Prompt chaining is a technique used to improve the performance and reliability of large language models (LLMs) by breaking down complex tasks into smaller, more manageable steps. In this method, you give the LLM one small task (or subtask), and its response is used as input for the next prompt in the chain. This allows the LLM to handle a series of simpler tasks rather than struggling with one big, complicated prompt all at once.

Why is Prompt Chaining Useful?

When you have a complex task, asking the LLM to handle everything in one go might lead to errors or confusion. By splitting the task into steps, prompt chaining makes it easier for the LLM to give accurate and well-structured responses. It also makes it easier to find and fix any issues during the process, and you can analyze the performance at each stage.

This approach is especially helpful when building chatbots or assistants that need to provide personalized and user-friendly experiences.

Example of Prompt Chaining: Document Question-Answering

Imagine you need the LLM to answer a question based on a large document. If you ask it directly, it might give a weak or incomplete answer. But with prompt chaining, you can break it into two smaller steps:

1. Step 1: Extract Relevant Information
   First, create a prompt that asks the LLM to pull out quotes from the document that are relevant to the question.

   Prompt 1:
   ```
   You are a helpful assistant. Please extract quotes from the document that are relevant to the given question. If no quotes are found, say "No relevant quotes found!"

   {{document}}

   ```

   Example Response:
   ```
   <quotes>
   - Chain-of-thought (CoT) prompting[27]
   - Generated knowledge prompting[37]
   - Self-consistency decoding[39]
   </quotes>
   ```

2. Step 2: Answer the Question Using the Quotes
   Now, create another prompt that uses the quotes to answer the question in a clear and friendly way.

   Prompt 2:
   ```
   Using the relevant quotes (within <quotes></quotes>) and the document (within ####), please provide an answer to the question. Make sure the answer is helpful and friendly.

   {{document}}

   <quotes>
   - Chain-of-thought (CoT) prompting[27]
   - Generated knowledge prompting[37]
   - Self-consistency decoding[39]
   </quotes>
   ```

   Example Response:
   ```
   The document mentions several techniques, including Chain-of-thought (CoT) prompting, Generated knowledge prompting, and Self-consistency decoding, which are all methods used to improve interactions with large language models.
   ```

By chaining these two steps together, the LLM is more likely to provide a thorough and accurate response. You can also refine the process further, like designing another prompt to clean up the quotes before generating the final answer.


Summary

Prompt chaining helps divide a big task into smaller steps, making it easier for LLMs to handle complex tasks. This approach not only improves performance but also makes it easier to understand and fix problems if something goes wrong. It’s a great method for applications like chatbots and document question-answering.
