AI Prompt Engineering · Lesson

Removing Ambiguity from Prompts

Techniques to eliminate multiple interpretations in your instructions.

What Ambiguity Does to AI

Ambiguous prompts force the model to choose between multiple valid interpretations. It picks one — usually the most common — and runs with it confidently, with no indication that it made a choice.

The result: you get a perfectly well-formed answer to the wrong question. Recognizing and eliminating ambiguity before sending is faster than rewriting the output afterward.

The Classic: 'Make It Better'

'Make it better' is perhaps the most ambiguous prompt in existence. Better how?

Shorter? Longer?
More formal? More casual?
More examples? Fewer?
Different tone? Different structure?
Fixed grammar? Different vocabulary?

The model picks one dimension and changes it. If that is not the dimension you wanted, you are stuck in a rewrite loop.

import anthropic

client = anthropic.Anthropic(api_key='sk-ant-your-key-here')

original_text = (
    'Our software helps companies manage their data. '
    'It has many features. Customers like it a lot.'
)

# Ambiguous improvement request
vague = f'Make this better:\n\n{original_text}'

# Unambiguous improvement request
specific = (
    f'Rewrite this product description to be exactly 50% shorter, '
    f'more confident in tone, and replace vague phrases like "many features" '
    f'and "a lot" with specific claims. Do not add new features I have not mentioned.\n\n'
    f'{original_text}'
)

for label, prompt in [('VAGUE', vague), ('SPECIFIC', specific)]:
    response = client.messages.create(
        model='claude-opus-4-5',
        max_tokens=200,
        messages=[{'role': 'user', 'content': prompt}]
    )
    print(f'--- {label} ---')
    print(response.content[0].text)
    print()

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