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₯ 234 students
π February 2026 update
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- Course Overview
- This comprehensive, concise course introduces the critical intersection of Data Pipelines, Generative AI (GenAI), and Retrieval Augmented Generation (RAG), empowering you to architect robust and scalable AI solutions. It directly addresses the contemporary challenges of deploying LLMs in enterprise environments.
- You will learn to construct scalable RAG systems that effectively integrate vast internal data sources with powerful Large Language Models, significantly enhancing the accuracy, relevance, and factuality of AI-generated responses.
- Focus is placed on bridging the gap between raw, dispersed data and intelligent GenAI applications, ensuring that LLMs can access and leverage proprietary or real-time information to provide contextually rich and up-to-date answers.
- Designed for developers, data scientists, and AI engineers, this course provides the practical knowledge and techniques required to move beyond basic LLM prompts and build sophisticated, data-aware generative AI systems.
- Explore the foundational components of a RAG architecture, from efficient data ingestion and indexing strategies to advanced prompt engineering and seamless LLM integration, all geared towards enterprise-grade deployment.
- Understand how to mitigate common LLM limitations like hallucinations and outdated knowledge by systematically retrieving pertinent information from your organizational knowledge base before generation.
- Despite its compact duration, the course is structured to deliver maximum impact, focusing on core concepts and practical implementation strategies crucial for building impactful Generative AI applications for businesses.
- Gain insight into the entire lifecycle of a RAG system, from initial data preparation and vectorization to query processing, retrieval, and intelligent response synthesis, preparing you for real-world scenarios.
- Requirements / Prerequisites
- Basic Programming Proficiency: Familiarity with Python programming is essential, as practical examples and tools will predominantly be in Python.
- Foundational Data Concepts: A general understanding of data structures, databases, and data manipulation will be beneficial for grasping pipeline concepts.
- Conceptual AI/ML Awareness: While not strictly required, a basic grasp of machine learning principles, particularly regarding natural language processing (NLP), will aid comprehension.
- Curiosity for Generative AI: An eagerness to learn about Large Language Models and their application in solving complex business problems is highly encouraged.
- Development Environment: Access to a computer with an internet connection and the ability to install necessary software libraries (e.g., Python packages).
- Skills Covered / Tools Used
- Designing Data Pipelines for RAG: Master techniques for ingesting, cleaning, transforming, and preparing diverse data sources for retrieval-augmented generation.
- Vectorization and Embedding Models: Learn to convert textual data into numerical vector embeddings using state-of-the-art models for efficient similarity search.
- Vector Database Integration: Work with concepts and practical applications of vector databases (e.g., Pinecone, Weaviate, Milvus, ChromaDB β conceptual understanding) for storing and querying embeddings at scale.
- Information Retrieval Strategies: Explore various retrieval methods, including semantic search, keyword search, and hybrid approaches, to fetch the most relevant context.
- Large Language Model (LLM) Integration: Seamlessly connect and interact with leading LLMs (e.g., OpenAI GPT series, open-source models) via APIs, understanding their input/output mechanisms.
- Prompt Engineering for RAG: Develop advanced prompt construction techniques specifically tailored for RAG systems to guide LLMs in generating accurate and coherent responses based on retrieved context.
- RAG Orchestration Frameworks: Understand the role and core functionalities of popular frameworks like LangChain or LlamaIndex in building end-to-end RAG applications.
- Scalability and Performance Optimization: Gain insights into architectural considerations, caching strategies, and indexing techniques to build RAG systems that perform efficiently under heavy loads for enterprise use.
- Evaluation Metrics for RAG: Learn to assess the effectiveness of your RAG system, evaluating retrieval precision, generation quality, and overall system performance.
- Deployment Considerations: Discuss basic principles for deploying RAG systems, including API exposition and integration into existing enterprise applications.
- Python Programming: Solidify your Python skills through hands-on examples and projects demonstrating RAG implementation.
- Data Chunking and Indexing: Explore strategies for breaking down large documents into manageable chunks and efficient indexing for optimal retrieval performance.
- Benefits / Outcomes
- Architect and Implement RAG Systems: You will gain the expertise to design, build, and deploy sophisticated Retrieval Augmented Generation pipelines from scratch.
- Enhance LLM Performance: Drastically improve the accuracy, reliability, and relevance of LLM outputs by grounding them with up-to-date and domain-specific information.
- Solve Enterprise-Specific Challenges: Apply Generative AI to real-world business problems, creating intelligent assistants, knowledge bases, and content generation tools that leverage proprietary data.
- Mitigate LLM Hallucinations: Develop strategies to effectively reduce instances of LLMs generating false or nonsensical information, building more trustworthy AI applications.
- Future-Proof Your Skills: Acquire highly sought-after skills in Generative AI, RAG, and data engineering, positioning yourself at the forefront of AI innovation.
- Develop Scalable AI Solutions: Learn to build RAG architectures capable of handling large datasets and high query volumes, suitable for production enterprise environments.
- Build a Portfolio Project: Gain practical experience that can be showcased, demonstrating your ability to implement complex GenAI systems.
- Deepen Understanding of GenAI Ecosystem: Develop a holistic view of how data infrastructure, LLMs, and retrieval mechanisms interact to create powerful AI applications.
- PROS
- Highly Relevant & In-Demand Topic: Covers cutting-edge Generative AI techniques that are critical for current and future AI development.
- Practical & Hands-On Focus: Emphasizes building actual systems, providing actionable skills rather than just theoretical knowledge.
- Addresses Core LLM Limitations: Directly tackles issues like hallucination and outdated information, offering tangible solutions.
- Enterprise Scalability Focus: Geared towards creating robust, production-ready RAG systems suitable for large organizations.
- Concise and Efficient Learning: With a 2.9-hour duration, it offers a quick yet impactful dive into a complex subject.
- Positive Student Feedback: A high rating (4.60/5) and substantial student count (234) indicate a well-received and effective course.
- Up-to-Date Content: The February 2026 update ensures the material reflects the latest advancements and best practices in the rapidly evolving AI landscape.
- CONS
- Limited Depth for Complex Topics: The relatively short duration of 2.9 hours means that while core concepts are covered, highly advanced optimization, specific cloud deployments, or extensive comparisons of niche tools might not be explored in exhaustive detail.
Learning Tracks: English,IT & Software,Other IT & Software
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