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.

Key takeaways

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.

Practical Applications of Generative AI: How to Sprinkle a Little AI in Your App - Phil Haack

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