Dataset preparation, evaluation, and RAG comparison

Overview

• Dataset preparation converts representative examples into the selected training format with reviewed expected outputs.
• Evaluation compares trained behavior against the baseline on separate data.
• RAG supplies current external knowledge at runtime; fine-tuning teaches recurring behavior.

Why this matters

A trained model can learn bad labels, memorize test examples, or improve easy cases while damaging difficult ones.

An everyday example

An employee trained on incorrect examples repeats incorrect procedures with greater consistency.

A technical example

Split by customer/document family and time where appropriate, so near-duplicate narratives do not leak across train and test sets.

How it works

1. Deduplicate
2. Redact/minimize
3. Label/review
4. Split
5. Train
6. Measure task accuracy, safety, abstention, latency, and cost
7. Review error slices.

Architecture and flow

Governed dataset → separated training/evaluation → comparison report → release gate.

The insurance assistant example

Train format/category behavior; retrieve the current policy and use claim tools for customer-specific facts.

When to use it

When a model update is supported by a stable dataset and reliable reference outcomes.

When to avoid it

Avoid training on synthetic outputs without review or judging success only by training loss.

Interview answer

“I fine-tune behavior and use RAG for knowledge. They can work together, but neither replaces clean sources, secure tools, or held-out evaluation.”

Benefits, trade-offs, and production considerations

• Maintain dataset provenance, rights, deletion policy, label quality, and contamination checks.
• Roll back if a trained model loses important safety or instruction-following behavior despite improved average task scores.

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