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Prompt Engineering Guide - Coggle Diagram

- - - - Chain-of-Thought Prompting
        
        Question 1: How does chain-of-thought (CoT) prompting improve complex reasoning capabilities?
        Answer 1: Chain-of-thought prompting enables complex reasoning capabilities by incorporating intermediate reasoning steps. It allows the model to perform reasoning before generating responses, which leads to better results on more complex tasks. CoT prompting can be combined with few-shot prompting for enhanced performance.
        Question 2: What is the impact of providing reasoning steps in the prompt for solving complex tasks?
        Answer 2: Providing reasoning steps in the prompt improves the model's ability to solve complex tasks. By breaking down the problem into steps and demonstrating them to the model, it gains a deeper understanding and can generate more reliable responses. Even a single example with reasoning steps can yield accurate results.
        Question 3: How does Auto-CoT address the manual effort involved in chain-of-thought prompting?
        Answer 3: Auto-CoT is an approach proposed by Zhang et al. that eliminates manual efforts in chain-of-thought prompting. It leverages large language models (LLMs) and the "Let's think step by step" prompt to automatically generate reasoning chains for demonstrations. It involves question clustering and demonstration sampling stages to construct diverse demonstrations with the help of simple heuristics. This approach mitigates mistakes in generated chains and reduces the need for manual crafting of examples.
        
        Self-Consistency
        
        Question: What is self-consistency in CoT prompting and how does it enhance the accuracy of responses?
        Answer: Self-consistency in CoT prompting refers to a technique where multiple reasoning paths are generated for a problem instead of just one. These different paths are aggregated to find the most consistent answer, resulting in improved accuracy of responses. It is an unsupervised method that does not require additional human input or models. The language chain manages prompt modifications to support self-consistency, relieving the requesting model or user from the need to remember or be aware of the specific CoT prompt modifiers. Templated reasoning simplifies the implementation of self-consistency.
      - Question 1: What is few-shot prompting and how does it enable in-context learning?
        Answer 1: Few-shot prompting is a technique that involves providing demonstrations or examples in the prompt to steer the model towards better performance. It allows for in-context learning, where the model learns from the demonstrations provided and uses them as conditioning for generating responses in subsequent examples.
        Question 2: What are the limitations of standard few-shot prompting?
        Answer 2: Standard few-shot prompting works well for many tasks but has limitations, especially when dealing with more complex reasoning tasks. It may not yield reliable responses for tasks that involve multiple reasoning steps. In such cases, more advanced prompt engineering techniques, such as chain-of-thought prompting, may be require