Rajeev Sakhuja

    Rajeev Sakhuja

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    Who Is Rajeev Sakhuja?

    Rajeev Sakhuja is a dedicated technical educator and software engineering expert focused on the rapidly evolving landscape of generative artificial intelligence. With a deep foundation in full-stack architecture and machine learning systems, Rajeev specializes in bridging the gap between theoretical AI research and practical, enterprise-grade deployment. He brings a methodical approach to complex subjects, breaking down the intricacies of Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and agentic workflows into digestible, actionable concepts. His teaching philosophy centers on the 'builder-first' mindset, ensuring that every student understands not just the how, but the why behind technical architectural choices. Rajeev is committed…

    Overview of Rajeev Sakhuja

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    Rajeev Sakhuja's Programs

    Generative AI Engineering: LLMs, RAG, and Agentic Systems bannerHighest rated

    Generative AI Engineering: LLMs, RAG, and Agentic Systems

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    The Generative AI Engineering: LLMs, RAG, and Agentic Systems course, created by seasoned instructor Rajeev Sakhuja, represents an exhaustive deep dive designed to transform software engineers, developers, and system architects into highly skilled Generative AI experts. This extensive 28.5-hour curriculum bridges the gap between basic API querying and advanced production-grade AI system architecture. Learners will explore the internal mechanics of Large Language Models (LLMs) and master state-of-the-art frameworks like LangChain and LangGraph for designing intricate multi-agent systems. The program places heavy emphasis on building high-performance Retrieval-Augmented Generation (RAG) pipelines, utilizing modern embedding models, vector databases, reranking strategies, and advanced search patterns to mitigate hallucinations. Beyond theoretical concepts, students will gain hands-on experience in implementing Model Context Protocol (MCP) servers, allowing LLMs to interact seamlessly with external databases and tools. Additionally, the course tackles practical model optimization, walking developers through fine-tuning workflows using Hugging Face datasets and quantization techniques to balance computational cost and latency. By focusing on production readiness, including human-in-the-loop workflows, system evaluation, structured outputs, and scaling strategies, this training is ideal for tech professionals who want to build, deploy, and maintain robust, autonomous agentic systems in enterprise environments.

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    Rajeev focuses on the practical implementation of generative AI, specifically teaching how to build, refine, and deploy LLMs, RAG pipelines, and agentic systems in production.