Extracting insights from customer data can be a time-consuming process, especially for those who lack advanced SQL skills. Many companies struggle to gain a clear understanding of important acquisition and retention metrics such as repeat revenue, customer lifetime value (CLV), churn rates, or product bundling opportunities. Such a knowledge gap can impede business growth.

Fortunately, large language models (LLMs) have fundamentally changed how we approach data analysis. By leveraging LLMs, we can extract meaningful information from databases with remarkable efficiency, effectively reducing the time required by a factor of ten. This opens new business possibilities to uncover actionable insights and make informed strategic decisions quickly.

In this tutorial, I will explore how to harness the capabilities of LLMs and LangChain to build interactive dashboards that provide a comprehensive view of your business data. I will use LangChain chains to extract insights from BigQuery and build the dashboards in Looker Studio. The process involves generating SQL queries using LLMs and then pushing these queries to BigQuery as views. While I will use BigQuery and Looker Studio as example platforms, the concepts and techniques covered in this tutorial can be easily extended to other data warehouses and dashboard tools.

In this tutorial, you will learn to:

  1. Set up the necessary environment and dependencies
  2. Fetch BigQuery schemas to understand the structure of your data
  3. Connect to BigQuery using the appropriate credentials
  4. Configure and utilize LLMs, such as Claude-3, to generate SQL queries
  5. Create BigQuery views based on the generated SQL queries
  6. Load and process insights from a file to automate view creation
  7. Generate meaningful view names using LLMs
  8. Handle query execution failures and retries
By the end of this tutorial, you will have a solid foundation for building dashboards using LLMs and LangChain, enabling you to unlock valuable insights from your business data efficiently.

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