Prompt Manager

• Prompt Manager is an integrated system that structures, optimizes, evaluates, stores, and manages natural-language prompts for LLMs and foundation models.
• It employs a modular architecture with standardized interfaces for LLMs, predictors, tasks, and optimizers to facilitate flexible and reliable prompt improvements.
• Advanced optimization algorithms like OPRO, EvoPromptGA, and CAPO drive performance gains on tasks such as GSM8K and SST-5, supporting cost-aware and reproducible prompt evolution.

A Prompt Manager is an integrated software system that structures, optimizes, evaluates, stores, and manages natural-language prompts for LLMs and related foundation models. It provides the workflow scaffolding, modular interfaces, and algorithmic backends necessary for both manual and automated prompt improvement, facilitating reliable model behavior across complex and evolving tasks. Modern prompt managers support modular optimization, experiment tracking, versioning, cross-tool integration, and robust evaluation, enabling technical users to control and audit prompt evolution in research and production settings (Zehle et al., 2 Dec 2025).

1. Modular Architecture and Data Flow

Contemporary prompt managers organize their systems around interchangeable abstractions for LLM access, prediction, task definition, and optimization. A typical high-level structure consists of four base component classes—LLM, Predictor, Task, and Optimizer—with standardized interfaces. This facilitates extensibility and enables the easy swapping of components to match backend models, evaluation criteria, and optimization strategies.

+-----------+ <--> +--------------+ <--> +-----------+ <--> +-------------+ | BaseLLM | | BasePredictor| | BaseTask | | BaseOptimizer| +-----------+ +--------------+ +-----------+ +-------------+ [Gemma, OpenAI, LocalHF] [Answer extraction, Classification] [Reward metric, Classification, Judge] [OPRO, EvoPromptGA, CAPO]

In each optimization iteration, the prompt manager executes the following pipeline:

1. Optimizer generates candidate prompts $p_i$.
2. Each candidate prompt is instantiated across examples: the LLM produces outputs, the Predictor parses them, and the Task metric computes scores.
3. Scores and prompt candidates are fed back to the Optimizer for population update.
4. Caching mechanisms track evaluated candidates to reduce redundant computation.

Key Python class signatures:

class BaseLLM: def generate(self, prompt: str) -> str def token_usage(self) -> (n_in, n_out) class BasePredictor: def __init__(self, LLM: BaseLLM) def predict(self, prompt: str, x: Any) -> y_pred class BaseTask: def __init__(self, data: DataFrame, description: str, metric: Callable) def evaluate(self, prompt: str) -> float class BaseOptimizer: def __init__(self, predictor: BasePredictor, task: BaseTask, **cfg) def optimize(self, n_steps: int) -> List[str] def _step(self, population: List[str], scores: List[float]) -> List[str]

All components inherit from these base classes, enabling unified experiment configuration and extension (Zehle et al., 2 Dec 2025).

2. Prompt Optimization Algorithms

Prompt managers implement discrete optimization methods that treat the prompt as a program to be refined over candidate spaces:

• OPRO (Meta-LLM Optimization):

Iteratively prompt an LLM with task description, scored prompt candidates, and few-shot examples, with the objective:

$$\max_{p}J(p),\quad J(p)=\frac{1}{N}\sum_{i=1}^{N}\mathbf{1}[\hat{y}_i(p)=y_i]$$

where $\hat{y}_i$ is the model prediction under prompt $p$.

• EvoPromptGA (Genetic Algorithm):

Maintains a population $P_t$ of textual prompts. Selection, crossover, and mutation are conducted by an LLM:

$$p_{child} = \mathrm{LLM}(\texttt{crossover}(p_a, p_b)),\quad p' = \mathrm{LLM}(\texttt{mutate}(p))$$

• EvoPromptDE (Differential Evolution):

Updates candidate vectors (token sequences) using LLM-mediated operators:

$$v = p_{r1} + F \cdot (p_{r2} - p_{r3}),\quad u = \text{crossover}(p_i, v)$$

with $F$ mutation scale and $C_r$ crossover probabilty.

• CAPO (Cost-Aware Prompt Optimization):

Adapts EvoPromptGA to jointly optimize instructions and few-shot selections under a token budget $B$, providing budget management callbacks for API usage (Zehle et al., 2 Dec 2025).

These approaches allow dynamic balancing of exploration and cost. Optimizers are selected, configured, and plugged into the workflow according to problem requirements.

3. Extensibility and Experiment Management

Prompt managers facilitate extensibility by exposing a uniform API for adding new optimizers, predictors, tasks, and LLM wrappers. An experiment configuration dataclass (e.g., ExperimentConfig) collects all hyperparameters, backend model selectors, metric functions, and callback registrations in a YAML-like format.

Experiment orchestration supports both code-based and GUI-driven workflows. Callbacks, implemented via BaseCallback, allow early stopping, budget enforcement, and detailed logging.

Example usage (manual Py API):

LLM = APILLM(api_url="...", model_id="...", api_key="...") predictor = MarkerBasedPredictor(LLM=LLM) task = ClassificationTask(df, task_description="Classify sentiment.", x_column="text", y_column="label", metric=accuracy_score) optim = CAPO(predictor=predictor, task=task, meta_llm=LLM, initial_prompts=["Sentiment analysis: ..."]) best_prompts = optim.optimize(n_steps=12)

Example usage (single-line orchestration):

from promptolution.experiments import ExperimentConfig, run_experiment config = ExperimentConfig( optimizer="capo", task_description="Solve grade-school math word problems step by step.", n_steps=12, api_url="...", model_id="..." ) best_prompts = run_experiment(df, config)

Component modularity enables rapid switching between optimization strategies and task definitions with minimal configuration edits (Zehle et al., 2 Dec 2025).

4. LLM-Agnostic Adapter Patterns

A core capability of modern prompt managers is model-agnostic design. All model-specific code is confined to adapter classes that wrap APIs (APILLM for cloud endpoints such as OpenAI or Anthropic, LocalLLM for HuggingFace pipelines, VLLM for high-throughput servers). These adapters implement only two required methods: generate(prompt) and token_usage().

Predictor implementations decouple output parsing from model backend, supporting easy integration of new extraction heuristics or output post-processing without modifying underlying LLM wrappers.

Example API wrapper:

class APILLM(BaseLLM): def __init__(self, api_url, model_id, api_key, **kwargs): ... def generate(self, prompt): resp = requests.post(self.api_url, ...) return resp.json()["choices"][0]["text"]

By maintaining minimal interfaces, prompt managers can support open, local, or proprietary models interchangeably, and extend to future backends with little code refactoring (Zehle et al., 2 Dec 2025).

5. Empirical Benchmarks and Comparative Evaluation

Prompt managers are increasingly evaluated on representative tasks—classification (SST-5), math reasoning (GSM8K), and others—against established toolkits such as AdalFlow and DSPy. Optimization efficacy is measured by standardized metrics (accuracy, token utilization) across controlled data splits.

Performance Comparison Table

Framework	Optimizer	GSM8K	SST-5
Baseline	unoptimized	78.1%	44.6%
AdalFlow	AutoDiff	88.7%	55.7%
DSPy	GEPA	84.7%	42.0%
promptolution	OPRO	69.7%	56.0%
	EvoPromptGA	91.0%	53.3%
	CAPO	93.7%	56.3%

CAPO yields the highest accuracy (+15.6 point gain) for GSM8K and leads on sentiment analysis (SST-5). Underperformance in one optimizer (e.g., OPRO on GSM8K) is mitigated by rapid optimizer switching enabled by modular design.

Prompt managers thus offer competitive or state-of-the-art performance, robust configuration, and tool interoperability (Zehle et al., 2 Dec 2025).

6. Practical System Integration and Usage Scenarios

Prompt managers are deployed for both research experimentation and production model serving. Practical considerations include:

• Component inheritance: allows rapid development and testing of new optimizers or predictors.
• Experiment bundling: all parameters and state are tracked centrally, supporting experiment reproducibility and analysis.
• API and GUI orchestration: both programmable and end-user interfaces are provided.
• Cost-aware optimization: crucial for cloud-based models with strict token budgets; optimization can be halted or tuned based on consumption via callbacks.
• Output caching: redundant evaluations are avoided to maximize throughput and minimize latency.

The system architecture enables scalable and extensible workflows adaptable to evolving LLM technologies and deployment contexts. It establishes a canonical solution for prompt optimization and management, integrating research-grade optimization with practical engineering constraints (Zehle et al., 2 Dec 2025).