Everyone wants generative AI and RAG, but very few companies have data that is ready for it. Here is how you prepare your data infrastructure.
The Garbage In, Garbage Out Problem
LLMs are incredibly powerful, but if you feed them unstructured, messy, or contradictory data, they will generate confident hallucinations.
Step 1: Unifying Siloed Data
Before AI can reason over your data, you need to bring it together. Modern ETL pipelines (using tools like dbt and Airflow) are essential for creating a single source of truth.
Step 2: Cleaning and Structuring
AI models need context. This means resolving duplicate records, standardizing formats, and ensuring metadata is accurate before vectorization begins.
Investing in data engineering is non-negotiable for AI success. Clean, unified data is the true moat for any modern enterprise.
Pankaj Kumar Malhi
Founder & Lead AI Architect
Pankaj is an AI systems engineer specializing in secure Retrieval-Augmented Generation (RAG) vector pipelines, multi-tenant cloud gateways, and fast Next.js SaaS platforms.
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