RAG & Vector Databases Course in Hyderabad
Build retrieval-augmented generation systems with embeddings and vector search
The RAG and Vector Databases course teaches you to build question-answering and knowledge assistants that answer from your own documents. You start with large language model basics, embeddings and similarity search, then learn the retrieval-augmented generation pipeline: loading and parsing documents, chunking, metadata, embedding, indexing, retrieval, re-ranking, prompting with context and citations. You work with vector search options such as pgvector in PostgreSQL, Qdrant, Chroma, Weaviate, Pinecone and FAISS, and compare them for filtering, hybrid search, scale and operations. Later modules cover hybrid keyword and vector retrieval, query rewriting, multi-step and agentic retrieval, evaluation of retrieval and answer quality, guardrails, access control for documents, observability, cost and latency tuning, and deployment with FastAPI. The course suits developers, data professionals and engineers with basic Python. You build several RAG applications and finish with a capstone assistant with evaluation. Classes run in Ameerpet, Hyderabad, and in live online batches.
Syllabus
LLM & RAG Fundamentals
- •How LLMs work and why they hallucinate
- •RAG versus fine-tuning versus long context
- •RAG pipeline components and design choices
- •Lab: Call an LLM API and observe limits without retrieval
- •Outcome: Decide when RAG is the right approach
Embeddings & Similarity Search
- •What embeddings are and how to choose an embedding model
- •Distance metrics: cosine, dot product and Euclidean
- •Approximate nearest neighbour search and HNSW or IVF concepts
- •Lab: Embed sentences and build a semantic search in NumPy
- •Outcome: Build and explain semantic search
Document Ingestion & Parsing
- •Loaders for PDF, Word, HTML and web pages
- •Handling tables, images and scanned pages with OCR
- •Cleaning, deduplication and metadata extraction
- •Lab: Build an ingestion pipeline for a PDF collection
- •Outcome: Turn messy documents into clean text with metadata
Chunking Strategies
- •Chunk size, overlap and their effect on retrieval
- •Recursive, semantic and structure-aware chunking
- •Parent-child and sentence-window retrieval
- •Lab: Compare three chunking strategies on the same questions
- •Outcome: Choose chunking with evidence
Vector Databases: pgvector, Qdrant, Chroma & FAISS
- •Vector database concepts: collections, payloads, indexes and filters
- •pgvector in PostgreSQL, Qdrant, Chroma and FAISS hands-on
- •Managed options such as Pinecone and Weaviate; trade-offs for scale and operations
- •Lab: Load the same data into two vector stores and compare queries
- •Outcome: Select and operate a vector store
Retrieval Techniques
- •Top-k, thresholds, maximal marginal relevance and metadata filtering
- •Hybrid retrieval: BM25 plus vectors with reciprocal rank fusion
- •Cross-encoder re-ranking and query rewriting
- •Lab: Add hybrid search and re-ranking and measure the gain
- •Outcome: Improve retrieval quality systematically
Generation: Prompting with Context & Citations
- •Context-aware prompt templates and output formats
- •Citations, quote extraction and 'I do not know' behaviour
- •Handling conflicting or insufficient context
- •Lab: Build a cited answer generator with refusal rules
- •Outcome: Produce grounded answers users can verify
Building RAG with LangChain, LlamaIndex & Plain Python
- •Building the pipeline from scratch to understand each step
- •LangChain and LlamaIndex retrievers and pipelines
- •Choosing frameworks versus custom code
- •Lab: Rebuild the same RAG app in two frameworks
- •Outcome: Use frameworks without hiding the mechanics
Advanced RAG: Agentic, Multi-Hop & Multimodal
- •Query decomposition and multi-hop retrieval
- •Agentic retrieval with tool calls and iterative search
- •GraphRAG and multimodal retrieval overview
- •Lab: Build a multi-step retriever for complex questions
- •Outcome: Handle questions that need more than one lookup
Evaluation & Testing
- •Building golden question sets
- •Retrieval metrics: recall, precision, MRR and nDCG
- •Answer quality: faithfulness, relevance and LLM-as-judge limits; tools such as RAGAS
- •Lab: Create an evaluation harness and track scores per change
- •Outcome: Measure quality instead of guessing
Security, Access Control & Guardrails
- •Per-user document access and metadata filters
- •PII handling, prompt injection through documents and output filters
- •Audit logs and data retention
- •Lab: Enforce role-based access in retrieval and test leakage
- •Outcome: Prevent leaks of restricted content
Deployment, Observability & Capstone
- •Serving RAG with FastAPI and Docker
- •Tracing, logging, caching, latency and token cost control
- •Capstone: production-style RAG assistant with evaluation and access control
- •Portfolio write-up and interview practice
- •Outcome: A deployed, evaluated RAG application
Tools Covered
Projects Included
- Semantic Search Engine: Build embeddings and search over a document set with metadata filters; deliverable is a search app and relevance examples.
- Policy Q&A Assistant: Create a cited question-answering assistant over PDFs; deliverable is a web demo and a ten-question test sheet.
- Hybrid Retrieval Upgrade: Add BM25, vector fusion and re-ranking and measure the change; deliverable is a before-and-after evaluation report.
- Role-Based Knowledge Assistant: Enforce per-role document access in retrieval; deliverable is a tested assistant with a leakage test log.
- Capstone Production RAG: Deploy a RAG API with evaluation and monitoring; deliverable is a GitHub repository, architecture diagram and cost estimate.
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Call: +91 9908099980Prerequisites & Eligibility
Understand the foundation and skills needed to succeed in the RAG & Vector Databases course.
Course Prerequisites
- Any graduate is eligible for enrollment across all program tracks
- Dedication to work on real-time scenario projects and live client project implementations
- Willingness to engage in aptitude, soft skills and interview readiness sessions
- Commitment to complete full-day training or intensive/internship program schedules
Why Choose GloryTecks
Build practical capabilities through structured learning, expert mentors and hands-on projects.
5+ Years of Excellence
Established training institute in Hyderabad with a proven track record of producing industry-ready professionals.
Expert Faculty
Learn from trainers with real-world industry experience, who teach the way the work is actually done.
Placement Assistance
A dedicated placement team with tie-ups across hiring companies. We support you until you get hired.
Comprehensive Curriculum
Updated syllabus covering the latest technologies and industry best practices, with hands-on projects.
Career Growth Focus
Not just training, but complete career transformation with soft skills and interview preparation.
Online & Classroom
Attend live online batches or classroom training at our Ameerpet centre, whichever suits you.
Flexible Batches
Weekday, weekend and online batch timings to suit working professionals, students and freshers.
Real-time Projects
Work on real-time scenario projects so you finish with practical experience to show employers.
We provide placement support with direct access to hiring companies, along with comprehensive training that helps freshers become job-ready professionals.
Certification
Explore certification and practical learning opportunities with a focus on RAG & Vector Databases.
Certificate of Completion
RAG & Vector Databases
This certifies that
[Your Name]
has successfully completed the
RAG & Vector Databases
Training Program
GloryTecks
Ameerpet, Hyderabad
Certificate ID
GT-YYYY-XXXX
Certificate Includes
- Course completion certificate from GloryTecks
- Project completion certificates
- Internship completion certificate for eligible internship programs
- Digital certificate with a unique verification ID
- Shareable on LinkedIn and other professional platforms
- Valid proof of skill acquisition for employers
- Includes the detailed syllabus covered
Industry Focus
A certificate built around the tools and skills employers ask for, adding credibility to your resume.
Skill Validation
Proves your expertise in the course and the technologies you worked with.
Career Advancement
Strengthens your profile when you apply for jobs and negotiate your salary.
Digital & Physical
Get a digital certificate for online sharing and a physical copy for framing.
Course Certificate
Upon successful completion of the training program
Project Certificate
For major projects completed during training
Course Features & Highlights
Build a strong foundation through a structured RAG & Vector Databases course with practical learning and hands-on exposure.
Live Projects
Work on real-world projects from day one with industry use cases.
Expert Trainers
Learn from industry professionals with real-world experience.
Placement Support
Dedicated placement team with assistance until you get hired.
Industry Certification
Get certified and boost your resume with recognized credentials.
LMS Access
Access to learning materials and recorded sessions.
Mock Interviews
Weekly mock interviews to prepare you for real job scenarios.
Resume Building
Professional resume preparation and LinkedIn profile optimization.
Additional Benefits
- Flexible Payment Plans
- 6-12 Months LMS Access
- Doubt Clearing Sessions
- 24/7 Learning Support
Frequently asked questions
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Best RAG & Vector Databases Training in
Ameerpet, Hyderabad
Located at Ameerpet — Hyderabad’s IT training hub. Easily accessible from all areas of Hyderabad. Just 2 minutes walk from Ameerpet Metro Station.
Placement Support
Career support covers resume and LinkedIn preparation, mock interviews and a portfolio of 5 course projects. Roles that use these skills include Generative AI Developer, AI Engineer, Search or Retrieval Engineer, NLP Engineer; support is guidance with the job search, not a guarantee of a job.