Solution of OCI Generative AI Professional 1Z0-1127-24 || OCI Generative AI Professional SCORE=100%

Summary

This video script discusses the Oracle OCI Generative AI certification, which is free until July 31, 2024. The speaker covers the solutions and explanations for various questions related to AI topics, including greedy decoding, MMR, RAG models, k-prompting, and prompt injection. They also delve into techniques like Chain of Thought and the benefits of using a Vector database with large language models. The script promises more videos with a question bank for exam preparation, aiming to help viewers pass the certification exam.

Takeaways

• 📅 The Oracle OCI Generative AI certification is free until July 31st, 2024, after which it will become a paid examination.
• 📚 The speaker has covered multiple exams including Oracle Cloud Infrastructure and Oracle Cloud Artificial Intelligence, providing solutions and explanations to help pass them.
• 💡 Greedy decoding in language models is characterized by always selecting the word with the highest probability at each step, which can limit diversity.
• 🔍 MMR (Maximum Margin Relevance) is a retrieval method used to balance relevancy and diversity in search results, ensuring a mix of similar yet varied documents.
• 🤖 For an AI assistant that handles both image analysis and text generation, a Retrieval-Augmented Generation (RAG) model is the likely choice due to its hybrid approach.
• 🔑 K-prompting refers to providing a few examples of the intended task in the prompt to guide the model's output, a technique derived from the course material.
• 🚫 Prompt injection or 'jailbreaking' is exemplified by a scenario where a user asks for a method to bypass a security system in a story, which the AI navigates carefully.
• 🤖 The 'Chain of Thought' technique prompts LLMs to emit intermediate reasoning steps in their responses, enhancing transparency and interpretation.
• 📝 The prompt template discussed can support any number of variables, including the possibility of having none, offering flexibility in input specification.
• 🚫 Among the pre-trained foundational models available in OCI Generative AI service, the translation model is notably absent from the offerings.
• 💰 Using a Vector database with large language models provides a cost benefit by offering real-time updated knowledge bases more cheaply than fine-tuned LLMs.
• 🔄 The integration of a vector database into RAG-based LLMs shifts the basis of their responses from pre-trained internal knowledge to real-time data retrieval, improving accuracy and credibility.

Q & A

What is the main characteristic of greedy decoding in the context of language models?

The main characteristic of greedy decoding is that it picks the most likely word to emit at each step of decoding, which can lead to suboptimal results in terms of diversity and exploration.

What does MMR stand for and what is it used for in retrieval systems?

MMR stands for Maximum Margin Relevance. It is used to balance between relevancy and diversity in retrieval systems, ensuring diversity among the results while still considering the relevance to the query.

What type of model would an AI development company likely focus on integrating into their AI assistant for both image analysis and text to visual generation?

The company would likely focus on integrating a Retrieval-Augmented Generation (RAG) model, which uses text as input for retrieval and generates accurate visual representation based on retrieved information.

What does 'k-prompting' refer to when using large language models for task-specific applications?

K-prompting refers to explicitly providing k examples of the intended task in the prompt to guide the model's output, enhancing the model's understanding and performance for specific tasks.

Which scenario exemplifies prompt injection or jailbreaking in the context of language models?

The scenario where a user submits a query for writing a story where a character needs to bypass a security system exemplifies prompt injection or jailbreaking.

What technique involves prompting the language models to emit intermediate reasoning steps as part of their response?

The technique that involves prompting the language models to emit intermediate reasoning steps is known as 'Chain of Thought,' which enhances transparency and interpretability in the model's answers.

What is true about prompt templates in relation to input variables?

Prompt templates support any number of variables, including the possibility of having none, offering flexibility in specifying input variables for various use cases.

Which category of pre-trained foundational model is not available in the OCI Generative AI service?

The category of pre-trained foundational model not available in the OCI Generative AI service is the translation model.

What is a cost-related benefit of using a Vector database with large language models?

A cost-related benefit of using a Vector database with large language models is that they offer real-time updated knowledge bases and are cheaper than fine-tuned language models, reducing the need for extensive training and maintenance.

How does the integration of a vector database into RAG-based language models fundamentally alter their response?

The integration of a vector database into RAG-based language models fundamentally alters their response by shifting the basis of their responses from pre-trained internal knowledge to real-time data retrieval, allowing for more accurate and up-to-date information.