AI Research Workflow & Daily Practice
Behind every great AI model is a disciplined researcher — someone who experiments methodically, reflects daily, and builds systems of thought as carefully as systems of code. These twenty-four habits describe how professional AI researchers work day-to-day.
Start with a Research Question
Every day begins with a clear question: What am I trying to understand or improve? It anchors all experiments.
Plan Experiments Before Coding
Sketch hypotheses, variables, and expected outcomes before touching the keyboard; this saves days of debugging later.
Use Checklists
Run through standardized checklists before training (data format, seeds, paths, GPU availability) to prevent simple mistakes.
Name Everything Consistently
Models, runs, datasets, and configs follow consistent naming patterns. Order creates reproducibility.
Log Every Run
Keep a log of parameters, commit hash, date, metrics, and observations for each experiment. The logbook is the lab.
Track Metrics Visually
Use dashboards (W&B, TensorBoard) to spot trends early; humans are pattern detectors — use your eyes.
Save Intermediate Checkpoints
Never trust a single successful run; store snapshots so you can roll back, compare, or restart easily.
Automate Environment Setup
Containerize everything (Docker, Conda, uv, or venv). Your environment should rebuild from one command.
Version Control Everything
Code, data, configs, and notebooks live under Git. Research without version control is guesswork.
Document As You Go
Add concise comments, README notes, and markdown logs during the workday — not afterward.
Read One Paper Daily
Maintain intellectual freshness; one well-understood paper a day compounds faster than sporadic bursts.
Summarize Papers in Your Own Words
After reading, write a short abstract in plain language; understanding means rephrasing.
Replicate Results Periodically
Choose one external paper a month and replicate its key result. It keeps your fundamentals sharp.
Balance Exploration and Exploitation
Allocate time between trying new ideas and improving known ones. Innovation needs both.
Use Seeded Randomness
Always fix random seeds for reproducibility; uncontrolled randomness ruins experiments.
Monitor Compute Usage
Track GPU hours, memory, and disk; efficiency is an intellectual discipline, not just a budget constraint.
Debug Systematically
When something breaks, change one variable at a time. Guessing wastes compute; reasoning saves it.
Maintain a “Research Scratchpad”
Keep an open text file or notebook for stray ideas, observations, and potential follow-ups.
Reflect Weekly
Review what worked, what failed, and what patterns are emerging. Write it down; reflection turns noise into insight.
Back Up Everything
Automate backups to remote storage; nothing derails a project like losing irreplaceable results.
Collaborate Generously
Share partial results and code early; peers will spot blind spots you can’t.
Teach and Present
Explaining your work forces clarity. Every researcher should prepare internal mini-talks or notes weekly.
Rest and Reset
Step away regularly. Most breakthroughs come after rest, not after exhaustion.
End Each Day with Tomorrow’s Plan
Write down what to test, fix, or read next. Momentum is the most powerful research tool.