{"id":64563,"date":"2025-06-17T16:12:24","date_gmt":"2025-06-17T10:42:24","guid":{"rendered":"https:\/\/staging.nanoschool.in\/wp\/?p=64563"},"modified":"2025-11-19T15:28:10","modified_gmt":"2025-11-19T09:58:10","slug":"building-rag-pipelines-with-llms","status":"publish","type":"post","link":"https:\/\/staging.nanoschool.in\/wp\/aipg\/building-rag-pipelines-with-llms\/","title":{"rendered":"Building RAG Pipelines with LLMs"},"content":{"rendered":"<div class=\"nsp-isolated-wrapper\">\n<p><meta name=\"viewport\" content=\"width=device-width, initial-scale=1.0\">\n    <link rel=\"stylesheet\" href=\"https:\/\/cdnjs.cloudflare.com\/ajax\/libs\/font-awesome\/6.0.0-beta3\/css\/all.min.css\">\n<style>\n* {\nbox-sizing: border-box;\nmargin: 0;\npadding: 0;\n}\nbody {\nfont-family: Arial, sans-serif;\n}\n.aih-navbar {\ndisplay: flex;\njustify-content: space-between; \nalign-items: center;\npadding: 10px 20px;\nbackground-color: #ffffff;\nborder-bottom: 1px solid #eeeeee;\nheight: 60px; \n}\n.aih-logo-container {\ndisplay: flex;\nalign-items: center;\n}\n.aih-logo-img {\nheight: 35px; 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RAG enhances AI responses by grounding them in factual, external data\u2014making this a must-learn skill for developers, researchers, and innovators working in knowledge-intensive domains like legal tech, finance, healthcare, and education.<\/p>\n<\/div>\n<\/section>\n<section class=\"nsp-block\">\n<h2>Aim<\/h2>\n<div>\n<p data-start=\"338\" data-end=\"563\">To provide participants with practical skills and technical knowledge to design, build, and deploy Retrieval-Augmented Generation (RAG) pipelines using Large Language Models (LLMs) for accurate, context-aware AI applications.<\/p>\n<\/div>\n<\/section>\n<section class=\"nsp-block\">\n<h2>Program Objectives<\/h2>\n<div>\n<ul>\n<li data-start=\"3553\" data-end=\"3629\">\n<p data-start=\"3555\" data-end=\"3629\">To train participants in the practical construction of RAG architectures<\/p>\n<\/li>\n<li data-start=\"3630\" data-end=\"3697\">\n<p data-start=\"3632\" data-end=\"3697\">To deepen understanding of how LLMs interact with external data<\/p>\n<\/li>\n<li data-start=\"3698\" data-end=\"3779\">\n<p data-start=\"3700\" data-end=\"3779\">To empower learners to build scalable, accurate, and context-aware AI systems<\/p>\n<\/li>\n<li data-start=\"3780\" data-end=\"3848\">\n<p data-start=\"3782\" data-end=\"3848\">To prepare professionals for high-demand GenAI engineering roles<\/p>\n<\/li>\n<\/ul>\n<\/div>\n<\/section>\n<section class=\"nsp-block\">\n<h2>Program Structure<\/h2>\n<h2 data-start=\"273\" data-end=\"329\">Week 1: Foundations of Retrieval-Augmented Generation<\/h2>\n<h3 data-start=\"331\" data-end=\"372\">Module 1: Introduction to RAG Systems<\/h3>\n<ul data-start=\"373\" data-end=\"518\">\n<li data-start=\"373\" data-end=\"427\">\n<p data-start=\"375\" data-end=\"427\">Chapter 1.1: What is Retrieval-Augmented Generation?<\/p>\n<\/li>\n<li data-start=\"428\" data-end=\"471\">\n<p data-start=\"430\" data-end=\"471\">Chapter 1.2: Components of a RAG Pipeline<\/p>\n<\/li>\n<li data-start=\"472\" data-end=\"518\">\n<p data-start=\"474\" data-end=\"518\">Chapter 1.3: Benefits and Limitations of RAG<\/p>\n<\/li>\n<\/ul>\n<h3 data-start=\"520\" data-end=\"578\">Module 2: Understanding Retrieval and Vector Databases<\/h3>\n<ul data-start=\"579\" data-end=\"742\">\n<li data-start=\"579\" data-end=\"620\">\n<p data-start=\"581\" data-end=\"620\">Chapter 2.1: Dense vs. Sparse Retrieval<\/p>\n<\/li>\n<li data-start=\"621\" data-end=\"673\">\n<p data-start=\"623\" data-end=\"673\">Chapter 2.2: Vector Embeddings and Semantic Search<\/p>\n<\/li>\n<li data-start=\"674\" data-end=\"742\">\n<p data-start=\"676\" data-end=\"742\">Chapter 2.3: Overview of Tools (FAISS, Weaviate, Pinecone, Qdrant)<\/p>\n<\/li>\n<\/ul>\n<hr data-start=\"744\" data-end=\"747\" \/>\n<h2 data-start=\"749\" data-end=\"787\">Week 2: Building the Core RAG Stack<\/h2>\n<h3 data-start=\"789\" data-end=\"831\">Module 3: Integrating LLMs with Search<\/h3>\n<ul data-start=\"832\" data-end=\"1028\">\n<li data-start=\"832\" data-end=\"890\">\n<p data-start=\"834\" data-end=\"890\">Chapter 3.1: Embedding Generation (OpenAI, Hugging Face)<\/p>\n<\/li>\n<li data-start=\"891\" data-end=\"943\">\n<p data-start=\"893\" data-end=\"943\">Chapter 3.2: Chunking and Preprocessing Strategies<\/p>\n<\/li>\n<li data-start=\"944\" data-end=\"983\">\n<p data-start=\"946\" data-end=\"983\">Chapter 3.3: Prompt Templates for RAG<\/p>\n<\/li>\n<li data-start=\"984\" data-end=\"1028\">\n<p data-start=\"986\" data-end=\"1028\">Chapter 3.4: Connecting LLMs to Vector DBs<\/p>\n<\/li>\n<\/ul>\n<h3 data-start=\"1030\" data-end=\"1069\">Module 4: RAG System Implementation<\/h3>\n<ul data-start=\"1070\" data-end=\"1262\">\n<li data-start=\"1070\" data-end=\"1116\">\n<p data-start=\"1072\" data-end=\"1116\">Chapter 4.1: Document Ingestion and Indexing<\/p>\n<\/li>\n<li data-start=\"1117\" data-end=\"1165\">\n<p data-start=\"1119\" data-end=\"1165\">Chapter 4.2: Query Handling and Retrieval Flow<\/p>\n<\/li>\n<li data-start=\"1166\" data-end=\"1210\">\n<p data-start=\"1168\" data-end=\"1210\">Chapter 4.3: Response Synthesis using LLMs<\/p>\n<\/li>\n<li data-start=\"1211\" data-end=\"1262\">\n<p data-start=\"1213\" data-end=\"1262\">Chapter 4.4: Evaluation Metrics for RAG Responses<\/p>\n<\/li>\n<\/ul>\n<hr data-start=\"1264\" data-end=\"1267\" \/>\n<h2 data-start=\"1269\" data-end=\"1318\">Week 3: Optimization, Deployment, and Projects<\/h2>\n<h3 data-start=\"1320\" data-end=\"1357\">Module 5: Advanced RAG Techniques<\/h3>\n<ul data-start=\"1358\" data-end=\"1512\">\n<li data-start=\"1358\" data-end=\"1406\">\n<p data-start=\"1360\" data-end=\"1406\">Chapter 5.1: Hybrid Search (BM25 + Embeddings)<\/p>\n<\/li>\n<li data-start=\"1407\" data-end=\"1463\">\n<p data-start=\"1409\" data-end=\"1463\">Chapter 5.2: RAG with Structured and Unstructured Data<\/p>\n<\/li>\n<li data-start=\"1464\" data-end=\"1512\">\n<p data-start=\"1466\" data-end=\"1512\">Chapter 5.3: Multi-turn and Conversational RAG<\/p>\n<\/li>\n<\/ul>\n<h3 data-start=\"1514\" data-end=\"1551\">Module 6: Deployment and Capstone<\/h3>\n<ul data-start=\"1552\" data-end=\"1782\">\n<li data-start=\"1552\" data-end=\"1617\">\n<p data-start=\"1554\" data-end=\"1617\">Chapter 6.1: Deploying RAG Systems with LangChain or LlamaIndex<\/p>\n<\/li>\n<li data-start=\"1618\" data-end=\"1668\">\n<p data-start=\"1620\" data-end=\"1668\">Chapter 6.2: Monitoring, Caching, and API Design<\/p>\n<\/li>\n<li data-start=\"1669\" data-end=\"1730\">\n<p data-start=\"1671\" data-end=\"1730\">Chapter 6.3: Capstone Project \u2013 Build Your Own RAG Pipeline<\/p>\n<\/li>\n<li data-start=\"1731\" data-end=\"1782\">\n<p data-start=\"1733\" data-end=\"1782\">Chapter 6.4: Industry Use Cases and Future Trends<\/p>\n<\/li>\n<\/ul>\n<\/section>\n<section class=\"nsp-block\">\n<h2>Who Should Enrol?<\/h2>\n<div>\n<ul>\n<li data-start=\"2534\" data-end=\"2596\">\n<p data-start=\"2536\" data-end=\"2596\">Developers, data scientists, researchers, and AI engineers<\/p>\n<\/li>\n<li data-start=\"2597\" data-end=\"2660\">\n<p data-start=\"2599\" data-end=\"2660\">Graduate students and working professionals in AI\/ML or NLP<\/p>\n<\/li>\n<li data-start=\"2661\" data-end=\"2717\">\n<p data-start=\"2663\" data-end=\"2717\">Prior experience with Python and APIs is recommended<\/p>\n<\/li>\n<\/ul>\n<\/div>\n<\/section>\n<section class=\"nsp-block\">\n<h2>Program Outcomes<\/h2>\n<div>\n<ul>\n<li data-start=\"3294\" data-end=\"3358\">\n<p data-start=\"3296\" data-end=\"3358\">Understand how to build and optimize a complete RAG pipeline<\/p>\n<\/li>\n<li data-start=\"3359\" data-end=\"3424\">\n<p data-start=\"3361\" data-end=\"3424\">Work with real-world data to build question-answering systems<\/p>\n<\/li>\n<li data-start=\"3425\" data-end=\"3469\">\n<p data-start=\"3427\" data-end=\"3469\">Integrate vector databases with LLM APIs<\/p>\n<\/li>\n<li data-start=\"3470\" data-end=\"3525\">\n<p data-start=\"3472\" data-end=\"3525\">Design 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#eef2f7!important;border-radius:12px!important;box-sizing:border-box!important;\" aria-label=\"Testimonial Section\"> <div style=\"all:initial!important;flex:0 1 320px!important;width:auto!important;background:#ffffff!important;border:1px solid #e5e7eb!important;border-radius:12px!important;box-shadow:0 6px 18px rgba(15,23,42,.06)!important;padding:16px 18px!important;color:#0f172a!important;font:400 14px\/1.55 system-ui,-apple-system,Segoe UI,Roboto,Ubuntu,Cantarell,Noto Sans,sans-serif!important;text-align:left!important;transition:transform .2s ease, box-shadow .2s ease, border-color .2s ease!important;box-sizing:border-box!important;\" role=\"listitem\"> <div style=\"all:initial!important;line-height:1!important;\"> <span style=\"all:initial!important;font-size:18px!important;color:#f59e0b!important;fill:currentColor!important;vertical-align:middle!important;\">\u2605\u2605\u2605\u2605\u2605<\/span> <\/div> <div style=\"all:initial!important;display:flex!important;align-items:center!important;justify-content:space-between!important;gap:10px!important;margin-bottom:8px!important;\"> <strong style=\"all:initial!important;color:#0f172a!important;font-weight:700!important;font-size:16px!important;\">Cancer Drug Discovery: Creating Cancer Therapies<\/strong> <\/div> <span style=\"all:initial!important;display:block!important;margin-top:6px!important;color:#334155!important;font-size:14px!important;max-height:80px !important;overflow:hidden!important;text-overflow:ellipsis!important;\" aria-label=\"Testimonial Content: <p>Undoubtedly, the professor's expertise was evident, and their ability to cover a vast amount of material within the given timeframe was impressive. However, the pace at which the content was presented made it challenging for some attendees, including myself, to fully grasp and absorb the information.<\/p>\n\"><p>Undoubtedly, the professor's expertise was evident, and their ability to cover a vast amount of material within the given timeframe was impressive. However, the pace at which the content was presented made it challenging for some attendees, including myself, to fully grasp and absorb the information.<\/p>\n<\/span> <span style=\"all:initial!important;display:block!important;margin-top:10px!important;font-size:12px!important;color:#64748b!important;\"> <span aria-label=\"Testimonial Author: Mario Rigo\" style=\"all:initial!important;\">Mario Rigo<\/span> \u2022 <time style=\"all:initial!important;\" datetime=\"November 30, 2023 at 5:18 pm\">November 30, 2023 at 5:18 pm<\/time><\/span><\/div><\/div>\n<\/div><\/div><div class=\"frm_grid_container\"><div class=\"frm12\" style=\"border-color: #ffffff;background-color: #ffffff;\"><div style=\"all:initial!important;display:flex!important;flex-wrap:wrap!important;gap:18px!important;justify-content:center!important;align-items:stretch!important;width:100%!important;max-width:1200px!important;margin:0 auto!important;padding:14px!important;background:#f8fafc!important;border:1px solid #eef2f7!important;border-radius:12px!important;box-sizing:border-box!important;\" aria-label=\"Testimonial Section\"> <div style=\"all:initial!important;flex:0 1 320px!important;width:auto!important;background:#ffffff!important;border:1px solid #e5e7eb!important;border-radius:12px!important;box-shadow:0 6px 18px rgba(15,23,42,.06)!important;padding:16px 18px!important;color:#0f172a!important;font:400 14px\/1.55 system-ui,-apple-system,Segoe UI,Roboto,Ubuntu,Cantarell,Noto Sans,sans-serif!important;text-align:left!important;transition:transform .2s ease, box-shadow .2s ease, border-color .2s ease!important;box-sizing:border-box!important;\" role=\"listitem\"> <div style=\"all:initial!important;line-height:1!important;\"> <span style=\"all:initial!important;font-size:18px!important;color:#f59e0b!important;fill:currentColor!important;vertical-align:middle!important;\">\u2605\u2605\u2605\u2605\u2605<\/span> <\/div> <div style=\"all:initial!important;display:flex!important;align-items:center!important;justify-content:space-between!important;gap:10px!important;margin-bottom:8px!important;\"> <strong style=\"all:initial!important;color:#0f172a!important;font-weight:700!important;font-size:16px!important;\">Power BI and Advanced SQL Mastery Integration Workshop, CRISPR-Cas Genome Editing: Workflow, Tools and Techniques<\/strong> <\/div> <span style=\"all:initial!important;display:block!important;margin-top:6px!important;color:#334155!important;font-size:14px!important;max-height:80px !important;overflow:hidden!important;text-overflow:ellipsis!important;\" aria-label=\"Testimonial Content: <p>Good! Thank you<\/p>\n\"><p>Good! Thank you<\/p>\n<\/span> <span style=\"all:initial!important;display:block!important;margin-top:10px!important;font-size:12px!important;color:#64748b!important;\"> <span aria-label=\"Testimonial Author: Silvia Santopolo\" style=\"all:initial!important;\">Silvia Santopolo<\/span> \u2022 <time style=\"all:initial!important;\" datetime=\"December 5, 2023 at 4:01 pm\">December 5, 2023 at 4:01 pm<\/time><\/span><\/div><\/div>\n<\/div><\/div><div class=\"frm_grid_container\"><div class=\"frm12\" style=\"border-color: #ffffff;background-color: #ffffff;\"><div style=\"all:initial!important;display:flex!important;flex-wrap:wrap!important;gap:18px!important;justify-content:center!important;align-items:stretch!important;width:100%!important;max-width:1200px!important;margin:0 auto!important;padding:14px!important;background:#f8fafc!important;border:1px solid #eef2f7!important;border-radius:12px!important;box-sizing:border-box!important;\" aria-label=\"Testimonial Section\"> <div style=\"all:initial!important;flex:0 1 320px!important;width:auto!important;background:#ffffff!important;border:1px solid #e5e7eb!important;border-radius:12px!important;box-shadow:0 6px 18px rgba(15,23,42,.06)!important;padding:16px 18px!important;color:#0f172a!important;font:400 14px\/1.55 system-ui,-apple-system,Segoe UI,Roboto,Ubuntu,Cantarell,Noto Sans,sans-serif!important;text-align:left!important;transition:transform .2s ease, box-shadow .2s ease, border-color .2s ease!important;box-sizing:border-box!important;\" role=\"listitem\"> <div style=\"all:initial!important;line-height:1!important;\"> <span style=\"all:initial!important;font-size:18px!important;color:#f59e0b!important;fill:currentColor!important;vertical-align:middle!important;\">\u2605\u2605\u2605\u2605\u2605<\/span> <\/div> <div style=\"all:initial!important;display:flex!important;align-items:center!important;justify-content:space-between!important;gap:10px!important;margin-bottom:8px!important;\"> <strong style=\"all:initial!important;color:#0f172a!important;font-weight:700!important;font-size:16px!important;\">Artificial Intelligence for Cancer Drug Delivery<\/strong> <\/div> <span style=\"all:initial!important;display:block!important;margin-top:6px!important;color:#334155!important;font-size:14px!important;max-height:80px !important;overflow:hidden!important;text-overflow:ellipsis!important;\" aria-label=\"Testimonial Content: <p>Informative lectures<\/p>\n\"><p>Informative lectures<\/p>\n<\/span> <span style=\"all:initial!important;display:block!important;margin-top:10px!important;font-size:12px!important;color:#64748b!important;\"> <span aria-label=\"Testimonial Author: G Jyothi\" style=\"all:initial!important;\">G Jyothi<\/span> \u2022 <time style=\"all:initial!important;\" datetime=\"January 18, 2024 at 11:44 pm\">January 18, 2024 at 11:44 pm<\/time><\/span><\/div><\/div>\n<\/div><\/div><div class=\"frm_grid_container\"><div class=\"frm12\" style=\"border-color: #ffffff;background-color: #ffffff;\"><div style=\"all:initial!important;display:flex!important;flex-wrap:wrap!important;gap:18px!important;justify-content:center!important;align-items:stretch!important;width:100%!important;max-width:1200px!important;margin:0 auto!important;padding:14px!important;background:#f8fafc!important;border:1px solid #eef2f7!important;border-radius:12px!important;box-sizing:border-box!important;\" aria-label=\"Testimonial Section\"> <div style=\"all:initial!important;flex:0 1 320px!important;width:auto!important;background:#ffffff!important;border:1px solid #e5e7eb!important;border-radius:12px!important;box-shadow:0 6px 18px rgba(15,23,42,.06)!important;padding:16px 18px!important;color:#0f172a!important;font:400 14px\/1.55 system-ui,-apple-system,Segoe UI,Roboto,Ubuntu,Cantarell,Noto Sans,sans-serif!important;text-align:left!important;transition:transform .2s ease, box-shadow .2s ease, border-color .2s ease!important;box-sizing:border-box!important;\" role=\"listitem\"> <div style=\"all:initial!important;line-height:1!important;\"> <span style=\"all:initial!important;font-size:18px!important;color:#f59e0b!important;fill:currentColor!important;vertical-align:middle!important;\">\u2605\u2605\u2605\u2605\u2605<\/span> <\/div> <div style=\"all:initial!important;display:flex!important;align-items:center!important;justify-content:space-between!important;gap:10px!important;margin-bottom:8px!important;\"> <strong style=\"all:initial!important;color:#0f172a!important;font-weight:700!important;font-size:16px!important;\">Artificial Intelligence for Cancer Drug Delivery<\/strong> <\/div> <span style=\"all:initial!important;display:block!important;margin-top:6px!important;color:#334155!important;font-size:14px!important;max-height:80px !important;overflow:hidden!important;text-overflow:ellipsis!important;\" aria-label=\"Testimonial Content: <p>delt with all the topics associated with the subject matter<\/p>\n\"><p>delt with all the topics 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