Saurabh Baji’s Post

Exciting News for Generative AI Builders 🚀 Amazon Bedrock now supports the latest Cohere Rerank 3.5 model through the new Rerank API! Cohere Rerank 3.5, an advanced reranking model, provides enhanced performance for industry verticals such as finance, e-commerce, hospitality and more with SOTA performance on multi-lingual use and new reasoning capabilities. What are Rerankers? Rerankers reorder search results to highlight the most relevant information for a user's query. They achieve this by refining query results by scoring and reordering documents based on their relevance to the user's query, returning only the top matches. Enhancing your search pipeline with a reranker can significantly boost its efficiency and effectiveness. Here's how: 🔍 Increases Recall By reranking the same set of documents, you often uncover more relevant results than with semantic search alone. This means users find what they're looking for faster. 🎯 Increases Precision Rerankers score relevance at query time, contextualizing the user's query with the retrieved documents. This prioritizes the most pertinent information. ⭐️ Improves User Experience Delivering a concise list of highly relevant results reduces search time, enhancing user satisfaction and positively impacting churn and conversion rates. Learn more about this latest release and how it can enhance your AI-powered solutions in our launch blog - https://lnkd.in/gVaFP3iE Bedrock Rerank Documentation - https://lnkd.in/gjYkNmau Cohere launch blog: https://lnkd.in/g5htv4Xs 👏 Team that made it possible: Gopikrishnan Anilkumar, Juan Camilo Del Rio Cuervo, Rohan Paul, James (Zheng) Yi, Roberto Quintana, Brendan Sysun, Elliott C., Brian T. Flynn, Gloria P., Emily Han, Nick Reed, Sudip Roy, Payal Singh, Aidan Gomez, Vinod Devan, Nils Reimers #GenerativeAI #AmazonBedrock #Cohere

Exciting News for Generative AI Builders 🚀 Amazon Bedrock now supports the latest Cohere Rerank 3.5 model through the new Rerank API! Cohere Rerank 3.5, an advanced reranking model, provides enhanced performance for industry verticals such as finance, e-commerce, hospitality and more with SOTA performance on multi-lingual use and new reasoning capabilities. What are Rerankers? Rerankers reorder search results to highlight the most relevant information for a user's query. They achieve this by refining query results by scoring and reordering documents based on their relevance to the user's query, returning only the top matches. Enhancing your search pipeline with a reranker can significantly boost its efficiency and effectiveness. Here's how: 🔍 Increases Recall By reranking the same set of documents, you often uncover more relevant results than with semantic search alone. This means users find what they're looking for faster. 🎯 Increases Precision Rerankers score relevance at query time, contextualizing the user's query with the retrieved documents. This prioritizes the most pertinent information. ⭐️ Improves User Experience Delivering a concise list of highly relevant results reduces search time, enhancing user satisfaction and positively impacting churn and conversion rates. Learn more about this latest release and how it can enhance your AI-powered solutions in our launch blog - https://lnkd.in/gVaFP3iE Bedrock Rerank Documentation - https://lnkd.in/gjYkNmau Cohere launch blog: https://lnkd.in/g5htv4Xs 👏 Team that made it possible: Gopikrishnan Anilkumar, Juan Camilo Del Rio Cuervo, Rohan Paul, James (Zheng) Yi, Roberto Quintana, Brendan Sysun, Elliott C., Brian T. Flynn, Gloria P., Emily Han, Nick Reed, Sudip Roy, Payal Singh, Aidan Gomez, Vinod Devan, Nils Reimers #GenerativeAI #AmazonBedrock #Cohere

New! Welcome the latest member of the Amazon Titan family - Titan Text Premier! Great for text-based apps, optimized for RAG and AI agents. Let's dive in. Model choice is proving to be one of the most important functional requirements for generative AI success - letting customers match their gen AI use cases to models which provide the best possible fit with their needs. And then leaning into those benefits by fine-tuning the model behavior so that it's - not too hot, not too cold - just right. Today, we're expanding this choice further with the latest member of the Titan family of models: Titan Text Premier, a new LLM for text-based enterprise applications, which has been specifically optimized for RAG and agent-based applications. 🌟 Titan Text Premier has been specifically optimized for RAG with Bedrock Knowledge Bases (includes training the model to handle the nuances of these features, such as their specific prompt formats), making it easier to ground your gen AI application in your own private data, driving up accuracy for your own custom apps. 💫 Works great with Agents. Titan Text Premier outperforms comparably priced models (such as Gemini Pro 1.0 and GPT 3.5) in common-sense reasoning and reasoning over text benchmarks, both of which are important for agents being able to understand goals, and create or update strategies to achieve those goals. ✨ You can fine-tune Titan Text Premier with your own data to align the model's outputs with your company’s own brand, style, and voice, and fine-tune for more accurate interaction with services and tools in agent systems. 💂♂️ The model also integrates with Guardrails for Bedrock so you can implement additional safeguards customized to your application requirements and responsible AI policies. 🔍 Amazon indemnifies customers who responsibly use Amazon Titan models against claims that generally available Amazon Titan models or their outputs infringe on third-party copyrights. We already see customers exploring Titan Text Premier to implement interactive AI assistants that create summaries from unstructured data such as emails. They’re also exploring the model to extract relevant information across company systems and data sources to create more meaningful product summaries. Fire it up and prompt happy! #genai #aws #ai

New! Welcome the latest member of the Amazon Titan family - Titan Text Premier! Great for text-based apps, optimized for RAG and AI agents. Let's dive in. Model choice is proving to be one of the most important functional requirements for generative AI success - letting customers match their gen AI use cases to models which provide the best possible fit with their needs. And then leaning into those benefits by fine-tuning the model behavior so that it's - not too hot, not too cold - just right. Today, we're expanding this choice further with the latest member of the Titan family of models: Titan Text Premier, a new LLM for text-based enterprise applications, which has been specifically optimized for RAG and agent-based applications. 🌟 Titan Text Premier has been specifically optimized for RAG with Bedrock Knowledge Bases (includes training the model to handle the nuances of these features, such as their specific prompt formats), making it easier to ground your gen AI application in your own private data, driving up accuracy for your own custom apps. 💫 Works great with Agents. Titan Text Premier outperforms comparably priced models (such as Gemini Pro 1.0 and GPT 3.5) in common-sense reasoning and reasoning over text benchmarks, both of which are important for agents being able to understand goals, and create or update strategies to achieve those goals. ✨ You can fine-tune Titan Text Premier with your own data to align the model's outputs with your company’s own brand, style, and voice, and fine-tune for more accurate interaction with services and tools in agent systems. 💂♂️ The model also integrates with Guardrails for Bedrock so you can implement additional safeguards customized to your application requirements and responsible AI policies. 🔍 Amazon indemnifies customers who responsibly use Amazon Titan models against claims that generally available Amazon Titan models or their outputs infringe on third-party copyrights. We already see customers exploring Titan Text Premier to implement interactive AI assistants that create summaries from unstructured data such as emails. They’re also exploring the model to extract relevant information across company systems and data sources to create more meaningful product summaries. Fire it up and prompt happy! #genai #aws #ai