RAG (Retrieval-Augmented Generation) Sequence models combine retrieval-based search with LLM-generated responses, ensuring factually grounded and contextually relevant outputs.
How the RAG Sequence Model Works:
Retrieves multiple documents for an input query.
Uses all retrieved documents collectively to generate a well-informed response.
Ensures the answer is contextually aware and factually accurate.
Why Other Options Are Incorrect:
(A) is incorrect because RAG does not ignore part of the query.
(B) is incorrect because it does not rely on a single document.
(C) is incorrect because RAG does not modify the input query but focuses on retrieval and generation.
🔹 Oracle Generative AI Reference:
Oracle AI implements RAG-based architectures to enhance LLM-generated responses by retrieving and grounding responses in factual data.

Fine-tuned customer models in the OCI Generative AI service are stored in Object Storage, and they are encrypted by default. This encryption ensures strong data privacy and security by protecting the model data from unauthorized access. Using encrypted storage is a key measure in safeguarding sensitive information and maintaining compliance with security standards.
Reference
OCI documentation on data storage and security practices
Technical details on encryption and data privacy in OCI services

In the simplest form of keyword-based search, documents are evaluated based on keyword matching and term frequency. This approach does not account for context, semantics, or the meaning behind the words, but rather focuses on:
Presence of Keywords - If a document contains the search term, it is considered relevant.
Term Frequency (TF) - The more a keyword appears in a document, the higher the ranking in basic search algorithms.
Inverse Document Frequency (IDF) - Words that are common across many documents (e.g., "the," "is") are given less weight, while rare words are prioritized.
Boolean Matching - Some basic search engines support logical operators like AND, OR, and NOT to refine keyword searches.
Exact Match vs. Partial Match - Some systems prioritize exact keyword matches, while others allow partial or fuzzy matches.
🔹 Oracle Generative AI Reference:
Oracle has implemented semantic search and advanced AI-driven document search techniques in its cloud solutions, but traditional keyword-based search still forms the foundation of many enterprise search mechanisms.