Given a set of relevant quotes (delimited by <quotes></quotes>) extracted from a document and the original document (delimited by ####), please compose an answer to the question. Ensure that the answer is accurate, has a friendly tone, and sounds helpful.
####
{{document}}
####
<quotes>
- Chain-of-thought (CoT) prompting[27]
- Generated knowledge prompting[37]
- Least-to-most prompting[38]
- Self-consistency decoding[39]
- Complexity-based prompting[41]
- Self-refine[42]
- Tree-of-thought prompting[43]
- Maieutic prompting[45]
- Directional-stimulus prompting[46]
- Textual inversion and embeddings[59]
- Using gradient descent to search for prompts[61][62][63][64]
- Prompt injection[65][66][67]
</quotes>
Output of Prompt 2:
The prompting techniques mentioned in the document include:
1. Chain-of-thought (CoT) prompting[27]
2. Generated knowledge prompting[37]
3. Least-to-most prompting[38]
4. Self-consistency decoding[39]
5. Complexity-based prompting[41]
6. Self-refine[42]
7. Tree-of-thought prompting[43]
8. Maieutic prompting[45]
9. Directional-stimulus prompting[46]
10. Textual inversion and embeddings[59]
11. Using gradient descent to search for prompts[61][62][63][64]
12. Prompt injection[65][66][67]
Each of these techniques employs unique strategies to enhance or specify the interactions with large language models to produce the desired outcomes.
As you can see, simplifying and creating prompt chains is a useful prompting approach where the responses need to undergo several operations or transformations. As an exercise, feel free to design a prompt that removes the citations (e.g., [27]) from the response before sending this as a final response to the user of your application.
You can also find more examples of prompt chaining in this documentation that leverages the Claude LLM. Our example is inspired and adapted from their examples.https://www.promptingguide.ai/techniques/prompt_chaining
####
{{document}}
####
<quotes>
- Chain-of-thought (CoT) prompting[27]
- Generated knowledge prompting[37]
- Least-to-most prompting[38]
- Self-consistency decoding[39]
- Complexity-based prompting[41]
- Self-refine[42]
- Tree-of-thought prompting[43]
- Maieutic prompting[45]
- Directional-stimulus prompting[46]
- Textual inversion and embeddings[59]
- Using gradient descent to search for prompts[61][62][63][64]
- Prompt injection[65][66][67]
</quotes>
Output of Prompt 2:
The prompting techniques mentioned in the document include:
1. Chain-of-thought (CoT) prompting[27]
2. Generated knowledge prompting[37]
3. Least-to-most prompting[38]
4. Self-consistency decoding[39]
5. Complexity-based prompting[41]
6. Self-refine[42]
7. Tree-of-thought prompting[43]
8. Maieutic prompting[45]
9. Directional-stimulus prompting[46]
10. Textual inversion and embeddings[59]
11. Using gradient descent to search for prompts[61][62][63][64]
12. Prompt injection[65][66][67]
Each of these techniques employs unique strategies to enhance or specify the interactions with large language models to produce the desired outcomes.
As you can see, simplifying and creating prompt chains is a useful prompting approach where the responses need to undergo several operations or transformations. As an exercise, feel free to design a prompt that removes the citations (e.g., [27]) from the response before sending this as a final response to the user of your application.
You can also find more examples of prompt chaining in this documentation that leverages the Claude LLM. Our example is inspired and adapted from their examples.https://www.promptingguide.ai/techniques/prompt_chaining