Technology
Title: In-depth Analysis of RAG Technology: The Key to Optimizing the Accuracy of Q&A Applications.
Introduction: In the age of artificial intelligence, the accuracy of Q&A applications is crucial to their utility. Recently, the developers of OpenAI shared a success story of using RAG technology to improve the accuracy of Q&A in the financial field from the original 45% to an astonishing 98%. This has led to a deep interest in RAG technology. This article will provide readers with a comprehensive and profound understanding of RAG technology, from its basic concepts to the various aspects of the pipeline, as well as ways to improve accuracy.
i.Background: Interpreting "40%".
a.Successful cases in the financial sector.
RAG technology is a specific case of improving the accuracy of Q&A in the financial field.
Why is the difference between the original accuracy rate and the improved value 40%?
b.The impact of large models in the AI era.
Proportion of large model capabilities and space for application developers.
Optimism vs. pessimism about the capabilities of large models.
ii.RAG Technology Overview.
a.Definition and metaphor of rag.
The criticality of RAG technology in Q&A applications.
Compare the large model with the RAG pipeline to deepen the understanding.
b.The importance of RAG and the field of application.
The value of RAG technology in improving the accuracy of Q&A.
RAG is widely used in different fields.
c.Difficulties and challenges of RAG technology.
The ease of getting started with RAG and the difficulty of fine operation.
OpenAI's evaluation and cognition of RAG technology.
iii.RAG pipeline overview.
a.RAG is not a single technology, but an assembly line.
The overall architecture of RAG technology.
The role and goal of the pipeline.
b.The various links of the assembly line and their analogies.
Data preparation: Ensure the integrity of data and generate new dataThe role of new data generation in improving accuracy.
Chunking: Splitting data, aligning granularity, the importance and practical operation of aligning granularity.
Vectorization (embedding): The concept of transforming content into a vector vector that can be processed by a computer and its application in RAG.
Indexing: Classifying, organizing, and establishing indexes after vectorization of data blocks.
Retriever: Find the key role and optimization method of relevant data retrieval in the database.
The first round of retrieval evaluation: evaluate the matching degree of the retrieval results, evaluate the importance and practical operation of the retrieval link for the pipeline
iv.RAG tricks.
a.Methods and means to improve the RAG assembly line.
In the optimization stage of data preparation, it is suggested that the large model is used to summarize the actual effect of the data.
Create a more complete dataset to enhance model training.
Comparison of technology selection and fine-tuning in the vectorization phase with the direct use and fine-tuning of existing models.
Comparison of the effectiveness of using a mature language vector model with fine-tuning and then using it.
How to optimize the algorithm in the retrieval link to find the data blocks with a high degree of matching to the question more accurately.
Optimize the algorithm to improve retrieval efficiency and accuracy.
Methods of evaluation of search results: comparison and selection of manual evaluation and machine evaluation.
Hands-on experience in machine evaluation with GPT4-TURBO.
b.Advice and practical experience for each link.
Data preparation: The technical and cost considerations of new data generation, and the actual effect of using large models to summarize and label data.
Recommendations for strategies and practices for new data generation.
Vectorization: Select appropriate vector models and fine-tuning strategies to evaluate the performance of different vector models.
Fine-tune the selection of strategies and practical recommendations.
Retrieval: Optimize the algorithm to improve the retrieval efficiency, optimize the actual effect of the algorithm and case sharing.
A method that uses machine learning techniques to improve the accuracy of searches.
Evaluation of search results: Suggested approach to machine assessment: Advantages and limitations of machine assessment.
How to combine manual and machine evaluations for a comprehensive assessment.
v.Conclusion
a.Summarize the importance and application value of RAG.
The unique value of RAG technology in Q&A applications.
The practical effect of RAG technology on improving accuracy.
b.Emphasize the fine grinding and optimization of all aspects of the assembly line.
The criticality and interconnectedness of each link.
The direct impact of fine polishing on the performance of Q&A applications.
Conclusion:Through the in-depth analysis of RAG technology, we not only understand its basic concepts and various aspects of the pipeline, but also obtain practical tips to improve the accuracy of Q&A applications。The success stories and applications of RAG technology in the financial sector show us its strong potential. In the development of artificial intelligence, the deep understanding and continuous optimization of RAG technology will provide strong support for us to create more intelligent and efficient Q&A applications. In practice, we need to continuously pay attention to the innovation and improvement of each link to adapt to the changing application scenarios and achieve the best performance of the Q&A application.