The Opik Optimizer uses a **PromptLibrary** system that lets you customize the internal prompts used by each optimizer. This is useful when you need to:

- Add domain-specific constraints (legal, medical, coding standards)
- Inject safety or compliance requirements
- Adjust output formatting or style
- Experiment with different reasoning approaches

## Quick Start

Every optimizer accepts a `prompt_overrides` parameter:

```python
from opik_optimizer import MetaPromptOptimizer

# Simple dict override
optimizer = MetaPromptOptimizer(
    model="gpt-4o",
    prompt_overrides={"reasoning_system": "Be concise. Focus on clarity."}
)
```

## How It Works

Each optimizer defines its own `DEFAULT_PROMPTS` dictionary with keys specific to that algorithm. The PromptLibrary:

1. Stores the default prompts
2. Applies your overrides (dict or callable)
3. Validates that override keys exist (catches typos early)
4. Provides `get_prompt()` for runtime access

```mermaid
flowchart LR
    subgraph Optimizer
        D[DEFAULT_PROMPTS<br/>reasoning: ...<br/>synthesis: ...]
    end

    subgraph PromptLibrary
        M[Merge with overrides]
        V[Validate keys]
        F[Format templates]
    end

    O[prompt_overrides<br/>dict or callable]

    D --> M
    O --> M
    M --> V --> F

    F --> G[optimizer.get_prompt]
```

## Override Methods

::::accordion-group
:::accordion{title="Dict Override (Simple Replacement)"}
Best when you know exactly which prompt to replace with a static string:

```python
from opik_optimizer import EvolutionaryOptimizer

optimizer = EvolutionaryOptimizer(
    model="gpt-4o",
    prompt_overrides={
        "synonyms_system_prompt": "Return exactly ONE synonym. No explanation.",
        "infer_style_system_prompt": "Analyze the writing style briefly.",
    }
)
```
:::

:::accordion{title="Callable Override (Dynamic Modification)"}
Best when you need to modify existing prompts, apply conditional logic, or update multiple prompts:

```python
from opik_optimizer import MetaPromptOptimizer
from opik_optimizer.utils.prompt_library import PromptLibrary

def customize_prompts(prompts: PromptLibrary) -> None:
    # List available keys
    print("Available keys:", prompts.keys())

    # Prepend a constraint to the reasoning prompt
    original = prompts.get("reasoning_system")
    prompts.set("reasoning_system", "Always respond in English.\n\n" + original)

    # Append format instructions to another prompt
    if "candidate_generation" in prompts.keys():
        prompts.set(
            "candidate_generation",
            prompts.get("candidate_generation") + "\n\nUse markdown formatting."
        )

optimizer = MetaPromptOptimizer(
    model="gpt-4o",
    prompt_overrides=customize_prompts
)
```
:::
::::

## Discovering Available Keys

Each optimizer has different prompt keys. Use `list_prompts()` to discover them:

```python
from opik_optimizer import MetaPromptOptimizer

optimizer = MetaPromptOptimizer(model="gpt-4o")
print("Available prompt keys:")
for key in optimizer.list_prompts():
    print(f"  - {key}")
```

### Common Keys by Optimizer

| Optimizer                           | Key Examples                                                                                      |
| ----------------------------------- | ------------------------------------------------------------------------------------------------- |
| **MetaPromptOptimizer**             | `reasoning_system`, `candidate_generation`, `synthesis`, `pattern_extraction_system`              |
| **EvolutionaryOptimizer**           | `infer_style_system_prompt`, `synonyms_system_prompt`, `semantic_mutation_system_prompt_template` |
| **FewShotBayesianOptimizer**        | `example_placeholder`, `system_prompt_template`                                                   |
| **HierarchicalReflectiveOptimizer** | `batch_analysis_prompt`, `synthesis_prompt`, `improve_prompt_template`                            |

## Reading Prompts at Runtime

After creating an optimizer, you can inspect the current prompts:

```python
optimizer = MetaPromptOptimizer(
    model="gpt-4o",
    prompt_overrides={"reasoning_system": "Custom prompt here..."}
)

# Get the current (possibly overridden) prompt
current = optimizer.get_prompt("reasoning_system")
print(current)

# Get the original default (before any overrides)
default = optimizer.prompts.get_default("reasoning_system")
print(default)
```

## Template Variables

Some prompts contain placeholders that get filled at runtime using Python's `{variable}` format. When overriding prompts with placeholders, **keep the same placeholders**:

```python
# Original: "Generate {num_prompts} variations of the prompt."
# Your override should keep {num_prompts}:
prompt_overrides = {
    "candidate_generation": "Be creative. Generate {num_prompts} diverse variations."
}
```

## Use Cases

::::accordion-group
:::accordion{title="Adding Domain Constraints"}
```python
def add_legal_constraints(prompts: PromptLibrary) -> None:
    for key in prompts.keys():
        original = prompts.get(key)
        prompts.set(key,
            "LEGAL CONTEXT: Do not reference specific case law.\n\n" + original
        )

optimizer = MetaPromptOptimizer(
    model="gpt-4o",
    prompt_overrides=add_legal_constraints
)
```
:::

:::accordion{title="Enforcing Output Format"}
```python
optimizer = EvolutionaryOptimizer(
    model="gpt-4o",
    prompt_overrides={
        "infer_style_system_prompt": """
Analyze writing style. Return a JSON object with:
{
  "tone": "formal|casual|technical",
  "complexity": "simple|moderate|complex",
  "key_patterns": ["list", "of", "patterns"]
}
"""
    }
)
```
:::

:::accordion{title="Adding Safety Guardrails"}
```python
def add_safety_layer(prompts: PromptLibrary) -> None:
    safety_prefix = """
SAFETY REQUIREMENTS:
- Never generate harmful or offensive content
- Avoid personal identifiable information
- Flag uncertain responses

"""
    for key in prompts.keys():
        if "system" in key.lower():
            prompts.set(key, safety_prefix + prompts.get(key))

optimizer = MetaPromptOptimizer(
    model="gpt-4o",
    prompt_overrides=add_safety_layer
)
```
:::
::::

## Error Handling

The PromptLibrary validates keys to catch typos early:

```python
# This will raise KeyError - "reasoing_system" is misspelled
optimizer = MetaPromptOptimizer(
    model="gpt-4o",
    prompt_overrides={"reasoing_system": "Oops, typo!"}  # KeyError!
)

# Error message shows available keys:
# KeyError: "Unknown prompt keys: ['reasoing_system'].
#            Available: ['candidate_generation', 'reasoning_system', ...]"
```

## Best Practices

:::callout{intent="info"}
**Tips for effective prompt customization:**

1. **List keys first** – Always use `list_prompts()` to see available keys before overriding
2. **Keep placeholders** – If a prompt has `{variables}`, keep them in your override
3. **Test incrementally** – Override one prompt at a time to isolate effects
4. **Use callable for complex logic** – Dict is simpler, but callable is more powerful
5. **Don't break JSON** – Some prompts expect JSON output; maintain that structure
:::

## Full Example

```python
from opik_optimizer import MetaPromptOptimizer
from opik_optimizer.utils.prompt_library import PromptLibrary

def my_customizations(prompts: PromptLibrary) -> None:
    """Customize prompts for a code generation task."""

    # 1. Add coding focus to reasoning
    prompts.set(
        "reasoning_system",
        "You are an expert code prompt engineer.\n\n" + prompts.get("reasoning_system")
    )

    # 2. Enforce Python-specific patterns
    prompts.set(
        "candidate_generation",
        prompts.get("candidate_generation") + """

ADDITIONAL REQUIREMENTS:
- Prompts should encourage well-documented code
- Prefer type hints and docstrings
- Emphasize error handling and edge cases
"""
    )

# Create optimizer with customizations
optimizer = MetaPromptOptimizer(
    model="gpt-4o",
    prompt_overrides=my_customizations
)

# Verify customizations applied
print("Customized reasoning prompt:")
print(optimizer.get_prompt("reasoning_system")[:200] + "...")
```

## Related

- [API Reference](https://www.comet.com/development/optimization-runs/advanced/api_reference) – Full parameter documentation
- [Custom metrics](https://www.comet.com/development/optimization-runs/advanced/custom_metrics) – Build specialized evaluation metrics
- [Extending optimizers](https://www.comet.com/development/optimization-runs/advanced/extending_optimizers) – Create custom optimizer subclasses

## Related pages

- [Extending Optimizers](./development-optimization-runs-advanced-extending-optimizers.md)
- [Custom metrics](./development-optimization-runs-advanced-custom-metrics.md)
- [Sampling controls](./development-optimization-runs-advanced-n-samples.md)
- [Multiple Completions (n parameter)](./development-optimization-runs-advanced-multiple-completions.md)
- [Chaining optimizers](./development-optimization-runs-advanced-chaining-optimizers.md)
- [Opik Agent Optimizer API Reference](./development-optimization-runs-advanced-api-reference.md)

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