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AI Prompt Engineering · Lesson

Evaluating the Decision

Measuring quality and cost.

You Cannot Decide What You Cannot Measure

The prompt-vs-tune-vs-hybrid choice is only as good as the evaluation behind it. Without a frozen eval set and a cost model, every comparison is anecdote.

  • Quality and cost are two axes - never collapse them into one number prematurely
  • The eval set must be held out and stable across every approach you compare
  • The winner is the approach on the best point of the quality-cost frontier for your constraints

Build the Frozen Eval Set First

Before comparing anything, construct a held-out eval set that no approach trains on. It must cover the real distribution: common cases, known edge cases, and adversarial inputs in roughly production proportions.

Freeze it. Every approach - prompt-only, tuned, hybrid - is scored on the identical set. If the eval shifts between comparisons, the numbers are not comparable and the decision is invalid.

def split_eval(labeled, holdout_ratio=0.2, seed=42):
    import random
    rng = random.Random(seed)        # fixed seed = reproducible split
    data = labeled[:]
    rng.shuffle(data)
    cut = int(len(data) * (1 - holdout_ratio))
    train, frozen_eval = data[:cut], data[cut:]
    return train, frozen_eval        # eval never enters any training run

All lessons in this course

  1. When Prompting Is Enough
  2. When to Fine-Tune
  3. Hybrid: Prompt + Light Tuning
  4. Evaluating the Decision
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