Description
The solution_generation method has a hardcoded prompt. Users can only configure LLM models but cannot customize the generation behavior or provide domain-specific guidance.
Current limitation:
# llm_tree_search.py L433-440
solution_generation_prompt = """
You are a world class problem solver. Generate {per_step_solution_count} exact solutions...
"""
Goal
Allow users to customize solution generation via config:
- Custom system prompt template
- Domain-specific directions/constraints
- Solution format requirements
Proposed Config
search_strategy:
type: "llm_tree_search"
params:
idea_generation_model: "gpt-4.1"
idea_generation_prompt: |
You are an expert in {domain}. Generate solutions that...
idea_generation_directions:
- "Prefer memory-efficient approaches"
- "Consider edge cases for large inputs"
idea_generation_format: "numbered_list" # or "xml_tags"
Implementation
- Add new params to
LlmSteeredTreeSearch.__init__
- Template the
solution_generation_prompt with user values
- Update
strategies.yaml with new param defaults
References
solution_generation method
src/execution/search_strategies/strategies.yaml - Strategy presets
src/config.yaml - Mode configurations
Description
The
solution_generationmethod has a hardcoded prompt. Users can only configure LLM models but cannot customize the generation behavior or provide domain-specific guidance.Current limitation:
Goal
Allow users to customize solution generation via config:
Proposed Config
Implementation
LlmSteeredTreeSearch.__init__solution_generation_promptwith user valuesstrategies.yamlwith new param defaultsReferences
solution_generationmethodsrc/execution/search_strategies/strategies.yaml- Strategy presetssrc/config.yaml- Mode configurations