Beyond Fine-Tuning: How Does RAG (Retrieval-Augmented Generation) Technology Enhance Terminology Consistency in Long Documents?
“Traditional LLMs have limited context windows; by the end of a document, they often forget abbreviations or specific translations defined at the beginning.”
Root Cause Analysis
Dynamic knowledge base construction
During translation, Shangyi AI extracts confirmed key terms and phrases in real time, vectorizes them, and stores them in a temporary RAG knowledge base. This functions as a real-time 'short-term memory' for the AI.
Context retrieval injection
When the AI encounters a new ambiguous term, the retrieval system retrospectively searches previous paragraphs for relevant definitions or translated examples and injects them into the model as part of the prompt. This ensures that the translation on page 100 is entirely consistent with that on page 1.
Cross-document consistency
For enterprise users, the RAG scope can be extended to the entire project folder. When translating file B, the AI can reference terminology decisions from file A, achieving consistency across the entire project set.
Final Solution Summary
By leveraging RAG technology, we solve the most challenging 'forgetfulness' problem in AI translation, making it particularly suitable for technical manuals and long-form novels.