Czego AI nie potrafi

import anthropic

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

response = client.messages.create(
    model='claude-opus-4-5',
    max_tokens=128,
    messages=[{
        'role': 'user',
        'content': 'What is Bitcoin\'s price right now in USD?'
    }]
)
# The model will acknowledge it cannot access real-time data
print(response.content[0].text)

# To add real-time data, you must inject it yourself:
current_price = 67500  # fetched from an exchange API by YOUR code
response2 = client.messages.create(
    model='claude-opus-4-5',
    max_tokens=128,
    messages=[{
        'role': 'user',
        'content': f'Bitcoin price as of now: ${current_price}. Is this above or below $70,000?'
    }]
)
print(response2.content[0].text)

import openai

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

# Ask the model to disclose its cutoff and caveats
response = client.chat.completions.create(
    model='gpt-4o',
    messages=[{
        'role': 'user',
        'content': (
            'I need to know about the latest AI models released in the past 3 months. '
            'Please state your knowledge cutoff date and any caveats before answering.'
        )
    }]
)
print(response.choices[0].message.content)

# Best practice: inject a date stamp so the model knows the current date
from datetime import date
today = date.today().isoformat()
response2 = client.chat.completions.create(
    model='gpt-4o',
    system=f'Today is {today}. Your knowledge cutoff may be earlier — say so if relevant.',
    messages=[{'role': 'user', 'content': 'What are the latest LLM releases?'}]
)

import json
import anthropic

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

# Simulate storing user preferences between sessions
def load_user_profile(user_id):
    # In production: load from database
    return {'name': 'Alice', 'preferred_language': 'Python', 'skill_level': 'intermediate'}

def build_system_message(profile):
    return (
        f'The user\'s name is {profile["name"]}. '
        f'They prefer {profile["preferred_language"]} examples. '
        f'Their skill level is {profile["skill_level"]}. '
        f'Tailor all responses accordingly.'
    )

profile = load_user_profile('user-123')
response = client.messages.create(
    model='claude-opus-4-5',
    max_tokens=256,
    system=build_system_message(profile),
    messages=[{'role': 'user', 'content': 'Show me how to read a file.'}]
)
print(response.content[0].text)

import anthropic

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

# Asking for a citation is a classic hallucination trigger
response = client.messages.create(
    model='claude-opus-4-5',
    max_tokens=256,
    messages=[{
        'role': 'user',
        'content': (
            'Cite 3 peer-reviewed papers about the effect of social media on teen anxiety. '
            'Include author names, journal names, and publication years.'
        )
    }]
)
print(response.content[0].text)
# WARNING: verify every citation independently — some may be fabricated

import anthropic

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

# Ground the model with provided source material
document = (
    'According to the 2023 Pew Research report, 46% of US teens say '
    'they are online almost constantly, up from 24% in 2014-2015.'
)

response = client.messages.create(
    model='claude-opus-4-5',
    max_tokens=256,
    system=(
        'Answer ONLY using the provided document. '
        'If the answer is not in the document, say: "The document does not cover this."'
    ),
    messages=[{
        'role': 'user',
        'content': f'Document:\n{document}\n\nQuestion: What percentage of US teens are online almost constantly?'
    }]
)
print(response.content[0].text)

import openai

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

# Bad practice: ask the LLM to compute a complex calculation directly
response_direct = client.chat.completions.create(
    model='gpt-4o',
    messages=[{'role': 'user', 'content': 'What is 17.83% of 348,921.47?'}]
)
print('LLM answer:', response_direct.choices[0].message.content)

# Good practice: compute in Python, then ask LLM to explain it
result = round(348921.47 * 0.1783, 2)
response_explained = client.chat.completions.create(
    model='gpt-4o',
    messages=[{
        'role': 'user',
        'content': f'17.83% of 348,921.47 is ${result}. Explain what this means for a budget report.'
    }]
)
print('Explained:', response_explained.choices[0].message.content)

import anthropic

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

# Chain-of-thought improves complex reasoning
response = client.messages.create(
    model='claude-opus-4-5',
    max_tokens=512,
    messages=[{
        'role': 'user',
        'content': (
            'A train leaves Station A at 9:00 AM traveling at 80 km/h. '
            'Another train leaves Station B (300 km away) at 10:00 AM traveling at 100 km/h toward Station A. '
            'At what time do they meet?\n\n'
            'Think step by step before giving your answer.'
        )
    }]
)
print(response.content[0].text)

import anthropic
import base64
from pathlib import Path

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

# Images require explicit base64 encoding and vision-capable model
image_data = base64.standard_b64encode(Path('chart.png').read_bytes()).decode('utf-8')

response = client.messages.create(
    model='claude-opus-4-5',
    max_tokens=256,
    messages=[{
        'role': 'user',
        'content': [
            {
                'type': 'image',
                'source': {'type': 'base64', 'media_type': 'image/png', 'data': image_data}
            },
            {'type': 'text', 'text': 'Describe what this chart shows.'}
        ]
    }]
)
print(response.content[0].text)

# Pattern: inject real-time context + use AI for reasoning, not retrieval
import anthropic
from datetime import datetime

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

# Your application fetches these from real sources
weather_data = {'city': 'London', 'temp_c': 12, 'condition': 'rainy'}
news_headline = 'UK inflation drops to 2.3% in April 2025'

context = (
    f'Current date: {datetime.now().strftime("%Y-%m-%d")}\n'
    f'Weather in {weather_data["city"]}: {weather_data["temp_c"]}C, {weather_data["condition"]}\n'
    f'Today\'s top news: {news_headline}'
)

response = client.messages.create(
    model='claude-opus-4-5',
    max_tokens=256,
    system='You are a helpful assistant. Use only the provided context for current facts.',
    messages=[{'role': 'user', 'content': f'{context}\n\nWhat should I wear today and what is the economic mood?'}]
)
print(response.content[0].text)

import openai

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

# Ask the model to flag its own uncertainty
response = client.chat.completions.create(
    model='gpt-4o',
    messages=[
        {
            'role': 'system',
            'content': (
                'After every response, add a line starting with CONFIDENCE: '
                'and rate your certainty as HIGH, MEDIUM, or LOW, '
                'with a brief reason.'
            )
        },
        {
            'role': 'user',
            'content': 'Who won the 2023 FIFA Women\'s World Cup and what was the final score?'
        }
    ]
)
print(response.choices[0].message.content)