However, their strength lies in interpreting and manipulating code within the context of the surrounding natural language instructions or comments. In the context of Oracle Cloud Infrastructure (OCI), how might LLM fine -tuning be beneficial?
C. By allowing for parallel processing of different parts of the generation pipeline. While the other options might have some bearing on efficiency, parallel processing is a key strength of LangChain:
In a Transformer network, what is the role of the encoder -decoder pair?
What issue might arise from using small data sets with the Vanilla fine-tuning method in the OCI Generative AI service?
c) RAG eliminates the need for training data altogether, relying solely on retrieval: RAG still utilizes an LLM for text generation, so some training data is necessary. However, the retrieved information can supplement the LLM's knowledge and potentially improve results even with a smaller dataset. d) The impact of RAG on training data requirements depends solely on the retrieval component: While the retrieval component is important, the effectiveness of RAG also depends on the quality and size of the training data for the LLM. Here's how RAG can potentially reduce training data requirements: Contextual Guidance: Retrieved passages provide additional context for the LLM during generation. This can help the LLM produce more accurate and relevant outputs even with a limited dataset for its specific task. Factual Augmentation: The retrieved information can supplement the factual knowledge of the LLM, reducing the need for a massive dataset to cover all possible factual scenarios. In an LLM application that utilizes both RAG and LangChain, how does RAG contribute to the overall generation process?
B. Using a factual and objective tone for the prompt. C. Rephrasing the prompt if the initial LLM output is unsatisfactory. D. Injecting personal opinions or biases into the prompt. Explanation: D. Injecting personal opinions or biases into the prompt. Here's why the other options are good practices:
