Raghu Murali

🚀 Just wrapped up a hands-on project applying Retrieval-Augmented Generation (RAG) to a real-world use case: interpreting LIC’s New Jeevan Shanti policy document using LLMs. The core idea? Combine vector search over regulatory PDFs with GPT-4 to generate grounded, faithful answers to complex policy questions. Key capabilities we built: ✅ LCEL-based RAG architecture using LangChain ✅ Contextual compression retriever for focused document lookup ✅ Modular LLM & embedding selection (OpenAI, HuggingFace, Mistral) ✅ Evaluation using LLM-powered metrics: faithfulness, relevance, correctness 📄 The document used is publicly available here: LIC New Jeevan Shanti Policy PDF 💻 Full code available on GitHub: https://lnkd.in/gS9mEzWA 💡 This architecture brings the best of both worlds — retrieval accuracy and generative flexibility — especially in regulated domains like insurance and financial services. 🔜 Next step: Incorporate Agents that can reason across multiple steps — not just answer a question, but decide which tools to use, when to search, how to validate, and when to stop. That’s where the future of enterprise GenAI lies. Would love to hear how others are pushing RAG, agents, and LLMs in production! #GenAI #LangChain #LLM #RAG #AIForOps #InsuranceTech #LLMEvaluation #LangChainAgents

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A very nice compilation along with observations and recommendations on Creating products/solutions from Research. IIITH is one of the 6 institutions studied (others being IISc, IITD, IITB, BITS and CCAMP).

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One of the most transformative innovations powering today’s GenAI revolution is the advancement of foundation models (FMs) — large models pre-trained on Internet-scale data that demonstrate remarkable generalization across a wide range of tasks. FMs have redefined performance benchmarks in many fields including, natural language processing, computer vision, and computational biology, becoming the cornerstone of modern GenAI. Yet, progress on structured enterprise data such as tabular time series has lagged behind, where task-specific models optimized on manually extracted signals still dominate. Inspired by the success of FMs in other domains, we set out to explore whether FMs can also be built for finance and banking. After over three years of R&D, I’m excited to share that we’ve deployed our first structured-data FM into production at TD: TD AI Prism https://lnkd.in/gEAb7Scm. Trained on billions of customer events, Prism generalizes to many downstream predictive tasks, delivering higher accuracy and significantly faster time to market. Through this work, we're beginning to observe early signs of scaling laws, similar to those seen in LLMs, where more data and larger models lead to stronger generalization. As part of this effort, we also released a public FM trained on Internet tabular data, capable of making predictions on new datasets without any fine-tuning. Check it out here: https://lnkd.in/gmbWdAf7 As enterprises evolve with AI, we believe that foundation models for structured data will be a major force in redefining how predictive AI is deployed at scale, driving the next wave of innovation in enterprise applications. Layer 6 AI has multiple exciting initiatives in this space, so stay tuned for more updates!

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This article, co-authored by Vardaan Ahluwalia and me lays out India's significant new Research Development and Innovation Fund initiative in the context of other successful interventions: when governments and the private sector work together, they can make a transformative difference to deep technology entrepreneurship. Equally, initiatives to help manage financial risk should be followed by structural reforms in areas ranging from intellectual property regulation, ease of clearances for start-ups, tax rationalization in capital gains and options that helps start-ups attract talent, and a visa regime that attracts leading global researchers. https://lnkd.in/gk92YSZa

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Our own EVP, Chief Scientist & Head of Enterprise AI Prem Natarajan recently spoke to TechTarget about Capital One’s partnership with the National Science Foundation (NSF), which will help launch five new National AI Research Institutes to drive breakthroughs in high-impact areas across society like material and computational sciences, economics, and mental health. https://bit.ly/4grWY3m

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We are excited to co-host this upcoming AI leadership session with Tandon Consulting Club and our partners at Brooklyn College The City University of New York. We’ll be discussing how AI is shaping enterprise consulting in real organizational settings. I hope to see many of you there. 😍

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