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name: foundation-models description: Understanding Foundation Models - architecture, sampling parameters, structured outputs, post-training. Use when configuring LLM generation, selecting models, or understanding model behavior.

Foundation Models

Deep understanding of how Foundation Models work.

Sampling Parameters

Copy# Temperature Guide
TEMPERATURE = {
    "factual_qa": 0.0,           # Deterministic
    "code_generation": 0.2,       # Slightly creative
    "translation": 0.3,           # Mostly deterministic
    "creative_writing": 0.9,      # Creative
    "brainstorming": 1.2,         # Very creative
}

# Key parameters
response = client.chat.completions.create(
    model="gpt-4",
    messages=[...],
    temperature=0.7,    # 0.0-2.0, controls randomness
    top_p=0.9,          # Nucleus sampling (0.0-1.0)
    max_tokens=1000,    # Maximum output length
)

Structured Outputs

Copy# JSON Mode
response = client.chat.completions.create(
    model="gpt-4",
    messages=[...],
    response_format={"type": "json_object"}
)

# Function Calling
tools = [{
    "type": "function",
    "function": {
        "name": "get_weather",
        "parameters": {
            "type": "object",
            "properties": {
                "location": {"type": "string"},
                "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}
            },
            "required": ["location"]
        }
    }
}]

Post-Training Stages

Model Selection Factors

Best Practices

1. Match temperature to task type
2. Use structured outputs when parsing needed
3. Consider context length limits
4. Test sampling parameters systematically
5. Account for knowledge cutoff dates

Common Pitfalls

• High temperature for factual tasks
• Ignoring tokenization costs for multilingual
• Not accounting for context length limits
• Expecting determinism without temperature=0