Prompt Optimization Framework: A Research-Grade Framework for Automated Prompt Optimization and Multi-Dimensional Evaluation

This article introduces a Python-based research-grade prompt optimization framework. It aims to help researchers systematically discover optimal prompt strategies through comparative experimental design, multi-dimensional evaluation (accuracy/consistency/efficiency), and a greedy selection algorithm. The framework supports dual-mode execution (research validation and production application) and features a modular design for easy extension, suitable for scenarios like academic research and strategy optimization.

The Prompt Optimization Framework is designed specifically for academic research. Its core objective is to evaluate the performance of multiple prompt techniques through comparative experiments under the same model, parameter, and dataset conditions, and automatically identify the optimal strategy. The framework's design philosophy emphasizes "research clarity over premature optimization", with a clear and modular code structure that facilitates understanding and reproduction.

The framework uses three core metrics to comprehensively evaluate prompt strategies:

1. Accuracy: Supports multiple methods such as exact string matching, numerical comparison, and symbolic mathematical equivalence judgment;
2. Consistency: Measures the output stability of multiple runs to avoid interference from outliers;
3. Efficiency: Focuses on response latency, token usage, and answer conciseness, which has direct economic value.

The scoring mechanism calculates the comprehensive score based on user-configured weights to ensure objective evaluation.

Greedy Selection Algorithm: Selects the strategy with the highest comprehensive score through weighted scoring (default: each of the three metrics accounts for 1/3). In case of a tie, it sorts by priority: accuracy → consistency → efficiency. Dual-Mode Execution:

• Benchmark mode: Requires standard answers, compares all prompt techniques in real time, suitable for research validation;
• Normal mode: Ignores standard answers, pre-selects based on historical data (three-level selection mechanism), suitable for production scenarios.

The framework adopts a highly modular design. Core modules include dataset management, prompt generator, model interface, various scorers, and the main workflow. It supports:

• Adding custom prompt techniques (modify prompt_generator.py);
• Custom scorers (create new scorer classes);
• Extending datasets (add questions via the MathDataset class); It also supports Firebase Firestore to persist historical data, facilitating tracking of experimental trends.

The framework is suitable for multiple scenarios:

1. Academic research: Generate publishable comparative data of prompt techniques;
2. Teaching demonstration: Show differences between different prompt strategies;
3. Strategy optimization: Find the optimal prompt template for specific tasks;
4. Model evaluation: Evaluate LLM performance by controlling prompt variables;
5. Cost optimization: Balance accuracy and resource consumption.

The current version mainly focuses on mathematical problem-solving scenarios. Future extensions include:

• Supporting more models (cloud APIs like GPT, Claude, etc.);
• Adding advanced metrics such as hallucination detection and citation accuracy;
• Extending to non-mathematical fields like code generation and text creation;
• Batch dataset evaluation and visualization;
• Automated prompt optimization based on feedback loops.