Seaflux’s Post

Latest additions to our glossary, helping you navigate key concepts in AI and enhance your understanding of Retrieval-Augmented Generation (RAG): 📕 🔹 Semantic Search: This search type goes beyond keywords to grasp the context and intent behind a search query, offering more relevant and accurate results. It's about understanding meaning, not just words. https://lnkd.in/eTKun-ez 🔹 Vector Embeddings: These are numerical representations of words or data points mapped in a high-dimensional space, enabling semantic comparisons and analyses. They are fundamental in capturing the nuances of language. https://lnkd.in/eaD-REky 🔹 RAG (Retrieval-Augmented Generation): A technique that boosts the accuracy and reliability of generative AI models by sourcing facts from external databases. Think of it as equipping your AI with a research assistant, ensuring responses are factual and up-to-date. https://lnkd.in/ecQi9ZXZ Delve into these concepts and see how they can transform your approach to #AIprojects.

✴ Choosing the Right Chunk Size for Efficient Document Querying ✴ When I work with large language models like GPT for question-answering tasks, finding the right chunk size is essential. I started with smaller chunks (100-200 words) for quicker responses but quickly realized they often led to fragmented answers, especially when querying complex legal documents. To improve accuracy, I increased the chunk size to 400-500 words, which provided richer, more coherent insights. However, this also slowed processing times for intricate queries. I then experimented with intermediate sizes of 250-350 words, which offered a better balance between speed and context. By monitoring performance metrics I learned the importance of tailoring chunk size to the task. Smaller chunks work well for straightforward queries, while larger ones are better for complex tasks. This iterative process helped me optimize chunk sizes, maximizing the AI model’s potential while balancing speed and precision. #GenAi #AI #Datascience

Workshop Virtual Databricks - Governing Your Data Estate in the Age of AI - 19 de Novembro às 14h BRT In this session, you will learn how to:   - Create an enterprise catalog for structured/unstructured data, ML models, and features, AI tools - Utilize context-aware natural language search and discovery - Implement access permissions and audit data access - Access Unity Catalog managed data and AI assets from any compute engine via open APIs - Use AI to monitor data and ML pipeline quality - Share data effortlessly across clouds, regions, or platforms with Delta Sharing https://lnkd.in/dufR7_WS #dataintelligence #dataanalytics #datagovernance #genai #ai #llm

Naive Retrieval-Augmented Generation (RAG) for Enhanced Knowledge Systems In today's AI landscape, connecting large language models (LLMs) to external databases using retrieval-augmented generation (RAG) is a game-changer. This simple yet powerful framework enables seamless integration of external data, ensuring accurate, context-driven responses. How it works: Query Input Embedding model creates vector representations. Vector Store is indexed from a database. Context Retrieval feeds relevant data into the LLM. LLM generates a response based on both query and context. Perfect for scaling enterprise-level AI solutions with external data. #AI #MachineLearning #LLM #RAG #Data

Hey hey, Let's Talk About Semantic Routers in Your RAG Pipeline! Ever wonder how your AI decides what to do with a user prompt? The secret lies in the semantic router. 🔍 What's a Semantic Router? A semantic router is like a smart decision-making layer for your LLMs (Large Language Models) and agents. Here's what it does: ✅ Decides When to Query the Vector DB: If the user's prompt needs specific information, it fetches data from the vector database. ✅ Handles Chit Chat: If it's just small talk, it knows not to query the vector database. ✅ Filters Out Unwanted Topics: Keeps your AI from answering questions you don't want it to. This makes your AI smarter, more efficient, and better at giving the right responses. 💡 Want to know more about implementing semantic routers in your RAG pipeline? DM me if you want to learn more! #AI #MachineLearning #SemanticRouter #RAG #TechTips #AIChatbots #DataScience #AIInnovation

Thrilled to share my first video on LinkedIn, diving into insights about LLMOps! 🚀 A big thank you to Spritle Software ,and special shoutout to Balaji D Loganathan and Surendran Sukumaran for their valuable insights and guidance for fostering innovation and including me in this exciting journey. Stay tuned for more insights as we explore the future of AI operations together. #LLMOps #AI #Innovation #devops #Mlops #kubernetes

Lembrete - Amanhã 19 de Novembro às 14h BRT - Workshop Virtual Databricks - Governing Your Data Estate in the Age of AI #datagovernance #dataintelligence #dataanalytics #datascience #ai #genai #llm

Cost-effective routing could be a game-changer for large language models. Lmsys researchers just unveiled RouteLLM, an open-source framework that intelligently routes queries between powerful (expensive) and weaker (cheaper) LLMs. The key innovation? RouteLLM learns from human preferences to determine which model should handle each query - maximizing quality while minimizing costs. In tests routing between GPT-4 and a much smaller model, RouteLLM achieved 95% of GPT-4's performance at a fraction of the cost. Why does this matter? As LLMs grow larger and more capable, the compute costs are becoming prohibitive for many use cases. Intelligent routing provides a path to leverage the latest models cost-effectively. Beyond costs, routing could also enable dynamically matching queries to the most specialized or capable model. Imagine an AI assistant that seamlessly combines the strengths of different models! The possibilities are exciting as we move towards increasingly open and modular AI systems. RouteLLM is an important step in that direction. Read more about the research here: https://buff.ly/4eO36lb What are your thoughts on model routing and modular AI architectures? I'd love to hear your perspective! #RouteLLM #LargeLanguageModels #AI

Cost-effective routing could be a game-changer for large language models. Lmsys researchers just unveiled RouteLLM, an open-source framework that intelligently routes queries between powerful (expensive) and weaker (cheaper) LLMs. The key innovation? RouteLLM learns from human preferences to determine which model should handle each query - maximizing quality while minimizing costs. In tests routing between GPT-4 and a much smaller model, RouteLLM achieved 95% of GPT-4's performance at a fraction of the cost. Why does this matter? As LLMs grow larger and more capable, the compute costs are becoming prohibitive for many use cases. Intelligent routing provides a path to leverage the latest models cost-effectively. Beyond costs, routing could also enable dynamically matching queries to the most specialized or capable model. Imagine an AI assistant that seamlessly combines the strengths of different models! The possibilities are exciting as we move towards increasingly open and modular AI systems. RouteLLM is an important step in that direction. Read more about the research here: https://buff.ly/4eO36lb What are your thoughts on model routing and modular AI architectures? I'd love to hear your perspective! #RouteLLM #LargeLanguageModels #AI

RouteLLM: Achieves 95% of GPT-4’s quality at just 15% of the cost RouteLLM is revolutionizing AI by optimizing the use of large language models (LLMs). Developed by UC Berkeley and Anyscale, this open-source framework intelligently routes queries to the best-suited LLM based on cost and performance. Here’s why RouteLLM is a game-changer: Why RouteLLM? - Cost-Effective Excellence: RouteLLM routes simpler queries to cheaper models and reserves complex ones for more capable models, balancing cost and quality. - Human-Centric Design: Using human preference data, RouteLLM ensures the right model handles each query, optimizing performance and cost. - Proven Performance: Achieves 95% of GPT-4’s quality at just 15% of the cost, as demonstrated on benchmarks like MT Bench and MMLU. Key Features - Dynamic Query Routing: Automatically directs queries to the optimal model, saving costs while maintaining high-quality responses. - Open-Source Flexibility: Customize and extend RouteLLM to fit your needs. The code and datasets are publicly available for further innovation. - Robust Generalization: Maintains high performance even when switching between different strong and weak models, showcasing its adaptability. #AIRevolution #RouteLLM #CostEffectiveAI #HighPerformance #OpenSourceInnovation