Choisir les paramètres selon votre cas d’usage

Le cadre de décision des paramètres

Questions-réponses factuelles : l’exactitude avant tout

import openai
client = openai.OpenAI(api_key='sk-...')

# Factual Q&A configuration
def factual_qa(question):
    resp = client.chat.completions.create(
        model='gpt-4o',
        messages=[
            {'role': 'system', 'content': 'Answer factual questions concisely and accurately.'},
            {'role': 'user', 'content': question}
        ],
        temperature=0,
        top_p=1.0
    )
    return resp.choices[0].message.content

answer = factual_qa('What is the boiling point of water at sea level?')
print(answer)  # Always: '100 degrees Celsius (212 degrees Fahrenheit).'

Génération de code : presque gloutonne

# Code generation configuration
def generate_code(task):
    resp = client.chat.completions.create(
        model='gpt-4o',
        messages=[
            {'role': 'system', 'content': 'You are an expert Python programmer. Write clean, correct code.'},
            {'role': 'user', 'content': task}
        ],
        temperature=0.1,
        top_p=0.95
    )
    return resp.choices[0].message.content

code = generate_code('Write a Python function that merges two sorted lists.')
print(code)

Résumé : paramètres modérés

# Summarization configuration
def summarize(text):
    resp = client.chat.completions.create(
        model='gpt-4o',
        messages=[
            {'role': 'system', 'content': 'Summarize the following text in 3 sentences.'},
            {'role': 'user', 'content': text}
        ],
        temperature=0.4,
        top_p=1.0
    )
    return resp.choices[0].message.content

summary = summarize(long_article)
print(summary)

Discussion conversationnelle : variation naturelle

# Chat configuration
def chat_response(user_message, history):
    messages = history + [{'role': 'user', 'content': user_message}]
    resp = client.chat.completions.create(
        model='gpt-4o',
        messages=[
            {'role': 'system', 'content': 'You are a friendly, helpful assistant.'},
            *messages
        ],
        temperature=0.8,
        top_p=0.9
    )
    reply = resp.choices[0].message.content
    history.append({'role': 'user', 'content': user_message})
    history.append({'role': 'assistant', 'content': reply})
    return reply, history

Écriture créative : diversité élevée

# Creative writing configuration
def write_story_opening(prompt):
    resp = client.chat.completions.create(
        model='gpt-4o',
        messages=[
            {'role': 'system', 'content': 'You are a creative fiction writer. Write vivid, original prose.'},
            {'role': 'user', 'content': f'Write a story opening for: {prompt}'}
        ],
        temperature=1.1,
        top_p=0.95
    )
    return resp.choices[0].message.content

opening = write_story_opening('A detective discovers her partner is the killer.')
print(opening)

Remue-méninges et idéation

# Brainstorming: run multiple times for variety
def brainstorm(topic, n_runs=5):
    ideas = []
    for _ in range(n_runs):
        resp = client.chat.completions.create(
            model='gpt-4o',
            messages=[
                {'role': 'system', 'content': 'Generate creative, diverse ideas. Include unusual approaches.'},
                {'role': 'user', 'content': f'Give me 3 ideas for: {topic}'}
            ],
            temperature=1.3,
            top_p=0.95
        )
        ideas.append(resp.choices[0].message.content)
    return ideas

all_ideas = brainstorm('reducing customer churn')

Analyse de sensibilité des paramètres

def sensitivity_test(prompt, evaluator, temperatures, n_samples=5):
    results = {}
    for temp in temperatures:
        scores = []
        for _ in range(n_samples):
            resp = client.chat.completions.create(
                model='gpt-4o',
                messages=[{'role': 'user', 'content': prompt}],
                temperature=temp
            )
            score = evaluator(resp.choices[0].message.content)
            scores.append(score)
        results[temp] = sum(scores) / len(scores)

    for temp, score in sorted(results.items()):
        print(f'T={temp}: avg score = {score:.2f}')
    best_temp = max(results, key=results.get)
    print(f'Best temperature: {best_temp}')
    return best_temp

Quand conserver les valeurs par défaut

# Defaults are often correct
default_resp = client.chat.completions.create(
    model='gpt-4o',
    messages=[{'role': 'user', 'content': 'Draft a professional email declining a meeting.'}]
    # No temperature or top_p specified — API defaults apply
    # OpenAI default: temperature=1, top_p=1
)
print(default_resp.choices[0].message.content)

Fiche de référence rapide

PARAMETER_PRESETS = {
    # task: (temperature, top_p, notes)
    'factual_qa':        (0.0, 1.0, 'greedy, max accuracy'),
    'classification':    (0.0, 1.0, 'deterministic labels'),
    'code_generation':   (0.1, 0.95, 'near-greedy, avoid tail tokens'),
    'data_extraction':   (0.2, 1.0, 'slight variation for robustness'),
    'summarization':     (0.4, 1.0, 'preserve facts, vary phrasing'),
    'translation':       (0.3, 1.0, 'accurate but natural'),
    'chat':              (0.8, 0.9, 'natural conversation'),
    'email_drafting':    (0.7, 0.9, 'professional but varied'),
    'creative_writing':  (1.1, 0.95, 'vivid, original'),
    'brainstorming':     (1.3, 0.95, 'maximum diversity'),
}

for task, (temp, top_p, notes) in PARAMETER_PRESETS.items():
    print(f'{task:22} temp={temp}, top_p={top_p} | {notes}')

Pénalités de fréquence et de présence

resp = client.chat.completions.create(
    model='gpt-4o',
    messages=[{'role': 'user', 'content': 'Write a paragraph about coffee.'}],
    temperature=1.0,
    top_p=0.95,
    frequency_penalty=0.5,  # discourage repeating the same words
    presence_penalty=0.3    # encourage introducing new topics/aspects
)
print(resp.choices[0].message.content)

Vérification des connaissances

Récapitulatif : choisir les paramètres d’échantillonnage