Oracle 1z0-1127-24 Exam With Confidence Using Practice Dumps

The "Top p" parameter, also known as nucleus sampling, in generative AI models limits token selection based on the sum of their probabilities. It ensures that the cumulative probability of the selected tokens meets or exceeds a specified threshold p. This approach dynamically includes as many tokens as necessary to reach the desired probability sum, allowing for more diverse and contextually appropriate outputs compared to a fixed top-k selection.

References

Research papers on nucleus sampling and token selection methods

OCI Generative AI model documentation

Using vector databases with Large Language Models (LLMs) offers cost-related benefits, particularly by providing real-time updated knowledge bases. This approach can be more cost-effective than fine-tuning LLMs frequently, as vector databases allow for the dynamicretrieval of information without the need for constant retraining. This reduces operational costs while maintaining access to up-to-date data.

References

Articles on the cost efficiency of vector databases

Research on integrating vector databases with LLMs for real-time updates

Chain-of-Thought: The first prompt calculates the total number of wheels and then uses that information to determine how many sets of wheels can be bought. This sequential reasoning process aligns with the Chain-of-Thought technique.

Least-to-most: The second prompt solves a complex problem by first identifying the needed formula and then solving a simpler version before tackling the full question. This incremental approach matches the Least-to-most technique.

Step-Back: The third prompt starts by defining greenhouse gases and then explores their impact on climate change, taking a step back to establish foundational knowledge before addressing the main question.

References

Research articles on prompting techniques for language models

Documentation on effective use of prompting strategies