As a software engineer, you will have to set up your computer many times. This article will show you how to make this easier.
Tech interviews typically test DSA and systems design. It's time to include using AI.
When Ruby on Rails was released, it was a huge step forward for the web development community. It preferred convention over configuration and made it easy to get started and to iterate on a large project. We need that for the LLM and AI engineering ecosystem.
gpt-pilot, like so many other AI code generation tools, starts off strong but fails soon after.
LangChain is a great framework for working with LLM's, until you need to do something that it doesn't support.
Part 3 covers how LangChain enables parsing responses into data types.
Part 2 covers how LangChain makes working with models and switching between them easy.
Part 1 covers how LangChain makes working with templates and prompts easy.
paul-gauthier's aider is one of the best AI coding assistants, up there with GitHub Copilot. However, for AI pair programming, it's the best.
ChatGPT's Custom Instructions feature allows you to consistently customize your responses without using any context tokens. This article covers how I use this feature via my own unique personas.
Meta prompts enable the user to create great prompts. Let's break one down.
roadmap.sh has learning paths for everything related to software development. I went through their Prompt Engineering Roadmap and wanted to share my thoughts.