In summary, inference-stage enhancement methods offer
the advantages of being lightweight, cost-effective, requir-
ing no additional training, and utilizing powerful pre-trained
models. The main strength lies in freezing the parameters
of the LLMs during fine-tuning, focusing on providing con-
text that better suits the requirements, with the characteristics
of being fast and low-cost. However, this approach also has
some limitations, including the need for additional data pro-
cessing and process optimization, while being constrained by
the foundation model’s capabilities. Typically, this method
is often combined with process optimization techniques such
as step-wise reasoning , iterative reasoning, and adaptive re-
trieval to better meet the requirements of different tasks.
6.2 Augmentation Data Source
Data source is crucial factors for RAG effectiveness. Vari-
ous data sources offer distinct granularities and dimensions
of knowledge, requiring different processing methods. They
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