Awesome Prompt Engineering

Learn how to use Awesome Prompt Engineering effectively! Here are a few example prompts, tips, and the documentation of available commands.

Example prompts

1. Prompt 1: "Can you help me revise my prompt better by using Few-shot prompting?"

2. Prompt 2: "I need assistance with Chain-of-Thought (CoT) Prompting."

3. Prompt 3: "How can I use Self-Consistency Prompting effectively?"

4. Prompt 4: "Tell me more about Tree of Thoughts (ToT) Prompting."

5. Prompt 5: "I would like to learn about ReAct Prompting."

Features and commands

1. Few-shot prompting: This feature helps you improve your prompts by providing examples or demonstrations. It allows you to specify a few examples of desired behavior to guide the model's response.

2. Chain-of-Thought (CoT) Prompting: This technique allows you to extend conversations or prompts by building on previous responses. You can use this to create more interactive and dynamic interactions with the model.

3. Self-Consistency Prompting: This approach involves using the model's own output as input to create a feedback loop. It helps in refining or iterating on a prompt by incorporating the model's suggestions or responses back into the input.

4. Tree of Thoughts (ToT) Prompting: With this technique, you can explore different branches or possibilities within a prompt by structuring it in a hierarchical manner. It enables the model to generate diverse outputs based on different paths or options provided.

5. ReAct Prompting: ReAct Prompting allows you to guide the model's behavior by giving feedback or reactions to its responses. You can reinforce desirable behaviors or correct and guide the model towards better outputs.

Note: The above features and commands are general descriptions and may not represent all the specific functionalities of the Awesome Prompt app. Please refer to the official documentation for more details on each feature and command.