AI Engineer & Architect. I designed the personalization architecture for the world's largest jewelry company and automated claims handling for the leading insurance company in the Nordics
In this tutorial, we'll explore how to generate insights from BigQuery using Llama 3 and Langchain. The focus will be on handling errors gracefully and feeding them back into the chain for iterative improvement.
This tutorial will explore building dashboards using Large Language Models (LLMs) and LangChain. We will use LangChain chains to extract insights from BigQuery by first generating SQL and then pushing the generated SQL to BigQuery as views, forming the basis for interactive dashboards.
In this tutorial, we'll explore how to build knowledge bases for advanced SQL generation using LangChain. We'll leverage LangChain chains to extract insights from BigQuery by generating and executing SQL queries. Redis will store the SQL query examples needed for few-shot-prompting.
In this tutorial, we'll connect LLMs to Google Analytics 4 data using LangChain. This allows us to build chat interfaces to marketing analytics data, speeding up the process of generating actionable insights.
In this tutorial, we'll dive into modeling Customer Lifetime Value (CLV) in a way that helps ecommerce businesses spot their most profitable customer segments. This helps businesses refine strategies around customer acquisition and growth.
This guide helps data analysts, data scientists, and developers leverage LLMs for generating SQL queries from natural language questions, making complex data wrangling in BigQuery and other SQL databases more accessible and intuitive.
