Setting Up Your AI Workspace

Choosing Your Core Tools

A minimal AI workspace needs three things: a place to run and version prompts, a place to call model APIs, and a place to store the outputs you'll evaluate against. You don't need a large stack to start — a single project folder with clear separation between these three concerns is enough.

Organizing Credentials and Access

Keep API keys out of your workflow files entirely. Store them in environment variables and reference them by name, so a workflow can move between local, staging, and production without ever touching the secret itself.

Structuring Your Workspace

A workspace that scales usually looks like this:

• prompts/ — versioned prompt templates, one file per task
• pipelines/ — the orchestration logic that chains calls together
• evaluations/ — test datasets and scoring scripts
• outputs/ — logged runs, kept for comparison across iterations

Setting a Baseline

Before building anything complex, run your simplest possible workflow end to end and record the result. This baseline is what every later optimization gets measured against — without it, you can't tell whether a change actually helped.

Practical Review Checklist

Before moving on, confirm that you can:

• State the primary objective in one clear sentence
• Identify the assumptions that still need evidence
• Choose a small set of meaningful success measures
• Explain the process to another person without hidden steps
• Record what should be reviewed during the next iteration

Conclusion

A well-structured workspace is what makes iteration fast later. Time spent here upfront is what prevents rework once workflows start getting complex.