Build Scalable RAG Systems with Data Pipelines, LLM Integration & Prompt Engineering for Enterprise Generative AI
β±οΈ Length: 2.9 total hours
β 4.60/5 rating
π₯ 279 students
π February 2026 update
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- Course Overview
- This comprehensive course teaches you to build robust and scalable Generative AI applications by seamlessly integrating advanced Data Pipelines with cutting-edge Retrieval Augmented Generation (RAG) systems.
- Learn to overcome common Large Language Model (LLM) limitations like hallucination, knowledge cutoffs, and outdated information by empowering models with real-time, domain-specific context through intelligent data retrieval.
- Cover the full development lifecycle of enterprise-grade GenAI solutions: from efficient data ingestion and vectorization to sophisticated prompt engineering, LLM integration, and system orchestration.
- Designed for data scientists, machine learning engineers, and AI developers, this course provides the practical skills necessary to deploy powerful, context-aware generative AI applications in production environments.
- Requirements / Prerequisites
- Solid Python programming skills are essential, including familiarity with core data structures, functions, and libraries, as Python will be the primary language used for development and system implementation.
- Basic conceptual understanding of machine learning fundamentals (e.g., model training, evaluation, feature engineering) will be beneficial for grasping the underlying principles of generative AI and RAG architectures, though no advanced ML expertise is strictly mandatory.
- Familiarity with data processing concepts such as ETL (Extract, Transform, Load) pipelines, data warehousing, and basic database principles will provide a strong foundation for designing efficient data workflows within RAG systems.
- Skills Covered / Tools Used
- Data Pipeline Engineering for AI: Master techniques for efficient data ingestion, cleaning, transformation, and embedding diverse datasets into vector stores, specifically optimizing data flows for RAG system performance and scalability.
- Vector Databases & Semantic Search: Gain proficiency in selecting, implementing, and querying various vector databases (e.g., principles of Pinecone, ChromaDB, Weaviate) to perform high-performance semantic searches and retrieve relevant context for LLMs.
- Advanced Prompt Engineering: Develop expert-level skills in crafting effective prompts, including few-shot prompting, chain-of-thought techniques, and robust output parsing, to maximize LLM accuracy, relevance, and ensure desired response formats.
- LLM Integration & Orchestration: Learn to integrate leading Large Language Models (e.g., OpenAI, Hugging Face models APIs) into custom applications, utilizing powerful orchestration frameworks like LangChain or LlamaIndex for complex prompt chaining and agentic behavior.
- RAG System Architecture & Deployment: Understand best practices for designing scalable and resilient RAG architectures, considering factors like document chunking strategies, retrieval optimization, and enterprise deployment patterns for real-world scenarios.
- Benefits / Outcomes
- Upon completion, you will be capable of designing and implementing complete end-to-end RAG systems that effectively augment LLMs with accurate, up-to-date external information, significantly boosting their reliability and factual grounding.
- You will gain the expertise to build scalable data pipelines specifically tailored to feed and continuously update dynamic knowledge bases for advanced generative AI applications, ensuring consistent context availability and freshness.
- Develop a robust portfolio by creating practical, production-ready GenAI solutions that skillfully combine data engineering rigor with cutting-edge LLM integration and advanced prompt engineering techniques, positioning you as a valuable asset in the AI landscape.
- Confidently address critical enterprise AI challenges related to data governance, knowledge management, and building trustworthy AI, by implementing transparent, controllable, and contextually rich generative AI systems.
- PROS
- Highly practical and project-focused curriculum, emphasizing the hands-on construction of scalable, real-world Generative AI systems.
- Directly tackles the crucial challenge of grounding LLMs with accurate, current, and domain-specific data, effectively mitigating common issues like hallucination.
- Comprehensive coverage from foundational data pipeline engineering to advanced RAG system design and expert-level prompt engineering techniques.
- Equips learners with highly sought-after, in-demand skills for designing, developing, and deploying enterprise-grade generative AI applications.
- Provides a clear, actionable methodology for enhancing the factual accuracy, reliability, and trustworthiness of AI outputs.
- CONS
- The course’s fast pace and integration of multiple advanced topics may prove challenging for learners without a solid prior foundation in both Python programming and basic data engineering concepts.
Learning Tracks: English,IT & Software,Other IT & Software