An Adaptive Knowledge-Enhanced Framework Based on RAG: A Study on Improving English Teaching Effectiveness

Large language models (LLMs) with the Transformer architecture as the core have made significant progress in the field of natural language processing, and their application value in English teaching has also attracted much attention. In tasks such as text generation, question-answering systems, and translation, the processing capabilities of LLMs have significantly improved. However, existing LLMs have problems such as insufficient coverage of professional knowledge, rough semantic parsing, and weak personalized services. To address the aforementioned issues, this study proposes a dual-path retrieval-enhanced generation scheme that integrates vector databases and intelligent agents, aiming to improve the application of large models in English language teaching. Semantic retrieval of unstructured data in English teaching is realized through vector databases, knowledge is dynamically acquired by combining agents, and the accuracy is improved by using Bloom filters to fuse dual-path retrieval. At the same time, the retrieval efficiency is optimized by an importance-oriented algorithm, and user profiles are constructed based on multi-dimensional data to achieve personalized adaptation. Experiments show that the maximum optimization of the retrieval time of this scheme can reach 26.32%, and the highest retrieval accuracy can reach 86%. The key indicators and scores in tasks such as English knowledge retrieval and question-answering reasoning are better than those of the comparative schemes, providing an effective technical path for intelligent English teaching.