Building Production-Grade Agentic RAG: A Technical Deep Dive – Part 1
Beyond Fixed Windows — Agentic & ML-Based Chunking Introduction: The RAG Gap The promise of Retrieval-Augmented Generation (RAG) is compelling: ground large... Read more.
Modernizing Data Warehouses for AI: A 4-Step Roadmap
It’s the same conversation in every boardroom and Slack channel: “How are we using LLMs? Where are our AI agents? When do we get our Copilot?” But... Read more.
How Poor Data Engineering Corrodes GenAI Pipelines
Generative AI (GenAI) has captivated the world with its ability to create, synthesize, and reason. From crafting compelling marketing copy to assisting in scientific... Read more.
Designing Production-Grade GenAI Automation
A dbt Ops Agent Case Study A small, well-instrumented workflow can turn dbt failures into reviewable Git changes by combining deterministic parsing, constrained... Read more.
The Data Engineer Role in a ML Pipeline
Data engineers provide the critical foundation for every successful Machine Learning (ML) deployment, supporting the powerful models and insights that often grab... Read more.
AI Agent Workflows: Pydantic AI
Building Intelligent Multi-Agent Systems with Pydantic AI In the rapidly evolving landscape of artificial intelligence, multi-agent systems have emerged as a powerful... Read more.
The Ultimate Vector Database Showdown: A Performance and Cost Deep Dive on AWS
In the age of AI, Retrieval-Augmented Generation (RAG) is king. The engine powering this revolution? The vector database. Choosing the right one is critical for... Read more.
Data Engineer – The Top 10 Books to read in 2023
Whether you are just starting out as a data engineer or you are an old pro it is always important to stay up to date on trends and technologies. In this post I will... Read more.