Optimize prompts

Use this playbook whenever you need to improve a prompt (single-turn or agentic) and want a repeatable process rather than manual tweaks.

• Record the current prompt and score using your production metric.
• Log at least 10 representative dataset rows so the optimizer can generalize.
• Capture latency and token costs—optimizations should not regress them unexpectedly.

from opik_optimizer import HRPO

optimizer = HRPO(
    model="openai/gpt-4o",
    max_parallel_batches=4,
    seed=42,
)
result = optimizer.optimize_prompt(
    prompt=my_prompt,
    dataset=my_dataset,
    metric=answer_quality,
    max_trials=5,
    n_samples=50,
)

• Set project_name on the optimizer to group runs by team or initiative.
• Start with max_trials = 3–5. Increase once you confirm the metric is reliable.
• Use n_samples to limit cost during early exploration; rerun on the full dataset before promoting a prompt.
• For optimizers with inner-loop evaluations (HRPO, GEPA), set n_samples_minibatch to keep those steps lightweight.
• Use n_samples_strategy to keep subsampling deterministic (default: "random_sorted").

Tool optimization is now documented separately. Use it when you want to improve MCP tool descriptions without changing prompt text.

• Optimize tools (MCP)

If you need finer control than roles (for example, only optimize a specific assistant message), use prompt_segments to extract and update parts by segment ID.

Intent/Trigger: use segment-level updates when you need to constrain changes to exact message segments.

• Required parameters: prompt, dataset, metric
• Optional parameters: segment update args (updates passed to prompt_segments.apply_segment_updates)
• Minimal valid payload: optimizer.optimize_prompt(prompt=updated_prompt, dataset=my_dataset, metric=answer_quality)

from opik_optimizer.utils import prompt_segments

segments = prompt_segments.extract_prompt_segments(my_prompt)
for segment in segments:
    print(segment.segment_id, segment.role)

# Update only message:1 (second message)
updates = {"message:1": "User question: {user_query}"}
updated_prompt = prompt_segments.apply_segment_updates(my_prompt, updates)

# Use the updated prompt in optimization (the original prompt is unchanged)
result = optimizer.optimize_prompt(
    prompt=updated_prompt,
    dataset=my_dataset,
    metric=answer_quality,
)

You can pass a dict of ChatPrompt objects to optimize a coordinated prompt bundle (for example, a multi-agent setup or system/user prompt pair that must stay in sync). Each key names a prompt and is preserved through optimization.

from opik_optimizer import MetaPromptOptimizer, ChatPrompt

prompts = {
    "researcher": ChatPrompt(
        name="researcher",
        messages=[
            {"role": "system", "content": "Gather facts and cite sources."},
            {"role": "user", "content": "{question}"},
        ],
    ),
    "synthesizer": ChatPrompt(
        name="synthesizer",
        messages=[
            {"role": "system", "content": "Summarize findings clearly."},
            {"role": "user", "content": "{question}"},
        ],
    ),
}

optimizer = MetaPromptOptimizer(model="openai/gpt-4o-mini", prompts_per_round=2)
result = optimizer.optimize_prompt(
    prompt=prompts,
    dataset=my_dataset,
    metric=answer_quality,
    max_trials=3,
)

result.prompt returns a dict keyed by the same names so you can update each agent prompt together.

• Compare result.score vs. result.initial_score to ensure material improvement.
• Review the history attribute for regression reasons.
• Use Dashboard results to visualize per-trial performance.

• Optimization Studio
• Define datasets
• Define metrics
• Chaining optimizers
• Avoiding overfitting – Prevent your prompt from memorizing the training data by using separate validation datasets