RAG Engineering
Build Accurate, Data-Aware
AI with RAG Pipelines
Enhance your LLM's accuracy with custom Retrieval-Augmented Generation pipelines. Building with Pinecone, ChromaDB, and LangChain.
Get a RAG AuditCore Capabilities
Vector Database Setup
Design and deploy optimized vector stores using Pinecone, ChromaDB, or MongoDB Atlas Vector Search for lightning-fast semantic retrieval.
Document Ingestion Systems
Build robust data pipelines that parse, chunk, embed, and index PDFs, docs, web pages, and databases for comprehensive knowledge retrieval.
Reducing LLM Hallucinations
Implement citation tracking, confidence scoring, and retrieval validation to ensure your AI provides accurate, source-backed answers.
Hybrid Search Architecture
Combine dense vector search with sparse keyword search (BM25) for maximum retrieval accuracy across diverse query types.
Tech Stack
Frequently Asked
What is RAG in AI?
Retrieval-Augmented Generation (RAG) is a technique that enhances LLMs by retrieving relevant documents from a knowledge base before generating responses, dramatically improving accuracy and reducing hallucinations.
Why use RAG over fine-tuning?
RAG is ideal when your data changes frequently, as it doesn't require retraining. Fine-tuning is better for style/behavior changes. Often, a hybrid approach delivers the best results.
Which vector databases do you use?
I work with Pinecone, ChromaDB, Weaviate, Qdrant, and MongoDB Atlas Vector Search depending on your scale, budget, and infrastructure requirements.
Can RAG work with private/internal documents?
Absolutely. I build secure RAG systems that process internal PDFs, docs, spreadsheets, and databases while keeping your data private and compliant.
Need Accurate
AI Answers?
Let's build a RAG pipeline that makes your AI truly intelligent.
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