Retrieval with chain-of-thought reasoning is a powerful technique for handling complex QA tasks in RAG systems. It allows the system to perform step-by-step reasoning, guided by retrieved information, to arrive at a comprehensive and accurate answer. By exploring different variations and levels of complexity, you can tailor this approach to the specific requirements of your RAG application.

For complex question-answering (QA) tasks, especially those that require multi-step reasoning or involve finding information from multiple sources, combining retrieval with chain-of-thought reasoning can be very effective.

This technique differs from simple query decomposition in that the reasoning steps are not generated upfront. Instead, the system iteratively retrieves information and performs reasoning based on the retrieved context.

Here's a typical process:

- Initial Retrieval: The system first retrieves relevant documents or chunks based on the initial user query.
- CoT Prompting: The LLM is then prompted with the query and the retrieved context, and it's instructed to generate a reasoning sentence (T1) that represents an intermediate step towards finding the answer.
- Retrieval for Reasoning Step: The system retrieves new documents or chunks relevant to the reasoning sentence (T1).
- Loop Continuation: Steps 2 and 3 are repeated, generating new reasoning sentences (T2, T3, etc.) and retrieving corresponding information until a termination condition is met.
- Termination Condition: The loop typically terminates when the system finds the answer to the original query within the retrieved context. This could be determined by checking if the answer is a substring of the retrieved text or by using other criteria.


Example:

User Query: "What are the advantages of using Weights & Biases for experiment tracking compared to using a spreadsheet?"

Steps:

- Initial Retrieval: Retrieve documents related to Weights & Biases and experiment tracking.
- T1 (Reasoning): "Weights & Biases offers automatic logging of experiment data, while spreadsheets require manual entry."
- Retrieve for T1: Find documents about automatic logging and manual data entry.
- T2 (Reasoning): "Automatic logging saves time and reduces errors compared to manual entry."
- Retrieve for T2: Find documents discussing the benefits of automatic logging.
- Termination: The retrieved documents likely contain information about the advantages of Weights & Biases (e.g., time-saving, error reduction), so the loop terminates.


Complexities and Variations:

- Number of Reasoning Steps: The complexity can be adjusted by controlling the number of reasoning steps allowed before termination.
- Types of Reasoning: The reasoning steps can involve different types of logic or inference, depending on the task.
- Retrieval Methods: Different retrieval methods (e.g., dense retrieval, keyword-based retrieval) can be used within the loop.
