edfas.org 23 ELECTRONIC DEVICE FAILURE ANALYSIS | VOLUME 27 NO. 4 CoT and selects the most consistent one, improving reasoning diversity.[9] RETRIEVAL-AUGMENTED GENERATION Retrieval-augmented generation (RAG) is a prompting method that incorporates data from external data stores into the generation process. A general RAG approach consists of three high-level components:[10] Retrieval, which extracts relevant documents for a query using various strategies; Augmentation, which combines the query with retrieved data to create a new prompt; and Generation, which produces consistent responses based on the augmented query. RAG improves LLMs by providing up-to-date knowledge through easily updated data stores,[11] enabling verifiable generations by citing re- trieved documents,[12 reducing hallucinations by grounding responses in relevant data, and offering scalable adaptation by avoiding frequent retraining and achieving parameter efficiency.[13] Figure 1 illustrates a basic RAG workflow for FA. An engineer submits a question, and RAG retrieves upto-date information by embedding the query and searching a vector database for relevant data chunks. The database, pre-populated with embeddings from the data source, returns the top-k results based on similarity to the prompt. The results are combined with the query using a prompt template, and the augmented prompt is sent to the LLM generator for a useful answer. Naive RAG, as shown in Fig. 1, uses a simple sequential workflow that often suffers from imprecise retrieval, irrelevant prompts, and hallucinations. Therefore, Advanced RAG methods introduce pre- and post-retrieval strategies to improve accuracy and context. Finally, Modular RAG employs flexible, interconnected modules to address specific challenges, blending retrieval, augmentation, and generation processes. RAG systems can be optimized at various stages, including indexing, pre-retrieval, retrieval, post-retrieval, generation, and orchestration, outlined in Fig. 2. Each stage offers opportunities for improvement to enhance the overall performance of the RAG system. The main Fig. 1 Naive RAG architecture for FA using an internal ChatGPT 3.5 instance. Fig. 2 Classification of RAG features.
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