tuning doesn't affect compute optimization. Option B is unrelated, as IAM handles access control. Option D is incorrect because LLM fine -tuning doesn’t directly improve OCI security. : OCI fine-tuning enables tailored NLP solutions by adapting LLMs to domain -specific language and tasks. Compared to traditional LLM applications, how can LangChain models potentially improve the efficiency of text generation tasks?
In a Transformer network, what is the role of the encoder -decoder pair?
A well -designed prompt must clearly state the task and expected output to guide the LLM effectively. Option A is incorrect —while brevity helps, clarity matters more. Option B can confuse the model unless the jargon is necessary and well -contextualized. Option D is unprofessional and may reduce output quality depending on the use case. : Clear intent in the prompt ensures the LLM generates accurate and relevant responses. When creating a dedicated AI cluster for OCI Generative AI, what resource type is mandatory to include?
: Integrating access checks in the function code is key to safeguarding LLM APIs during deployment. How can semantic search enhance the retrieval process for Retrieval -Augmented Generation (RAG) tasks?
When creating a dedicated AI cluster for OCI Generative AI, which factor should be considered to ensure optimal performance for your workload?
Explanation: This is the least likely scenario when compared to the others because writing a creative poem based on a theme is a very common and basic task handled easily by standard LLMs —not something unlikely. Option A is a valid use case for code models. Option B, while complex, is becoming more achievable with advancing multi -modal models. Option C is realistic with language agents automating support tasks. : Writing creative text is one of the most typical and supported features of standard LLMs, making option D incorrect as the least likely. How can pre -trained models for summarization be leveraged within the OCI Generative AI service?
