Here’s the latest installment in the series on working with LLMS: Creating a GPT Assistant That Writes Pipeline Tests.
Once you get the hang of writing these tests, it’s mostly boilerplate, so I figured my team of assistants could help. I recruited Cody, GitHub Copilot, and Unblocked — with varying degrees of success. Then I realized I hadn’t yet tried creating a GPT. As OpenAI describes them, “GPTs are custom versions of ChatGPT that users can tailor for specific tasks or topics by combining instructions, knowledge, and capabilities.”
The rest of the series:
1 When the rubber duck talks back
2 Radical just-in-time learning
3 Why LLM-assisted table transformation is a big deal
4 Using LLM-Assisted Coding to Write a Custom Template Function
5 Elevating the Conversation with LLM Assistants
6 How Large Language Models Assisted a Website Makeover
7 Should LLMs Write Marketing Copy?
8 Test-Driven Development with LLMs: Never Trust, Always Verify
9 Learning While Coding: How LLMs Teach You Implicitly
10 How LLMs Helped Me Build an ODBC Plugin for Steampipe
11 How to Use LLMs for Dynamic Documentation
12 Let’s talk: conversational software development
13 Using LLMs to Improve SQL Queries
14 Puzzling over the Postgres Query Planner with LLMs
15 7 Guiding Principles for Working with LLMs
