Prompt the LLM to introspect on its output and identify any potential errors or areas for improvement, enhancing quality and accuracy.
A prompting method that instructs the LLM to traverse many different paths when completing a task. Movie recommender is used as the example task. Update the variables and steps for your use case.
Faithful Chain-of-Thought ensures reasoning chains accurately reflect the model's thought process by converting natural language queries into symbolic reasoning chains with Python, and then uses a deterministic solver to find the final answer.
Skeleton of Thought (SoT) typically uses 2 parallel prompts. This one-shot prompt merges them: first forming a task skeleton, then filling it in. Just update the question and run the prompt!
Input your prompt in the variable and it will be converted into a new prompt, following the Algorithm of Thoughts framework. A final, cohesive, prompt will be below the AoT framework output.
