Por que a especificidade é importante

O problema da falta de especificidade

Prompt vago: escreva sobre cães

import anthropic

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

vague = 'Write something about dogs.'
specific = (
    'Write a 200-word Instagram caption for a golden retriever puppy\'s '
    'first day at the beach. Tone: playful and emotional. '
    'End with 5 relevant hashtags.'
)

for label, prompt in [('VAGUE', vague), ('SPECIFIC', specific)]:
    response = client.messages.create(
        model='claude-opus-4-5',
        max_tokens=300,
        messages=[{'role': 'user', 'content': prompt}]
    )
    print(f'--- {label} ---')
    print(response.content[0].text)
    print()

Por que ocorre uma saída genérica

import openai

client = openai.OpenAI(api_key='sk-your-key-here')

# Default (vague) output
default_response = client.chat.completions.create(
    model='gpt-4o',
    messages=[{'role': 'user', 'content': 'Write about coffee.'}]
)
print('DEFAULT OUTPUT (first 200 chars):')
print(default_response.choices[0].message.content[:200])
print()

# Constrained output
constrained_response = client.chat.completions.create(
    model='gpt-4o',
    messages=[{
        'role': 'user',
        'content': (
            'Write a 100-word product description for a single-origin Ethiopian pour-over coffee. '
            'Target audience: specialty coffee enthusiasts. '
            'Tone: sophisticated, sensory. Mention flavor notes: jasmine, blueberry, dark chocolate.'
        )
    }]
)
print('CONSTRAINED OUTPUT:')
print(constrained_response.choices[0].message.content)

As cinco dimensões da especificidade

import anthropic

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

# All 5 dimensions specified
response = client.messages.create(
    model='claude-opus-4-5',
    max_tokens=256,
    messages=[{
        'role': 'user',
        'content': (
            'Format: 3-bullet summary.\n'
            'Length: each bullet max 20 words.\n'
            'Audience: busy startup founders with no ML background.\n'
            'Tone: direct, no jargon.\n'
            'Goal: explain why fine-tuning an LLM is expensive.'
        )
    }]
)
print(response.content[0].text)

Vago versus específico: solicitações por e-mail

import openai

client = openai.OpenAI(api_key='sk-your-key-here')

specific_prompt = (
    'Write a 3-paragraph professional email to a B2B SaaS client (TechFlow Inc) '
    'who has a payment 14 days overdue. '
    'Tone: firm but courteous — preserve the business relationship. '
    'Include: acknowledgment of potential oversight, the overdue amount placeholder [AMOUNT], '
    'a payment link placeholder [LINK], and a 5-day deadline. '
    'Subject line included.'
)

response = client.chat.completions.create(
    model='gpt-4o',
    messages=[{'role': 'user', 'content': specific_prompt}]
)
print(response.choices[0].message.content)

Vago versus específico: resumo

import anthropic

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

article = (
    'Researchers at MIT have developed a new neural network architecture that achieves '
    '94% accuracy on medical image diagnosis, outperforming human radiologists by 7%. '
    'The model was trained on 2.4 million anonymized X-ray images and uses a novel '
    'attention mechanism that highlights regions of interest for clinician review. '
    'The team expects FDA clearance by Q3 2025 for use as a diagnostic aid, not replacement.'
)

specific_prompt = (
    f'Summarize the following article in exactly 3 sentences:\n'
    f'Sentence 1: main finding.\n'
    f'Sentence 2: methodology.\n'
    f'Sentence 3: key implication for clinicians.\n\n'
    f'Article:\n{article}'
)

response = client.messages.create(
    model='claude-opus-4-5',
    max_tokens=200,
    messages=[{'role': 'user', 'content': specific_prompt}]
)
print(response.content[0].text)

Vago versus específico: geração de ideias

import openai

client = openai.OpenAI(api_key='sk-your-key-here')

specific_brainstorm = (
    'Generate 10 unique monetization strategies for a B2C meditation app '
    'targeting 25-35 year-old urban professionals. '
    'Exclude: subscription models (already implemented). '
    'Each strategy in one sentence. '
    'Order from most to least conventional. '
    'Label each with: CONVENTIONAL / EXPERIMENTAL / BOLD.'
)

response = client.chat.completions.create(
    model='gpt-4o',
    messages=[{'role': 'user', 'content': specific_brainstorm}]
)
print(response.choices[0].message.content)

Usar exemplos nas instruções

import anthropic

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

example_prompt = '''
Generate 3 product taglines in this style:

Example 1: Notion — 'The all-in-one workspace where better thinking happens.'
Example 2: Figma — 'Design together. Ship faster.'
Example 3: Linear — 'The issue tracker you'll actually enjoy using.'

Product: A CLI tool that automatically writes Git commit messages by analyzing your diff.
Taglines:
'''

response = client.messages.create(
    model='claude-opus-4-5',
    max_tokens=200,
    messages=[{'role': 'user', 'content': example_prompt}]
)
print(response.content[0].text)

Especificar o que NOT fazer

import openai

client = openai.OpenAI(api_key='sk-your-key-here')

response = client.chat.completions.create(
    model='gpt-4o',
    messages=[
        {
            'role': 'system',
            'content': (
                'You are a copywriter. Rules:\n'
                '- NEVER start a sentence with "Additionally" or "Furthermore"\n'
                '- NEVER use the phrase "In conclusion"\n'
                '- NEVER use passive voice\n'
                '- NEVER use bullet points or lists'
            )
        },
        {
            'role': 'user',
            'content': 'Write a 100-word about us section for a craft bakery called Morning Light.'
        }
    ]
)
print(response.choices[0].message.content)

Especificidade iterativa

import anthropic

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

# Iteration 1: first attempt
v1 = 'Write a LinkedIn post about my new job.'

# Iteration 2: add specifics based on what was missing
v2 = (
    'Write a LinkedIn post (max 150 words) announcing I just joined Stripe as a Senior Engineer. '
    'Tone: genuine excitement, not bragging. '
    'Include: what drew me to the role, one thing I plan to focus on. '
    'No cliches like "excited to announce" or "humbled to share". '
    'End with a genuine question for my network.'
)

response = client.messages.create(
    model='claude-opus-4-5',
    max_tokens=300,
    messages=[{'role': 'user', 'content': v2}]
)
print(response.content[0].text)

Criar uma lista de verificação de especificidade

# Prompt template with all specificity dimensions filled
prompt_template = '''
Task: {task_description}
Format: {format}
Length: {length}
Audience: {audience}
Tone: {tone}
Goal: {goal}
Do NOT: {constraints}
Example of good output: {example}
'''

filled = prompt_template.format(
    task_description='Explain what an API is',
    format='3 short paragraphs, plain prose — no bullet points',
    length='150 words maximum',
    audience='Non-technical business stakeholders',
    tone='Friendly, analogy-based, jargon-free',
    goal='Prepare them for a meeting with the engineering team',
    constraints='Do not use the words "endpoint", "REST", or "HTTP"',
    example='An API is like a waiter in a restaurant — it takes your order...'
)
print(filled)

Verificação de conhecimento

Por que a especificidade importa — recapitulação