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Chain-of-Thought Reasoning

Chain-of-Thought (CoT) reasoning enables step-by-step problem solving for complex queries.

Overview​

CoT reasoning breaks down complex problems into intermediate steps, improving accuracy and interpretability.

Usage​

See DSPy Strategies for implementing Chain-of-Thought reasoning with DSPy.

from recoagent.reasoning import DSPyReasoningEngine, DSPyStrategy

engine = DSPyReasoningEngine()

result = engine.reason(
    query="Solve this problem step by step",
    strategy=DSPyStrategy.COT
)

Benefits​

  • Improved Accuracy: Step-by-step reasoning reduces errors
  • Interpretability: See reasoning process
  • Better Debugging: Identify where reasoning fails

Best Practices​

  1. Use for Complex Problems: CoT works best for multi-step problems
  2. Monitor Performance: Track reasoning quality
  3. Cache Results: Cache expensive CoT operations