Advanced Prompting: Tree of Thoughts and Self-Consistency

Standard prompting works for simple tasks. For complex logic, you need algorithms that structure the model’s thinking process.

1. Self-Consistency (Majority Voting)

LLMs are probabilistic. If you ask a hard math question once, it might get it wrong.

• The Technique: Ask the same question 5 times (with a non-zero temperature).
• The Result: Take the most common answer. This simple trick improves math and logic performance by 10-20%.

2. Tree of Thoughts (ToT)

Instead of generating one linear answer, ask the model to explore multiple “paths.”

• Step 1: Generate 3 possible next steps.
• Step 2: Evaluate each step (Good/Bad).
• Step 3: Discard the bad ones and continue from the best one.
• Use Case: Strategic planning, coding architecture, or creative writing.

3. Prompt Chaining

Don’t ask for a miracle in one prompt. Break it down.

• Chain: Input -> [Summarizer] -> [Analyzer] -> [Critique] -> [Final Editor] -> Output.
• Benefit: Each step is simpler and easier to debug.

Summary

Advanced prompting treats the LLM as a Search Engine for Thoughts. By exploring multiple possibilities and breaking down tasks, you can achieve reasoning capabilities that far exceed the raw model’s baseline.