Which is a key characteristic of Large Language Models (LLMs) without Retrieval Augmented
Generation (RAG)?
B
Explanation:
Comprehensive and Detailed In-Depth Explanation=
LLMs without Retrieval Augmented Generation (RAG) depend solely on the knowledge encoded in
their parameters during pretraining on a large, general text corpus. They generate responses
basedon this internal knowledge without accessing external data at inference time, making Option B
correct. Option A is false, as external databases are a feature of RAG, not standalone LLMs. Option C
is incorrect, as LLMs can generate responses without fine-tuning via prompting or in-context
learning. Option D is wrong, as vector databases are used in RAG or similar systems, not in basic
LLMs. This reliance on pretraining distinguishes non-RAG LLMs from those augmented with real-time
retrieval.
: OCI 2025 Generative AI documentation likely contrasts RAG and non-RAG LLMs under model
architecture or response generation sections.
What do embeddings in Large Language Models (LLMs) represent?
C
Explanation:
Comprehensive and Detailed In-Depth Explanation=
Embeddings in LLMs are high-dimensional vectors that encode the semantic meaning of words,
phrases, or sentences, capturing relationships like similarity or context (e.g., "cat" and "kitten" being
close in vector space). This allows the model to process and understand text numerically, making
Option C correct. Option A is irrelevant, as embeddings don’t deal with visual attributes. Option B is
incorrect, as frequency is a statistical measure, not the purpose of embeddings. Option D is partially
related but too narrow—embeddings capture semantics beyond just grammar.
: OCI 2025 Generative AI documentation likely discusses embeddings under data representation or
vectorization topics.
What is the function of the Generator in a text generation system?
C
Explanation:
Comprehensive and Detailed In-Depth Explanation=
In a text generation system (e.g., with RAG), the Generator is the component (typically an LLM) that
produces coherent, human-like text based on the user’s query and any retrieved information (if
applicable). It synthesizes the final output, making Option C correct. Option A describes a Retriever’s
role. Option B pertains to a Ranker. Option D is unrelated, as storage isn’t the Generator’s function
but a separate system task. The Generator’s role is critical in transforming inputs into natural
language responses.
: OCI 2025 Generative AI documentation likely defines the Generator under RAG or text generation
workflows.
What differentiates Semantic search from traditional keyword search?
C
Explanation:
Comprehensive and Detailed In-Depth Explanation=
Semantic search uses embeddings and NLP to understand the meaning, intent, and context behind a
query, rather than just matching exact keywords (as in traditional search). This enables more relevant
results, even if exact terms aren’t present, making Option C correct. Options A and B describe
traditional keyword search mechanics. Option D is unrelated, as metadata like date or author isn’t
the primary focus of semantic search. Semantic search leverages vector representations for deeper
understanding.
: OCI 2025 Generative AI documentation likely contrasts semantic and keyword search under search
or retrieval sections.
What does the Ranker do in a text generation system?
C
Explanation:
Comprehensive and Detailed In-Depth Explanation=
In systems like RAG, the Ranker evaluates and sorts the information retrieved by the Retriever (e.g.,
documents or snippets) based on relevance to the query, ensuring the most pertinent data is passed
to the Generator. This makes Option C correct. Option A is the Generator’s role. Option B describes
the Retriever. Option D is unrelated, as the Ranker doesn’t interact with users but processes
retrieved data. The Ranker enhances output quality by prioritizing relevant content.
: OCI 2025 Generative AI documentation likely details the Ranker under RAG pipeline components.
What is the function of "Prompts" in the chatbot system?
B
Explanation:
Comprehensive and Detailed In-Depth Explanation=
Prompts in a chatbot system are inputs provided to the LLM to initiate and steer its responses, often
including instructions, context, or examples. They shape the chatbot’s behavior without altering its
core mechanics, making Option B correct. Option A is false, as knowledge is stored in the model’s
parameters. Option C relates to the model’s architecture, not prompts. Option D pertains to memory
systems, not prompts directly. Prompts are key for effective interaction.
: OCI 2025 Generative AI documentation likely covers prompts under chatbot design or inference
sections.
What is LCEL in the context of LangChain Chains?
C
Explanation:
Comprehensive and Detailed In-Depth Explanation=
LCEL (LangChain Expression Language) is a declarative syntax in LangChain for composing chains—
sequences of operations involving LLMs, tools, and memory. It simplifies chain creation with a
readable, modular approach, making Option C correct. Option A is false, as LCEL isn’t
fordocumentation. Option B is incorrect, as LCEL is current, not legacy. Option D is wrong, as LCEL is
part of LangChain, not a standalone LLM library. LCEL enhances flexibility in application design.
: OCI 2025 Generative AI documentation likely mentions LCEL under LangChain integration or chain
composition.
What is the purpose of memory in the LangChain framework?
B
Explanation:
Comprehensive and Detailed In-Depth Explanation=
In LangChain, memory stores contextual data (e.g., chat history) and provides mechanisms to
summarize or recall past interactions, enabling coherent, context-aware conversations. This makes
Option B correct. Option A is too limited, as memory does more than just input/output handling.
Option C is unrelated, as memory focuses on interaction context, not abstract calculations. Option D
is inaccurate, as memory is dynamic, not a static database. Memory is crucial for stateful
applications.
: OCI 2025 Generative AI documentation likely discusses memory under LangChain’s context
management features.
How are chains traditionally created in LangChain?
C
Explanation:
Comprehensive and Detailed In-Depth Explanation=
Traditionally, LangChain chains (e.g., LLMChain) are created using Python classes that define
sequences of operations, such as calling an LLM or processing data. This programmatic approach
predates LCEL’s declarative style, making Option C correct. Option A is vague and incorrect, as chains
aren’t ML algorithms themselves. Option B describes LCEL, not traditional methods. Option D is false,
as third-party integrations aren’t required. Python classes provide structured chain building.
: OCI 2025 Generative AI documentation likely contrasts traditional chains with LCEL under
LangChain sections.
How are prompt templates typically designed for language models?
B
Explanation:
Comprehensive and Detailed In-Depth Explanation=
Prompt templates are predefined, reusable structures (e.g., with placeholders for variables) that
guide LLM prompt creation, streamlining consistent input formatting. This makes Option B correct.
Option A is false, as templates aren’t complex algorithms but simple frameworks. Option C is
incorrect, as templates are customizable. Option D is wrong, as they handle text, not just
numbers.Templates enhance efficiency in prompt engineering.
: OCI 2025 Generative AI documentation likely covers prompt templates under prompt engineering
or LangChain tools.
Here is the next batch of 10 questions (21–30) from your list, formatted as requested with detailed
explanations. The answers are based on widely accepted principles in generative AI and Large
Language Models (LLMs), aligned with what is likely reflected in the Oracle Cloud Infrastructure (OCI)
2025 Generative AI documentation. Typographical errors have been corrected for clarity.