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Enterprise RAGVector DBProduction AI
RAG Unlocked: Production-Grade Search & Answer Systems
Transition from simple "Hello World" tutorials to building scalable, enterprise-ready RAG pipelines.
Enterprise AI is moving past chatty bots into Knowledge Management. Developers are no longer asking "how do I use an API?" but "how do I make this work with 10,000 internal documents safely?" This course answers the latter.
What You Will Master
Vector Database Implementation (Pinecone/Milvus)
Embedding Model Selection & Optimization
Production RAG Pipeline Orchestration
RAG vs. Fine-tuning Trade-offs
Hybrid Search & Reranking Techniques
Advanced Chunking Strategies (Semantic & Recursive)
Metadata Filtering for Precise Retrieval
Query Expansion with LLMs
Evaluation Frameworks (RAGAS / TruLens)
Production Deployment with FastAPI
Curriculum Overview
Project: The \"Enterprise-Ready\" RAG Engine
Students will deploy a fully functional RAG system that includes a FastAPI backend, hybrid search pipeline, and automated evaluation scripts.
FastAPI Backend
A production-ready API connected to a vector database.
Hybrid Pipeline
Semantic search combined with keyword filtering and re-ranking.
Evaluation Script
Automated framework to measure search accuracy and relevancy.
Frequently Asked Questions
₹15,000
Full Lifetime Access
Professional Certification
Production Ready GitHub Templates
Exclusive Discord Community
2024-2025 Tech Stack (Pinecone/Milvus)
Instructor
Celoris
Celoris Team
Pioneering AI Architectures
Expert engineering team specializing in Retrieval-Augmented Generation and enterprise-grade LLM applications. We help developers bridge the gap from prototype to production.
4.9(850+)
10 hours Content
Prerequisites
- Basic proficiency in Python
- Understanding of LLM basics (OpenAI/Claude APIs)
- Familiarity with Vector Databases is a plus but not required
- Desire to build production-grade AI systems