The podcast explores the utilization of large language models in daily life and work processes. It discusses the challenges and risks of using them as a service, the concept of retrieval augmented generation, and the use of embeddings and LLMs in text analysis and product development. The podcast also delves into the applications of text embeddings in similarity, search, and classification tasks, while addressing their limitations and potential risks.
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Quick takeaways
Cloud platforms like Google Cloud Platform, Azure, or Amazon Web Services provide easy access to large language models via APIs, enabling integration into projects.
Large language models can enhance traditional search methods through techniques like similarity analysis and classification, using embeddings to map text data in a semantic space.
Deep dives
Using Large Language Models in Everyday Life
Large language models have become an integral part of our daily lives. People can easily access systems like GPT or BARD to ask for advice. While basic usage involves asking simple questions, there are endless possibilities for leveraging large language models. One way is by using them as a data scientist, MLOps engineer, or general engineer to incorporate these technologies into their work. However, training your own large language model can be costly and inaccessible for the average person. Instead, developers can rely on cloud platforms like Google Cloud Platform, Azure, or Amazon Web Services to access large language models via APIs, enabling easy integration into projects.
Exploring the Role of Retrieval Augmented Generation (RAG)
Retrieval Augmented Generation (RAG), also known as AI search, is an emerging concept that enhances traditional search methods using large language models. By converting text into numeric representations called embeddings, large language models can facilitate powerful techniques such as similarity analysis and classification. These embeddings provide a way to map text data in a semantic space, allowing for advanced search capabilities. Additionally, these embeddings can be used for classification tasks without the need for extensive feature engineering. This opens up new possibilities for leveraging large language models in search engines and other applications.
Integrating Large Language Models into Products and Services
Large language models can be integrated into various products and services to enhance functionality. For example, instead of building open-ended chatbots, large language models can be used behind the scenes to process user input and provide structured responses. This approach minimizes the risk of generating nonsensical or misleading information. Furthermore, large language models can be utilized in tasks like extracting relevant information from user input or classifying sentiment. These models can be accessed through cloud services or platforms like Hugging Face, enabling developers to easily incorporate large language models into their projects.
It took a massive financial investment for the first large language models (LLMs) to be created. Did their corporate backers lock these tools away for all but the richest? No. They provided comodity priced API options for using them. Anyone can talk to Chat GPT or Bing. What if you want to go a step beyond that and do something programatic? Kyle explores your options in this episode.
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