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#chatbots

Every item tagged chatbots, newest first.

3 items

Customized to user?

The customization in AI models like ChatGPT comes from techniques such as few-shot learning, retrieval-augmented generation, and continuous learning. These methods allow models to adapt to user interactions and incorporate past conversations into new ones. The models use memory mechanisms and context to recall previous chats and apply that information in future interactions. This enables intelligent reuse of past ideas in new conversations.

Key takeaways
  • Models use few-shot learning and retrieval-augmented generation for customization.
  • Memory mechanisms help models recall past conversations.
  • Continuous learning allows models to adapt to user interactions.

ChatGPT's market share slips below 50% for first time

ChatGPT's market share dropped to 46.4% by May 2024, per Sensor Tower data, as competition from Google and Anthropic intensified. Despite losing its majority position, ChatGPT remains the largest player with 1.1B monthly active users on mobile. Builders should note the shift towards a more fragmented market. This change may impact strategy for those integrating ChatGPT into products.

Key takeaways
  • ChatGPT's market share below 50% for first time: 46.4%.
  • Still largest player with 1.1B mobile monthly active users.
  • Competition from Google and Anthropic drove the decline.
modelsJul 16

How we leveraged distilabel to create an Argilla 2.0 Chatbot

Argilla 2.0 integrated distilabel for data curation and model training. The Argilla team used distilabel to generate synthetic data, fine-tune models, and deploy a chatbot. This approach streamlined their development process and improved model performance. You can replicate this workflow using Argilla and distilabel.

Key takeaways
  • Argilla 2.0 used distilabel for data curation and model training.
  • Synthetic data generation improved model performance.
  • Streamlined development process via distilabel integration.