Best Practices in API and AI Models

The video explores the world of generative AI, covering essential terminologies, model APIs, and application building. It explains AI subfields like machine learning and NLP, and introduces Transformer models. The video also discusses using APIs, building AI-powered applications, and customizing models through techniques like RAG and fine-tuning. It emphasizes the importance of understanding model capabilities and designing effective prompts.

Which model architecture introduced the concept of self-attention mechanisms?

• Recurrent Neural Networks

• Support Vector Machines

• Convolutional Neural Networks

• Transformer Models

What is a large language model (LLM) primarily trained on?

• Image data

• Audio data

• Video data

• Text data

What is a key characteristic of machine learning?

• It requires explicit programming for each task.

• It focuses on enabling computers to learn from data.

• It is unrelated to artificial intelligence.

• It is a subfield of computer vision.

What is the primary focus of prompt engineering?

• Improving hardware for AI processing

• Developing new AI models

• Creating user interfaces for AI applications

• Designing effective prompts for desired outputs

What is essential to keep secure when using model APIs?

• The API documentation

• The API key

• The model parameters

• The user interface

Which of the following is a best practice when using APIs?

• Optimizing API usage by selecting model parameters

• Using outdated API versions

• Sharing API keys publicly

• Ignoring rate limits

What is the first step in building a chatbot using an LLM?

• Deploying the application

• Collecting user feedback

• Choosing an LLM provider

• Designing the user interface

What does retrieval-augmented generation (RAG) allow an AI model to do?

• Translate languages

• Perform sentiment analysis

• Generate images from text

• Access external information sources in real-time

What is the purpose of fine-tuning an AI model?

• To reduce the model's accuracy

• To increase the model's size

• To create a new model from scratch

• To adapt a pre-trained model to specific needs

Which of the following is a benefit of using generative AI in applications?

• It is only applicable to text generation

• It can generate novel outputs resembling training data

• It reduces the need for data

• It eliminates the need for human oversight