Self Corrective RAG

Accelerating Code Generation with Automated Error Handling

Traditional coding methods often lead to errors due to misunderstandings or lack of context, requiring manual review and correction. To streamline this process, LangGraph can be utilized to encode checks and automate fixes, significantly reducing the time spent on code generation.

Designing Effective Error Handling

Error handling should focus on simple assertions to minimize costs and latency. For instance, ChatLangChain, a question-answer system, sometimes incorrectly generates import statements, degrading user experience. To address this, a LangGraph control flow can be designed to test imports and pass errors back to the LLM for correction, improving performance.

Advanced Error Handling with LLMs

In cases where simple assertions are insufficient, LLMs can be employed to perform testing. For example, RAG systems may suffer from hallucinations or low-quality retrieval. To combat this, LLM-as-judge evaluators can be used to assess retrieval relevance, answer hallucination, and answer usefulness.

Self-Corrective RAG Application with LLM Error Handling

A self-corrective RAG application can be designed with multiple stages of error handling using LangGraph. The control flow is explicitly defined, with each node assigned a specific task:

1. Retrieval: Asserts the relevance of retrieved documents.

2. Answer Generation: Generates answers based on relevant documents.

3. Hallucination Check: Verifies answers for hallucinations related to retrieved documents.

4. Answer Usefulness: Evaluates whether answers address the user's question.

By leveraging LLMs for error handling, developers can create more robust and efficient coding systems, reducing the need for manual review and correction.