The prompt section (internally called ContextBuilderConfig) controls how prompts are generated for the LLM, including system messages, templates, and simplification suggestions.
prompt: system_message: "You are an expert to help find the best solution to the problem." template: "default" template_dir: null evaluator_system_message: "evaluator_system_message"
prompt: system_message: | You are an expert algorithm designer. Your task is to find the best solution that: - Maximizes accuracy on the test dataset - Minimizes computational complexity - Uses only standard library functions Provide well-documented, production-ready code.
prompt: system_message: | You are an expert algorithm designer specializing in optimization. Task: Improve the provided algorithm to maximize performance. Objectives: - Minimize time complexity (Big-O) - Minimize space complexity - Maintain correctness on all test cases Constraints: - Must use Python 3.10+ - No external dependencies beyond standard library - Code must be readable and well-documented Techniques to explore: - Dynamic programming - Memoization - Data structure optimization - Algorithmic paradigm shifts
prompt: system_message: | You are a machine learning expert. Design models for image classification. Dataset: CIFAR-10 (32x32 RGB images, 10 classes) Objectives: - Maximize test accuracy - Minimize inference time - Keep model size under 10MB Constraints: - PyTorch only - No pre-trained models - Must train in under 30 minutes on single GPU Explore: - Novel architectures - Efficient convolution alternatives - Regularization techniques - Data augmentation strategies
Configure separate messages for generation and evaluation:
configs/llm_judge.yaml
prompt: system_message: "You are an expert programmer. Improve the given program." evaluator_system_message: | You are a strict code quality judge. Evaluate the given code and return a JSON object with scores between 0.0 and 1.0 for each metric: { "correctness": <score>, "efficiency": <score>, "readability": <score>, "robustness": <score> } Be critical - only exceptional code should score above 0.8. Consider edge cases, error handling, and production readiness.
prompt: template: "evox" system_message: | Design an optimization algorithm for black-box function optimization. Requirements: - Function signature: optimize(func, dim, budget) -> best_x, best_y - Return best found solution and its value - Efficient exploration-exploitation balance
Techniques to consider:- Dynamic programming for optimal substructure- Greedy algorithms for local optimization- Divide-and-conquer for parallelization- Caching/memoization for repeated computation
Set Clear Constraints
Define hard limits:
Constraints:- Time complexity: O(n log n) or better- Space complexity: O(n) or better- No external libraries- Must pass all test cases
Include Examples
Show expected behavior:
Example:Input: [3, 1, 4, 1, 5, 9, 2, 6]Output: [1, 1, 2, 3, 4, 5, 6, 9]The algorithm should sort the array in ascending order.