Rahul Singhal’s Post

How Retrieval-Augmented Generation Enhances Generative AI The ongoing challenge for businesses is to identify safe and ethical generative AI adoption and integration practices. This includes staying up to date on technological changes that enhance the reliability and trustworthiness of AI outputs. Retrieval-augmented generation can address many of the current limitations of generative AI by reducing hallucinations and increasing accuracy and transparency #RAG #generatieveai

The role of databases in the AI revolution is paramount - especially a vector database that scales for enterprise applications. https://lnkd.in/eDne7ugE #ArtificialIntelligence #MachineLearning #LinkedInLearning

The role of databases in the AI revolution is paramount - especially a vector database that scales for enterprise applications. #ArtificialIntelligence #MachineLearning #LinkedInLearning https://lnkd.in/exe65iJa

Choosing the right generative AI components is crucial for success. 🤖 From LLMs to embedding models, a thoughtful approach is needed to consider interoperability, obsolescence, and factors like open-source vs. commercial options. What keeps you up at night when choosing generative AI tools? #AI #machinelearning #discussion #GenAi #DataRobot

In today’s AI-driven world, optimizing the use of artificial intelligence is crucial. Different AI systems vary in capability and cost, creating a need for smart management solutions. RouteLLM is an innovative framework designed to dynamically route user queries to the most suitable AI model, ensuring both cost-effectiveness and high-quality responses. What is RouteLLM? https://lnkd.in/djmx45t3

Great article about outlining your requirements and outcomes prior to engaging in AI. To maximize AI's value, understand the data required and its state before engaging in your implementation. Start small, however don't view AI as an application you can rollout or magic bullet that will automatically generate value. https://lnkd.in/g3r-MXZg

🔔 New insights await!   Don't miss out on this fascinating read for anyone interested in the cutting-edge advancements in Generative AI.   Explore how RAG is shaping the future of AI, driving innovation in various sectors, and enhancing precision in data management and query tools. 📖 Check out the full blog here: https://lnkd.in/evHPpU7N   #GenAI #RetrievalAugmentedGeneration #NewBlog #NtegraInsights 

Most AI teams have a consensus that a data-centric approach gives better results compared to the model-centric approach in vision AI development. However, for multiple reasons, teams can not opt for it because of- 1. Not enough data available 2. Synthetic data will not be suitable 3. Have enough real-world data however labeling them is expensive The most common challenge is the latter one - data labeling is expensive. We, at Labellerr started to journey to solve the 3rd challenge as I mentioned above and now with multiple iterations, we have developed a workflow that includes automation with human components to reduce the cost while maintaining quality. So that more teams can adapt the data-centric approach to their model development. Read our detailed article on the topic, here we discuss the approach, importance, and Labellerr's role to enable teams to adopt. https://lnkd.in/gj6U9Fny #datacentricai #computervision #visionai #imagelabeling

The second and third most common use cases for Generative AI are Analytics related.

Data quality: the top hurdle for successful AI? 🤖 🤔 This insideBIGDATA study explores the issue, calling for more human expertise across the #ML Ops lifecycle. Check it out! #AI #DataQuality #MachineLearning