influence the quality of the generated text, evaluating the final output is typically a separate step after the generation process. RAG leverages two main components: Retriever: As mentioned earlier, this component identifies relevant passages from a large text corpus based on the input query. These passages act as contextual information for the generative component. Generator: This component utilizes the retrieved passages along with the input query to generate the final text output. The retrieved passages help the generator produce more relevant and coherent text. When considering access control for OCI Generative AI resources, what role does IAM play?
database contribute to the retrieval process?
c) Data uploaded to OCI Generative AI becomes publicly accessible for collaboration: Public accessibility would be a major security risk. ;OCI Generative AI should provide access control mechanisms. d) Users retain full control over data location and access within the cloud: While some level of user control might be offered (e.g., IAM policies), OCI Generative AI likely manages the underlying infrastructure and enforces certain security measures by default. Here's how OCI Generative AI likely contributes to secure data lifecycle: Encryption: Data is automatically encrypted at rest (when stored within OCI) and in transit (when transferred between systems) using industry -standard encryption algorithms. This helps safeguard data confidentiality even in case of a security breach. Access Control: As discussed previously, IAM allows you to define granular access permissions for users and groups, ensuring only authorized personnel can access your custom datasets within OCI Generative AI. Data Isolation: OCI Generative AI might isolate your data from other users' data, minimizing the risk of unauthorized access or exposure. Secure Communication: Secure communication protocols are likely used to ensure data integrity and prevent tampering during transmission. By leveraging these security features, OCI Generative AI helps you maintain control over your custom datasets and minimizes the risk of data breaches or unauthorized access throughout the data lifecycle, from upload to model training and deployment. It's important to consult the official OCI Generative AI documentation for the most up-to-date information on specific data security practices and any user responsibilities related to handling custom datasets within the service. During LLM fine -tuning, which layers of the model are typically adjusted the most?
pre-training. This approach helps the model achieve better performance on the specific task compared to training a completely new network from scratch. Which of the following statements accurately describes the type of large language models (LLMs) offered by OCI Generative AI?
between words to find documents that align with the query's intent, even if the exact keywords aren't present. C. Ranking results based on their popularity or social media engagement: While these factors might influence search results on some platforms, they don't directly reflect semantic similarity. Semantic search focuses on meaning, not popularity. D. Prioritizing documents based on the author's credibility: Author credibility can be a factor in evaluating search results, but it's not the core principle of semantic search. The focus is on identifying documents that are semantically relevant to the query. Therefore, the core principle behind semantic search used in LLM applications is: B. Identifying documents with similar meaning or intent, even if phrased differently What is the primary benefit of using pre-trained base models for fine-tuning in OCI Generative AI Service?
In the context of LLMs, what is the primary function of the self -attention mechanism?
