Advanced RAG techniques

I've recently had the privilege of attending a training program called AI Devs, where I learned practical implementation of Generative AI (Gen AI). One critical insight from this training is that merely having access to Gen AI does not provide a competitive edge. Instead, enterprises must focus on implementing effective and scalable Retrieval-Augmented Generation (RAG) techniques to truly harness the power of Gen AI. The complexities of data search and retrieval became particularly evident to me while working on my own personal assistant project, which you can explore i my portfolio here: Team X. I will publish a separate post about my adventure and joy of creating this applicaiton. Stay tuned, but let's come back now to RAG.

In the evolving world of AI and machine learning, Retrieval-Augmented Generation (RAG) is seen as a powerful technique to enhance the performance and accuracy of language models. By integrating external information retrieval with generative models, RAG can provide more accurate, relevant, and contextually appropriate responses. Currently many enterprises are building they own implementation of RAGs to incorporate it in their product portfolio.

In thsi post I summarize some advanced RAG techniques that I have learned about when working on my personal AI powered assistant.

Self-Questioning

One of the effective technique involves self-questioning. When inputting information (answers) into the RAG system, the Large Language Model (LLM) generates and stores additional questions related to the provided answers. This approach ensures that the model not only retrieves relevant information but also anticipates future queries, thereby enriching the knowledge base and enhancing the retrieval process.

Reranking

Reranking is another critical component in the RAG framework. After performing a vector search, a reranking model evaluates the retrieved results to determine their relevance to the user’s query. This can be realized through prompts like, "Is this information relevant to the user's query?" Reranking refines the search output, ensuring that the most pertinent information is prioritized and presented to the user.

Rephrasal

To maximize the utility of the retrieved data, rephrasal plays a vital role. This technique involves rephrasing the input data within the RAG system, making it more accessible and useful for retrieval by the LLM. By standardizing the format and language of the stored information, rephrasal ensures that the data can be effectively and accurately retrieved in response to diverse queries.

Hierarchization

Creating a hierarchy of information through hierarchization and establishing a graph of relations among the data points can significantly enhance the retrieval process. This structured approach organizes information into multiple levels, facilitating easier navigation and more efficient retrieval. Hierarchization helps in understanding the context and relationship between different pieces of information, thus improving the relevance and accuracy of the responses generated by the model.

Summarization on Multiple Levels

Summarization on multiple levels involves creating summaries of information at different levels of the hierarchy. This multi-tiered summarization makes it easier to find and retrieve the necessary information, as users can quickly navigate through summarized content before delving into detailed data. This technique ensures that the retrieval process is both time-efficient and effective.

Tagging

Tagging is the act of assigning specific tags to input information, enabling better categorization and retrieval. By tagging data with relevant keywords, the RAG system can more accurately index and retrieve information based on user queries. This enhances the precision of the search results and aids in quicker access to the desired information.

Hybrid Search

Incorporating hybrid search mechanisms, which combine vector databases with traditional relational databases, can greatly improve the effectiveness of the RAG system. While vector databases excel at handling unstructured data and semantic searches, relational databases are adept at managing structured data. Utilizing both allows for a more comprehensive search process, ensuring that no relevant information is overlooked.

Chunking Strategies

Effective chunking strategies are essential to ensure that no important data is lost during the retrieval process. Depending on the input data, different chunking methods can be employed:

• Fixed size chunks: Dividing data into equal-sized segments.
• By paragraph or header: Chunking based on natural divisions in the text, such as paragraphs or headers.
• Chunking with overlap: Creating overlapping chunks to preserve context across segments. Selecting the appropriate chunking mechanism based on the nature of the input data ensures that the information remains coherent and complete during retrieval.

Having access to LLMs is not enough to have a competetive advantage, everyone can have it. Companies need to build effective RAGs to beat the market and competitors.

By implementing mentioned in article advanced RAG techniques, we can significantly enhance the accuracy, relevance, and efficiency of information retrieval processes, leading to more robust and effective AI-driven solutions.