Development Environment
What is a development environment?
A development environment is a separate workspace where you can experiment, test, and iterate on your AI product without affecting the production system. It allows you to run evals, make changes to prompts or models, and validate improvements locally before pushing those changes to production, ensuring you can't accidentally break anything while experimenting.
This isolation is critical for rapid experimentation and safe iteration.
Why do product teams need separate development environments?
Development environments enable you to run multiple experiments in a single day. You can make a prompt change and run your evals, adjust temperature settings and run evals again, modify your chunking strategy and test it—all without waiting for engineers or risking your live product.
When you have your own development environment, you can execute all your evals and get scores to inform your experiments locally. Only when you find an improvement that's validated by your evals do you push it to production. This workflow keeps production stable while allowing fast-paced learning.
What tools make up a development environment?
Common tools for AI product development environments include Jupyter notebooks and Python. You install and configure the necessary programming languages, tools, and applications on your computer so you can write code, run tests, and analyze results.
The setup process has become more accessible—you can use AI assistants to walk you through configuring your development environment even without prior programming experience. This democratizes AI product development beyond traditional engineering roles.
Learn more:
- Q&A Session: Building AI Evals for the Interview Coach
- 21 Ways to Use AI at Work (And Build Your AI Product Toolbox)
- Behind the Scenes: Building the Product Talk Interview Coach
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