It’s the de facto framework, making embedding and retrieval a seamless process. Transforming your vector store into a retriever is as easy as a single method call. Built to support various data types like text and PDFs. Great for rapid development and deployment. On the flip side, 𝗟𝗹𝗮𝗺𝗮 𝗜𝗻𝗱𝗲𝘅 shines in enterprise settings. Its detailed metadata and advanced retrieval options align more closely with the needs of large-scale data operations. Excels in metadata management. Tracks relationships between document nodes. Customizable prompt templates for specific business use cases. At Nimble we leverage LLM frameworks in production for data acquisition and processing. Choosing the proper LLM framework can significantly influence your project's success. Summed up— 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿𝘀 𝗹𝗼𝗼𝗸𝗶𝗻𝗴 𝘁𝗼 𝗱𝗶𝘃𝗲 𝗶𝗻𝘁𝗼 𝗔𝗜 >> LangChain is your best bet. 𝗟𝗮𝗿𝗴𝗲-𝘀𝗰𝗮𝗹𝗲, 𝗰𝗼𝗺𝗽𝗹𝗲𝘅 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀 >>Llama Index is the better choice. Explore these frameworks and see how they can transform your AI workflows. In a few years, there might be a big debate about which platform will come out on top. It might not be as big as the old MAC vs. PC, but a lot of us are already trying to figure out which one to choose. wdt?

Steps to assess the Organization maturity : LLM capabilities With the LLM ased applications are coming wild, now it is the time for the organizations to have an assessment model if the Production ready models can be delivered. The Basics : 1: Identify Key Business Outcomes: Organizations should begin by clearly defining the specific business outcomes they aim to achieve through GenAI implementation and and KPIs used to measure them. These outcomes could range from improving customer service and automating processes to enhancing decision-making or developing new products and services. 2. Map Outcomes to Maturity Levels: Once the desired outcomes and KPIs are identified, organizations can map them to the corresponding levels in the maturity model. For example: Level 0: If the primary goal or capability is to collect and organize data for future GenAI initiatives, the organization is likely at Level 0. data of course is the foundational element that fuels AI; whether predictive AI or generative AI. Levels 1 & 2: If the focus is on using GenAI for basic tasks like content generation, summarizing content, question answering using the base capability and knowledge of the foundation model being served or to information retrieval, the organization might be at Levels 1 or 2. Levels 3 & 4: Organizations looking to customize GenAI models with their data or ensure the quality and relevance of outputs are likely at Levels 3 or 4. Levels 5 & 6: For complex use cases requiring multi-agent systems, advanced reasoning, or responsible AI practices, organizations might be aiming for Levels 5 or 6. 3. Assess Current Capabilities: Organizations should then assess their current capabilities regarding data infrastructure, model selection, prompt engineering, model tuning, evaluation, and infrastructure for multi-agent systems. This assessment can be done through internal audits, external consultations, or benchmarking against industry standards. 4. Identify Gaps and Opportunities: By comparing their desired outcomes with their current capabilities, organizations can identify gaps in their GenAI maturity. These gaps represent areas where investment and development are needed to reach the desired level. Additionally, they might discover opportunities to leverage existing strengths and accelerate their progress Using LLM Orchestration frameworks like Langchain, Llamindex..etc in building the agents, functions getting into COT (Chain of Thoughts) and ReAct (Reason and act) are the highest maturity levels in the LLM applications. Below is the representation of the factors based on LLM Maturity.

The Power of Fast, Scalable Batch LLM Inference with Mosaic AI! Imagine extracting insights from massive datasets, all in one go, without the hassle of infrastructure or data exports. With Batch LLM Inference, you can leverage Large Language Models (LLMs) to analyze unstructured data in bulk—fast and efficiently! 📊✨ 🔑 What’s Batch LLM Inference? Batch LLM Inference allows large-scale data processing, enabling you to run inference on thousands (or even millions) of documents simultaneously, reducing time and costs while boosting scalability. ⚡ Why Does It Matter? • Scale Effortlessly: Analyze extensive datasets in bulk. • Increase Efficiency: Streamline workflows and minimize manual processes. • Optimize Costs: Get more done without adding infrastructure costs. 💡 The Power of Mosaic AI Model Serving Run inference directly on governed data—no movement needed! Mosaic AI allows seamless integration of multiple AI models for real-time, governed insights at scale, thanks to Unity Catalog. 🛠️ SQL Interface Simplified Simply create an endpoint, write an SQL query, and execute—all from your notebook or workflow, interactively or on schedule. Gain the power of batch AI with minimal complexity. 🔍 Ready to Transform Your Data Analytics? Take your enterprise data to the next level with fast, scalable batch inference, and uncover AI-driven insights without infrastructure headaches. Don't forget to check out our website: https://lnkd.in/dhkr2nP2 #bi3technologies #bi3 #insight #impact #innovation #dataanalytics #datainsights #AI #DataAnalytics #BatchProcessing #LLM #MosaicAI #DataInsights #ScalableAI

Anthropic just open-sourced the Model Context Protocol (MCP) It solves a fundamental problem with LLM apps: Connecting AI to your actual data and systems. Think about it: Even the most advanced AI models are isolated. They can't access your databases. They can't use your internal tools. They're cut off from your systems. MCP solves this with 3 key capabilities: Resources ↳ AI can read files, databases, and documents ↳ Access happens through secure, controlled channels ↳ Your data stays on your systems Tools ↳ AI can use your internal tools and APIs ↳ Execute approved commands and operations ↳ All actions require user confirmation Prompts ↳ Create standardized templates ↳ Build consistent workflows ↳ Share context across teams Real examples of what MCP enables: AI accessing your codebase to explain complex functions AI analyzing your database to spot trends AI using your internal tools to automate tasks AI connecting to multiple data sources at once The best part? It's an open standard. You can build your own connectors. Companies can customize their integrations. Everything stays secure and controlled. This is a major step toward AI that can actually work with your systems. No more copying and pasting between tools. No more isolated AI experiences. No more building custom integrations for every data source. Watch Claude connect directly to GitHub, create a new repo, and make a PR through a simple MCP integration 👇

LLMs are shepherding in a new era of AI, no doubt about it. And while the volume and velocity of innovation is astounding, I feel that we are forgetting the importance of the quality of the data that powers this. There is definitely a lot of talk on what data is used to train the massive LLMs such as OpenAI, and there is a lot of talk on leveraging your own data through finetuning and RAG. I also see an increased attention on ops, whether it is LLMOps, MLOps or DataOps, all of which is great to keeping your system and data running. What I seeing far less attention to is managing your data, ensuring it is of high quality and that it is available when and where you need it. We all know about garbage in garbage out -- if you do not give your system good data, you will not get good results. I believe that this new era of AI means that data engineering and data infrastructure will become key. There are numerous challenges to get your system into production from a data perspective. Here are some key areas that I have seen causing challenges: 1. Data: The data used in development is often not representative of what is seen in production. This means the data cleaning and transforms may miss important aspects of production data. This in turn degrades the model performance as they were not trained and tested appropriately. Often new data sources are introduced in development that may not be available in production and they need to be identified early. 2. Pipelines: Moving our data/ETL pipelines from development to staging to production environments. Either the environment (libraries, versions, tools) have incompatibilities or the functions written in development were not tested in the other environments. This means broken pipelines or functions that need rewriting. 3. Scaling: Although your pipelines and systems worked fine in development, even with some stress testing, once you get to the production environment and do integration testing, you realize that the system is not scaling the way you expected and are not meeting the SLAs. This is true even for offline pipelines. Having the right infrastructure, platforms and teams in place to facilitate rapid innovation with seamless lifting to production is key to stay competitive. This is the one thing I see again and again being a large risk factor for many companies. What do you all think? Are there other key areas you believe are crucial to pay attention to in order to achieve efficient ways to get LLM and ML innovations into production?

Excited to share a new, free custom LLM evaluation offer, and a webinar + demo on the programmatic data development technology behind it. Why?  Because current eval solutions are not *specialized, fine-grain, or scalable* enough👇 As enterprises move from demos ('vibe check' evals) and co-pilots (user feedback/efficiency evals) to AI systems that are customer-facing and/or business critical, one of the key gaps is an approach to evaluation that is: - (1) Specialized - (2) Fine-grained - (3) Scalable More detail on each below: --> (1) Specialized: Imagine training a new college grad to become a subject matter (e.g. doctor, lawyer), but then having another random college student grade their final exam. That's what using off-the-shelf LLMs/auto-eval solutions to evaluate specialized LLM systems is like! For domain-specific and business-critical use cases, most enterprises cannot trust an off-the-shelf solution. They need their experts to evaluate it against their unique standards, objectives, and context. Evaluation needs to be specialized. --> (2) Fine-grained: For LLM systems where a user can potentially ask limitless questions / prompt for anything, the overall score is rarely what matters most. Rather, it's usually the scores on specific types of prompts (or "slices" of the dataset) that matter most. E.g. if evaluating a self-driving car, you probably care differentially about intersections & yellow lights. For a customer service chatbot: how it handles complaints is probably key. To be relevant and actionable, evaluations need to be fine-grained. --> (3) Scalable: Finally, evaluations set the clock speed of your LLM development. We worked with one large enterprise customer who was able to do one evaluation with their SMEs every four weeks (we took it down to 4hrs avg). To support AI development that moves fast enough for the market today, evaluation needs to be scalable. --> The key to all of these is how you label and develop data. With our programmatic approach Snorkel AI, enterprises can rapidly label & slice their data in a specialized and scalable way to get fine-grained evals that work. Check out the webinar & free eval offer for more! - Free eval offer: https://lnkd.in/gnZEkEST - LLM evaluation webinar + demo: https://lnkd.in/gmaSFR44

"While RAG can help reduce AI hallucinations and improve responses, it is not enough on its own. Problems like choosing the wrong LLM, or using the wrong approach to data chunking or indexing, can affect how well your RAG system works and impact the quality of its responses. For example, if you use chunks that are too big, then the LLM will return big chunks of text that might not be relevant to specific requests." "For instance, how does your RAG deployment cope with thousands or millions of requests from users simultaneously, and how fast can your vector database and LLM integration components parse that data so the system can return a response to the user?" "While users might accept slow performance from a free novelty service, they are not so willing to put up with poor response times when they are paying for a servicemore integrations or functionality over time. However, DIY also means that you are responsible for all of that integration and management, which can add up over time." " The alternative is to look at a stack-based approach, where support for different tools or integrations has been implemented for you. A pre-built stack allows your team to focus on building applications, rather than implementing infrastructure, and should also simplify your operations." https://lnkd.in/gWRvBWwM

GPT4 is already amazing. With GPT5 the risk of market dislocation is real as some companies will become better than others almost overnight. What took 30 years for industry could happen in less than 3 years for office workers. Are we ready for that?