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Practical Applications of Generative AI: How to Sprinkle a Little AI in Your App - Phil Haack
Explore the practical applications of generative AI, including how to fine-tune GPT models, handle limitations, and integrate them into your app, with expert Phil Haack.
- Generative AI is not the same as artificial general intelligence (AGI).
- GPT (Generative Pre-trained Transformer) is a type of generative model that can generate text based on a prompt.
- A prompt is a set of instructions that guides the model’s output.
- The quality of the output depends on the quality of the prompt.
- GPT’s primary function is to predict the next token in a sequence, not to understand the meaning of the text.
- The model uses an encoder-decoder architecture, with the encoder converting text into a numerical representation and the decoder generating text based on that representation.
- The input to the model is a sequence of tokens, which are then mapped to a vector space using a process called embeddings.
- The model can be fine-tuned for specific tasks, such as language translation or text classification.
- GPT has limitations, including hallucinations (i.e., generating text that is not supported by the input) and lack of understanding of the meaning of the text.
- In order to use GPT effectively, you need to carefully design the prompt and input data.
- You also need to consider how to handle the model’s limitations and output.
- OpenAI provides a cloud-based API for accessing GPT, and there are also libraries available for integrating GPT into applications.
- GPT can be used for a variety of tasks, including chatbots, language translation, and text summarization.
- It’s not magic, but rather the result of careful engineering and development.