Sumeet Malhotra

New from Packt Publishing >> "Building Neo4j-Powered Applications with LLMs: Create LLM-driven search and recommendations applications with Haystack, LangChain4j, and Spring AI" by Ravindranatha Anthapu and Siddhant Agarwal Available at https://amzn.to/4l9lLKO This LLM book is for database developers and data scientists who want to leverage knowledge graphs with Neo4j and its vector search capabilities to build intelligent search and recommendation systems. Working knowledge of Python and Java is essential to follow along. Familiarity with Neo4j, the Cypher query language, and fundamental concepts of databases will come in handy. 𝓦𝓱𝓪𝓽 𝓨𝓸𝓾 𝓦𝓲𝓵𝓵 𝓛𝓮𝓪𝓻𝓷: 🟠Design, populate, and integrate a Neo4j knowledge graph with RAG 🔵Model data for knowledge graphs 🟢Integrate AI-powered search to enhance knowledge exploration 🟠Maintain and monitor your AI search application with Haystack 🔵Use LangChain4j and Spring AI for recommendations and personalization 🟢Seamlessly deploy your applications to Google Cloud Platform

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An important article I wrote that differentiates LLM assistance and agentic AI. Please read. 🔗 https://lnkd.in/eb8mJyjJ A note for those applying to jobs: This matters for your career too. When you're in an interview and someone asks what you built, be precise. If you used an LLM to help you write code faster, say that, don't call it an agent. If you built a RAG pipeline, call it retrieval-augmented generation, don't call it agentic. If you built a chatbot with a system prompt, own it for what it is. Hiring managers who know the difference will respect your precision. Hiring managers who don't know the difference need you to teach them. Either way, you win by naming it correctly. Overselling what you built doesn't just hurt your credibility when someone digs deeper, it tells the room you don't actually understand the architecture you're describing. Know what you built. Know what it's called. Know why it matters at that level. That's what gets you hired. #AI #AgenticAI #LLM #NLP #SportsAnalytics #DataScience #CareerAdvice #MachineLearning

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Deepanker Venkatesh and I recently authored a perspective on the ISDA Common Domain Model (CDM), a foundational development shaping the future of capital markets. ISDA CDM is more than a standard. It represents: • Industry-wide consistency in how data and workflows are defined and executed. • Operational efficiency and risk reduction through streamlined post-trade processes. • Strategic readiness for regulatory alignment and cross-market interoperability. For market participants, the opportunity lies in moving from awareness to action. Engaging early with CDM will better position organizations to capture efficiencies, enable innovation, and strengthen competitive advantage. I welcome thoughts on how CDM will influence the operating model of the industry in the years ahead. Nageswar Cherukupalli, Meena Athinathan, Edward R. Merchant, Anke Bivens, Cary Ellexson, Sathish Ranganathan, Sachin Khatri, Vikesh Gupta, Andreas Pilleris

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ParadigmForge AI Inc. has developed several advanced technologies to enhance regulatory compliance and decision-making, including: Integrating cryptographic evidence chains, graph-based reasoning, and multi-model consensus validation. Designing to withstand regulatory audits and legal scrutiny, unlike traditional AI systems. Currently, our innovative, cutting-edge AI models include 6 models. #RegTech #GovTech #PublicPolicy #RegulatoryCompliance #TrustedAI #ResponsibleAI #ComplianceFirst #DigitalGovernment

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Deep research agent is a stepup from traditional RAG systems. This blog https://lnkd.in/dQpTi8hE by Sameer Rastogi delves into the architecture of deep research agents, demonstrating how its strategic, multi agent workflow allows it to perform complex research and generate high quality structured reports. #egnyte #engineering #deepresearch

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🚀 How to Evaluate LLM Applications Large Language Models (LLMs) are powerful, but their real impact depends on how we evaluate them. The right benchmarks and evaluation methods ensure accuracy, fairness, and real-world applicability. 🔍 Key Takeaways from the Framework: ✅ Benchmarks Language Understanding & QA: TruthfulQA, MMLU, DROP Common-sense & Reasoning: ARC, HellaSwag, BIG-Bench Hard Coding: HumanEval, CodeXGLUE Conversation & Chatbots: Chatbot Arena, MT Bench, Stanford HELM ✅ Evaluation Methods Character Level: Levenshtein Distance Word Based: BLEU, ROUGE, METEOR, Perplexity Embedding Based: BERTScore, MoverScore, QAG Score Language Model Based: GPTScore, SelfCheckGPT, G-Eval, Prometheus NLP Models: NLI, BLEURT Frameworks: DeepEval, RAGAS 💡 Whether you’re building chatbots, coding assistants, or reasoning systems, choosing the right benchmark + evaluation method is crucial to measure performance and reliability. 👉 Question for you: Which evaluation method do you think gives the most real-world reflection of LLM performance traditional metrics like BLEU/ROUGE or LLM-based evaluations like GPTScore & SelfCheckGPT? #LLM #AI #MachineLearning #NLP #Evaluation #GenAI #ArtificialIntelligence

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