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Vector Databases & RAG (Retrieval-Augmented Generation) with LlamaIndex

Build smarter, context-aware AI applications using vector databases and Retrieval-Augmented Generation with LlamaIndex.
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Course Duration: 10 Hours
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The Vector Databases & RAG with LlamaIndex course is an advanced program designed for AI developers, data engineers, and NLP practitioners seeking to enhance Large Language Models (LLMs) with real-world knowledge using Retrieval-Augmented Generation (RAG) techniques. In this hands-on course, you'll learn to use LlamaIndex—a popular framework for LLM-augmented applications—to build intelligent, contextual, and up-to-date AI systems that go beyond static training data.
 
What Is RAG and How Does It Work?
 
Retrieval-Augmented Generation (RAG) is a method that combines the reasoning power of LLMs with the precision and freshness of external data sources—typically stored in vector databases. Instead of relying solely on a model’s internal parameters, RAG systems retrieve relevant documents from a database and pass them into the LLM’s prompt at runtime. This ensures that the model generates responses grounded in facts, domain-specific knowledge, or real-time information.
 
Here’s a simplified RAG pipeline:
  1. Ingestion: Documents, PDFs, webpages, and structured data are ingested and broken into chunks.
  2. Embedding: Each chunk is converted into a vector representation using embedding models like OpenAI, Hugging Face, or Cohere.
  3. Storage: These embeddings are stored in a vector database like FAISS, Pinecone, Weaviate, or Chroma.
  4. Retrieval: When a user asks a question, the system converts the query into a vector and retrieves the most semantically relevant documents.
  5. Generation: The retrieved context is injected into the LLM prompt, allowing the model to generate an informed, grounded answer.
LlamaIndex acts as the bridge between LLMs and data sources, managing document ingestion, indexing, querying, and integration with vector stores—making it the go-to framework for implementing production-grade RAG pipelines.
 
How to Use This Course
 
This course provides a practical learning path, starting from core vector database concepts and progressing to full-stack RAG systems with LlamaIndex. You’ll work on real-world projects including knowledge assistants, semantic search engines, and enterprise Q&A bots.
 
To maximize your learning:
  • Start with foundational concepts of embeddings and similarity search.
  • Build simple RAG workflows using FAISS and LlamaIndex.
  • Progress to building agents and apps that use external tools, APIs, and structured documents.
  • Learn to optimize chunking, indexing, and retrieval strategies.
  • Explore advanced use cases like query decomposition and hybrid search.
By the end of the course, you’ll be able to build AI systems that stay updated, verifiable, and capable of answering questions using your own data.

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  • The videos for this course are being recorded freshly and should be available in a few days. Please contact info@uplatz.com to know the exact date of the release of this course.

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Course Objectives Back to Top
By the end of this course, learners will be able to:
  1. Understand the RAG architecture and its role in enhancing LLMs.
  2. Create vector indexes from custom datasets using LlamaIndex.
  3. Store and retrieve vectors using FAISS, Pinecone, or Chroma.
  4. Build semantic search and document QA systems.
  5. Optimize chunking, embedding, and retrieval configurations.
  6. Inject retrieved context dynamically into LLM prompts.
  7. Integrate LlamaIndex with OpenAI, Hugging Face, or Cohere APIs.
  8. Apply RAG to structured (CSV) and unstructured (PDF, HTML) data.
  9. Deploy full-stack RAG apps using Streamlit or FastAPI.
  10. Troubleshoot hallucination, latency, and memory issues in RAG apps.
Course Syllabus Back to Top
Course Syllabus
 
Module 1: Introduction to RAG and Vector Databases
  • What is RAG?
  • Why LLMs Need Retrieval for Factual Accuracy
  • Overview of Vector Embeddings and Semantic Search
Module 2: Getting Started with LlamaIndex
  • Installing LlamaIndex
  • Document Loaders and Node Parsing
  • Chunking Strategies and Metadata Tagging
Module 3: Embedding and Indexing
  • Embedding Models (OpenAI, Hugging Face, Cohere)
  • Vector Index Creation
  • Persistent and In-Memory Indexing with FAISS & Chroma
Module 4: Querying and Retrieval
  • Vector Search and Similarity Scoring
  • Top-K Retrieval and Filter Conditions
  • Prompt Engineering with Retrieved Context
Module 5: Advanced Query Handling
  • Query Decomposition and Routing
  • Structured Query Retrieval (SQL, Graph Data)
  • Keyword + Vector Hybrid Search
Module 6: Integrating with LLMs
  • Connecting to OpenAI GPT, Mistral, or Anthropic APIs
  • Conversational RAG (Chat Engines + Memory)
  • Output Formatting and Response Templates
Module 7: Working with Different Data Sources
  • PDFs, Web Pages, YouTube Transcripts
  • CSVs and Pandas DataFrames
  • Real-time Data with APIs and Agents
Module 8: Building RAG-Powered Applications
  • Enterprise Knowledge Assistant
  • Legal Document Search Engine
  • Research Copilot with Dynamic Context Retrieval
Module 9: Optimization and Troubleshooting
  • Chunk Size Tuning and Embedding Quality
  • Retrieval Latency and Cost Reduction
  • Evaluating Relevance and Reducing Hallucinations
Module 10: Capstone Project
 
  • Ingest Domain-Specific Documents
  • Build and Deploy a Complete RAG Chatbot
  • Measure Accuracy and Usability
Certification Back to Top

Upon successful completion of the course, learners will receive the Uplatz Certificate of Specialization in Retrieval-Augmented Generation with LlamaIndex. This certification validates your expertise in building intelligent, data-aware AI systems using RAG techniques and vector databases. It demonstrates your ability to integrate structured and unstructured knowledge into LLM workflows, enabling trustworthy, scalable, and up-to-date generative applications. The certificate is a valuable asset for careers in AI product engineering, search infrastructure, knowledge management, and custom LLM deployment. It highlights both theoretical understanding and hands-on skills in embedding, indexing, retrieval, and generation using LlamaIndex and modern vector stores. Whether you’re deploying enterprise chatbots or research copilots, this certification strengthens your credibility as a modern AI engineer or solution architect.

Career & Jobs Back to Top
As enterprises move toward data-aware LLM applications, the demand for professionals skilled in RAG and vector databases is rapidly rising. The ability to build applications that combine AI reasoning with organization-specific knowledge is a key differentiator for developers and AI architects.
 
Graduates of this course can pursue roles such as:
  • RAG Engineer
  • Vector Database Developer
  • Knowledge Assistant Engineer
  • NLP Engineer (Enterprise Search)
  • LlamaIndex Specialist
  • Semantic Search Engineer
  • LLM Developer – Context Integration
  • Applied AI Developer (RAG systems)
These roles are found across sectors like legal tech, healthcare, e-commerce, education, finance, and research organizations—anywhere real-time, accurate, and personalized AI is needed.
 
Companies are integrating RAG into customer support bots, legal analysis tools, knowledge retrieval systems, and productivity assistants. Mastery of vector indexing, retrieval techniques, and tools like LlamaIndex and FAISS provides a powerful foundation for building AI systems that stay up-to-date and domain-specific.
 
Salaries for RAG-focused roles are highly competitive, and many companies offer remote or hybrid opportunities in this fast-evolving space. Freelancers can also productize their knowledge by building private GPTs and chatbots tailored to specific industries or document sets.
Interview Questions Back to Top
  1. What is Retrieval-Augmented Generation (RAG)?
    RAG combines LLMs with document retrieval, injecting external context into prompts to improve factuality and domain relevance.
  2. What is the role of a vector database in RAG?
    It stores document embeddings for semantic search, enabling fast and accurate context retrieval based on similarity to user queries.
  3. How does LlamaIndex simplify RAG implementation?
    It handles document loading, chunking, indexing, querying, and LLM integration in a modular and scalable way.
  4. What are embeddings and why are they important?
    Embeddings are vector representations of text that preserve semantic meaning, used to compare similarity between queries and documents.
  5. What’s the difference between FAISS and Pinecone?
    FAISS is an open-source, local vector database; Pinecone is a managed, scalable cloud solution with additional features like metadata filtering.
  6. How can RAG reduce hallucination in LLMs?
    By grounding responses in retrieved factual context, reducing reliance on model memory alone.
  7. How do you optimize chunk size and overlap in indexing?
    By testing different values to balance context completeness and retrieval precision, typically using sliding windows or heuristics.
  8. What are hybrid retrieval techniques?
    Combining keyword-based (BM25) and vector-based search to improve recall and relevance.
  9. How do you evaluate the performance of a RAG system?
    Using metrics like relevance scoring, retrieval accuracy, latency, and user feedback on factual correctness.
  10. Can RAG work with real-time data sources?
    Yes, through API connectors and agent systems that ingest, embed, and retrieve data dynamically.
Course Quiz Back to Top
Start Quiz
Q1. What are the payment options?
A1. We have multiple payment options: 1) Book your course on our webiste by clicking on Buy this course button on top right of this course page 2) Pay via Invoice using any credit or debit card 3) Pay to our UK or India bank account 4) If your HR or employer is making the payment, then we can send them an invoice to pay.

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A2. Yes, you will receive course completion certificate from Uplatz confirming that you have completed this course with Uplatz. Once you complete your learning please submit this for to request for your certificate https://training.uplatz.com/certificate-request.php

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A17. Our course is designed to provide you with a solid foundation in the subject and equip you with valuable skills. While the course is a significant step toward your career goals, its important to note that the job market can vary, and some positions might require additional certifications or experience. Remember that the job landscape is constantly evolving. We encourage you to continue learning and stay updated on industry trends even after completing the course. Many successful professionals combine formal education with ongoing self-improvement to excel in their careers. We are here to support you in your journey!

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A18. While our course is designed to provide you with a comprehensive understanding of the subject, we currently do not offer placement services as part of the course package. Our main focus is on delivering high-quality education and equipping you with essential skills in this field. However, we understand that finding job opportunities is a crucial aspect of your career journey. We recommend exploring various avenues to enhance your job search:
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b) Networking: Attend industry events, workshops, and conferences to build connections with professionals in your field. Networking can often lead to job referrals and valuable insights.
c) Online Professional Network: Leverage platforms like LinkedIn, a reputable online professional network, to explore job opportunities that resonate with your skills and interests.
d) Online Job Platforms: Investigate prominent online job platforms in your region and submit applications for suitable positions considering both your prior experience and the newly acquired knowledge. e.g in UK the major job platforms are Reed, Indeed, CV library, Total Jobs, Linkedin.
While we may not offer placement services, we are here to support you in other ways. If you have any questions about the industry, job search strategies, or interview preparation, please dont hesitate to reach out. Remember that taking an active role in your job search process can lead to valuable experiences and opportunities.

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