Chaining optimizers

Some projects benefit from running two or more optimizers back-to-back. For example, use MetaPrompt to improve wording, then Parameter optimizer to fine-tune sampling settings. This guide explains why you might chain runs, the trade-offs, and the APIs you use to pass prompts and metadata between stages.

Strategy patterns

Pipeline	Why run it	Pros	Cons	Complexity
HRPO → Parameter	Existing long/complex prompt scenario: HRPO's reflective analysis finds failure modes; Parameter optimizer then tightens parameters on the improved prompt.	Excellent for legacy prompts with lots of existing complexity. Helps produce explainable changelog.	Requires metrics with rich `reason` strings; two stages increase cost.	Medium
Evolutionary → Few-Shot Bayesian	Cold-start scenario: explore many prompt architectures first, then let Few-Shot Bayesian pick the best example combination for the winning structure.	High diversity followed by precise example selection.	Evolutionary runs are expensive; Bayesian stage relies on curated datasets.	High
MetaPrompt → Parameter	Baseline prompts need polish plus sampling tweaks.	Quick wins with minimal configuration; can run in under an hour.	Less insight into failure modes than reflective pipelines.	Low.
Evolutionary → Parameter	Hunt for novel prompts, then squeeze out cost/latency by tuning temperature/top_p.	Balances creativity with production readiness.	Two heavy optimization loops; ensure budget headroom.	High.

Example pipeline

Python

from opik_optimizer import MetaPromptOptimizer, ParameterOptimizer, ChatPrompt
from opik_optimizer.parameter_optimizer import ParameterSearchSpace

meta = MetaPromptOptimizer(model="openai/gpt-4o")
parameter = ParameterOptimizer(model="openai/gpt-4o")

meta_result = meta.optimize_prompt(
    prompt=prompt,
    dataset=dataset,
    metric=metric,
    max_trials=4,
)

# Reuse the optimized prompt from the first stage
optimized_prompt = prompt.with_messages(meta_result.prompt)

search_space = ParameterSearchSpace(parameters=[
    {"name": "temperature", "distribution": "float", "low": 0.1, "high": 0.9},
    {"name": "top_p", "distribution": "float", "low": 0.7, "high": 1.0},
])

final_result = parameter.optimize_parameter(
    prompt=optimized_prompt,
    dataset=dataset,
    metric=metric,
    parameter_space=search_space,
    max_trials=20,
)

Checklist

Freeze datasets and metrics between stages to keep comparisons fair.
Use validation datasets consistently – if you provide a validation_dataset in the first stage, use the same split in subsequent stages to ensure fair comparison and avoid overfitting.
Log pipeline metadata (e.g., experiment_config={"pipeline": "hierarchical_then_param"}) so dashboards show lineage.
Budget tokens – chained runs multiply costs; start with smaller n_samples and increase once results look promising.
Reuse OptimizationResult – every optimizer returns an OptimizationResult, so you can pass result.prompt (and result.details, result.history) directly into the next stage without rebuilding state.

Automation tips

Use Makefiles or CI workflows to run stage 1 → stage 2 with clear checkpoints.
Store intermediate prompts in version control alongside metadata (optimizer, score, dataset).
Notify stakeholders with summary reports generated from final_result.history.

Strategy patterns

Example pipeline

Checklist

Automation tips

Related docs