Syed Muhammad Hussain

Syed Muhammad Hussain

Machine Learning Engineer at Beam AI specializing in LLMs, RAG systems, and AI automation.

Intelligent Enterprise Search with RAG

RAG Search System

Intelligent Enterprise Search with RAG

Application: AI-driven knowledge discovery for enterprises, enabling instant access to critical information.

Overview

Developed a high-performance Retrieval-Augmented Generation (RAG) pipeline that revolutionizes how enterprises search and retrieve information from their knowledge bases. This system combines semantic understanding with generative AI to provide accurate, contextual responses to complex queries.

Key Features & Technical Implementation

  • Multi-Stage Indexing System: Engineered a sophisticated indexing system with semantic chunking and vectorized retrieval using Qdrant & PGVector
  • LLM Integration: Integrated LangChain, LlamaIndex, and multiple LLMs (OpenAI, Anthropic, Mistral) to enhance domain-specific information retrieval
  • Advanced Prompt Engineering: Applied Chain-of-Thought (CoT) and Few-Shot Learning techniques, improving search accuracy by 40%
  • Cloud Deployment: Deployed on Azure for scalable, real-time document retrieval with minimal latency

Impact

  • Improved search accuracy by 40% through advanced prompt engineering
  • Reduced information retrieval time from minutes to seconds
  • Enabled real-time contextual generation combining external knowledge bases with generative reasoning

Technologies Used

  • Frameworks: LangChain, LlamaIndex
  • Vector Databases: Qdrant, PGVector
  • LLMs: OpenAI GPT-4, Anthropic Claude, Mistral
  • Cloud: Azure AI Services
  • Languages: Python