2022 • ENTERPRISE RAG

LLMs & Retrieval-Augmented Generation (RAG)

Pioneering early enterprise RAG architectures connecting internal document vector stores with foundation models for context-grounded Q&A.

Engineer: Rohit • Milestone Era: 2022

Era Context & Historical Background

As Large Language Models emerged, Rohit pioneered Retrieval-Augmented Generation (RAG) frameworks to ground LLM responses directly in validated corporate document repositories.

RAG ArchitecturePinecone / QdrantOpenAI APISemantic ChunkingPrompt Engineering

Key Technical Breakthroughs & Architecture

Context-Grounded Document Search

Built end-to-end RAG pipelines connecting enterprise vector databases directly to LLM prompt context windows.

Semantic Chunking Strategies

Designed layout-aware document chunking algorithms preserving header hierarchy and tabular data integrity.

Zero Hallucination Guardrails

Enforced strict source attribution rules requiring LLMs to cite exact document chunk IDs in generated responses.

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Author & Architect

Rohit - AI Consultant

Rohit

AI & Data Science Consultant

2+ Decades AI Experience

First project in AI & ANN in 2004 at IIT Roorkee under the mentorship of Dr. Sunil Padhi (HOD, Electrical Department), writing neural network backpropagation in C language to predict solar sunspots. Today designing stateful Agentic AI networks at rcode.in.

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