Najlepsze praktyki organizacji promptów

Kolejność jest decyzją projektową

Najważniejsze instrukcje na początku

# Good: Critical constraint leads
system_prompt = '''
You are a customer support agent. Never reveal pricing or discount policies.
Always escalate billing complaints to the billing team.

Help customers resolve product issues using the knowledge base below.
'''

# Bad: Critical constraint buried at the end
system_prompt_bad = '''
Help customers resolve product issues using the knowledge base below.
[...lots of instructions...]
Oh, and never reveal pricing policies.
'''

print('Contrast shown.')

Instrukcje dotyczące formatu danych wyjściowych na końcu

# Good: Format at the end
prompt = '''
<role>You are a data analyst.</role>

<task>Analyze the sales data below and identify the top 3 trends.</task>

<data>
Q1: $1.2M, Q2: $1.8M, Q3: $1.6M, Q4: $2.1M
Product A: 40% of revenue, Product B: 35%, Product C: 25%
</data>

<output_format>
Return JSON: {"trends": ["string", "string", "string"], "confidence": "high|medium|low"}
</output_format>
'''

print(prompt)

Przykłady blisko miejsca, którego dotyczą

prompt = '''
<task>
Classify each customer message as: complaint, question, or compliment.
</task>

<examples>
  Input: "Your app keeps crashing!" -> complaint
  Input: "How do I reset my password?" -> question
  Input: "The new design is beautiful." -> compliment
</examples>

<input>
I can not find where to cancel my subscription.
</input>
'''

# Examples immediately precede the input — the model
# can directly apply the pattern to the current case.
print(prompt)

Kontekst przed zadaniem

# Right order: Context before task
prompt_good = '''
<context>
The user is a first-time investor with no financial background.
They are asking about index funds.
Avoid jargon. Never give specific investment advice.
</context>

<task>
Answer the user question below in plain language.
</task>
'''

# Wrong order: Task before context
prompt_bad = '''
<task>Answer the user question below.</task>
<context>User is a first-time investor, avoid jargon.</context>
'''

print('Order matters: context before task.')

Rozdzielanie instrukcji pozytywnych i negatywnych

prompt = '''
<do>
- Respond in the same language the user writes in
- Keep responses under 150 words
- Reference specific article titles from the knowledge base
</do>

<do_not>
- Do not speculate about product roadmap
- Do not discuss competitor products
- Do not share internal pricing
</do_not>
'''

print(prompt)

Częsty błąd dotyczący kolejności: persona na końcu

Częsty błąd dotyczący kolejności: ukryte ograniczenia

# Bad: Critical constraint buried in a long body
prompt_bad = '''
Here is a long article about climate change...
[500 words of content]
Do not mention fossil fuel companies by name.
[More content...]
'''

# Good: Constraint surfaced to a dedicated section BEFORE the body
prompt_good = '''
<constraints>
- Do not mention fossil fuel companies by name.
- Do not make claims not supported by the article.
</constraints>

<article>
Here is a long article about climate change...
[500 words of content]
</article>
'''

print('Constraints before body prevents lost-in-the-middle.')

Efekt lost-in-the-middle

Lista kontrolna kolejności instrukcji

ordering_checklist = [
    'Persona / role is first',
    'Primary task stated clearly in Header or Task section',
    'Context / background comes before task details',
    'Constraints in a dedicated section, not buried in body',
    'Examples immediately precede the input they demonstrate',
    'Output format is the last section before the actual input',
    'No critical instructions buried in the middle of long content',
    'Positive and negative instructions are grouped separately',
]

for i, item in enumerate(ordering_checklist, 1):
    print(f'{i}. [ ] {item}')

Testowanie wrażliwości na kolejność

import anthropic

client = anthropic.Anthropic(api_key='YOUR_API_KEY')

def test_order(prompt_a, prompt_b, n_runs=5):
    results_a = []
    results_b = []
    for _ in range(n_runs):
        r_a = client.messages.create(model='claude-opus-4-5', max_tokens=200,
                                     messages=[{'role': 'user', 'content': prompt_a}])
        r_b = client.messages.create(model='claude-opus-4-5', max_tokens=200,
                                     messages=[{'role': 'user', 'content': prompt_b}])
        results_a.append(r_a.content[0].text)
        results_b.append(r_b.content[0].text)
    return results_a, results_b

# Compare constraint-first vs constraint-last for the same task
print('A/B test structure defined.')

Szybkie sprawdzenie

Kolejność promptu — najważniejsze informacje