Why do BrainSoup agent responses seem more relevant than direct ChatGPT responses?

A BrainSoup agent using ChatGPT (for example) as a language model generally provides more relevant answers than a direct use of ChatGPT thanks to several improvements:

  1. Semantic Kernel: This open-source AI orchestration framework developed by Microsoft allows BrainSoup to extend the capabilities of Large Language Models (LLMs) like ChatGPT.
  2. Context Window Optimization: BrainSoup drastically optimizes the use of the LLM context window, which impacts the relevance of the agent's responses.
  3. Long-term Memory: Agents in BrainSoup have access to documents and previous conversations, which can help provide more relevant responses based on past interactions.
  4. User-provided Data: BrainSoup allows users to provide their own data, which can help tailor the agent's responses to the user's specific needs and preferences.