Starting AI/ML Research? Follow These 7 Rules

Strong research connects one measurable problem to suitable data, a fair baseline, controlled experiments and honest analysis.

1. Start With the Problem

Name the application, user, unresolved limitation and measurable outcome before choosing an algorithm. A field like computer vision is not yet a research question.

2. Prove the Gap With Literature

Compare recent papers by data, method, baseline, metrics, findings and limits. Treat repeated weaknesses as leads—not automatic proof of novelty.

3. Audit the Dataset First

Check size, balance, duplicates, labels, representativeness, licensing and leakage risk. A complex model cannot rescue unsuitable evidence.

4. Establish a Fair Baseline

Start with a simple relevant method, then compare stronger approaches under the same data split, preprocessing and evaluation conditions.

5. Keep the Test Set Sealed

Split before learned preprocessing. Use training data to fit, validation data to choose, and the test set only for the final unbiased evaluation.

6. Measure the Error That Matters

Accuracy alone may hide minority-class failures. Choose precision, recall, F1, AUC, latency or cost according to the real decision and constraint.

7. Make Every Result Reproducible

Record dataset version, splits, preprocessing, code, seeds, hyperparameters, library versions and hardware. Then analyze failures and limitations.

Research Is a Chain of Evidence

Problem to gap. Gap to experiment. Experiment to result. Result to defensible contribution. Keep every link visible and testable.