A naukri.com initiative
Medium
1M
329
How Generative AI Assistants Work: A Technical Perspective
- Generative AI assistants are built on transformer-based architectures, a type of neural network that excels at processing and generating sequential data, such as text.
- The model processes these tokens rather than raw text, which allows it to handle a variety of languages, characters, and structures.
- For instance, if a user first asks, “Who is the president of the United States?” and then asks, “Where was he born?”, the model uses context to understand that “he” refers to the president mentioned in the previous question.
- Generative AI models like GPT are first pre-trained on massive amounts of text data from the internet.
- When generating a response, the model doesn’t simply predict a single outcome; it assigns probabilities to many possible next tokens.
- Generative AI assistants are designed to handle complex, multi-turn conversations where context must be maintained across multiple exchanges.
- This technique (RLHF) is crucial for handling complex and nuanced queries, improving the model’s safety, and avoiding harmful or nonsensical outputs.
- Generative AI models can also perform tasks without seeing any explicit examples.
- To serve millions of users efficiently, generative AI assistants are typically deployed on scalable cloud infrastructures, such as Amazon Web Services (AWS), Google Cloud Platform (GCP), or Microsoft Azure.
- As the field of AI evolves, future improvements in context retention, bias mitigation, and real-time scalability will further enhance the capabilities and reliability of generative AI assistants.
Read Full Article
19 Likes
For uninterrupted reading, download the app