RAG-Based Systems by Dhananjay Garg

RAG-Based Systems by Dhananjay GargRAG-Based Systems by Dhananjay Garg

RAG-Based Systems

Dhananjay Garg

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About this service

Summary

Our Retrieval-Augmented Generation (RAG) systems combine the power of large language models with proprietary knowledge bases to deliver accurate, contextually relevant, and factual responses. We design custom RAG architectures that connect to your data sources, ensuring your AI systems provide reliable information while maintaining the natural conversational abilities of modern language models.

What's included

Custom RAG Architecture
End-to-end design of a Retrieval-Augmented Generation system tailored to your business use case, with schema diagrams and flow documentation.
Knowledge Base Integration
Connection to your internal documents, databases, or APIs using vector stores (like Chroma, Pinecone, or FAISS) with embedding pipelines.
Semantic Search & Optimization
Implementation of high-accuracy semantic retrieval with ranking strategies, filters, and hybrid retrieval (dense + keyword).
LLM + Retriever Pipeline
A working pipeline that combines your retriever with a large language model (e.g., GPT-4, Claude, Llama) with prompt templates, fallback handling, and fact-checking logic.
Deployment & Monitoring Toolkit
Dockerized deployment + REST API interface, with observability dashboards for query tracking, latency, and hallucination monitoring.

Skills and tools

AI Automation

AI Chatbot Developer

AI Developer