customaize-agent:thought-based-reasoning

To improve LLM accuracy on complex math and multi-step reasoning tasks, you should use structured prompting strategies like Chain-of-Thought and Tree of Thoughts. These patterns elicit intermediate reasoning, search multiple paths, and apply self-consistency to avoid single-pass failures.

Choosing between ReAct, PAL, and Reflexion for LLM prompting depends on your task: ReAct grounds information gathering with tools, PAL aids computation via code, and Reflexion drives iterative improvement from failures. A decision matrix guides the optimal selection.

You need Tree of Thoughts or Least-to-Most decomposition for multi-hop QA when single-pass answers fail and the problem requires exploring or breaking down multiple reasoning paths. These techniques decompose complex constraints into manageable intermediate steps.

Yes, Chain-of-Thought with self-consistency works for symbolic manipulation problems by generating multiple structured reasoning paths and selecting the most consistent final answer. This approach significantly increases reliability over standard zero-shot prompting.

Standard LLM prompting fails on difficult logic problems because it lacks structured intermediate reasoning steps. You can fix this by applying failure-driven Reflexion for iterative improvement or using PAL for code-aided computation to verify logic.