Threadcast link:
@taskmaster4450/re-leothreads-2tyrmpfke
Link to recording:
https://x.com/taskmaster4450/status/1918307504452231444
Speakers
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This Lion's Den episode featured a wide-ranging discussion on the capabilities and limitations of Rafiki, the AI assistant integrated into the InLeo platform. The host and participants explored how Rafiki can be utilized as a productivity tool, while also highlighting areas where the AI agent falls short compared to more advanced language models.
Leveraging Rafiki as an Assistant
The host emphasized that Rafiki can be a valuable assistant when used properly. For example, one participant described how they were able to get Rafiki to provide price quotes for various crypto tokens, generate summary tables, and even format the information in Markdown for easy inclusion in articles. The key is to engage Rafiki through a conversational back-and-forth, providing feedback and corrections to help train its responses.
Rafiki's Limitations and Outdated Knowledge
However, the group also acknowledged significant limitations with Rafiki. As an older language model, its knowledge is often outdated, sometimes by a couple years. Participants reported issues where Rafiki would provide stale information, such as outdated sports team rosters or cryptocurrency prices. There were also concerns that Rafiki may not have access to real-time data or the ability to scrape information from external websites.
Prompting Strategies and Hypotheticals
To work around Rafiki's limitations, the host suggested experimenting with different prompting strategies. This includes framing questions as hypotheticals or fictional scenarios, rather than direct queries about current events. The group found that Rafiki was sometimes more responsive when asked to imagine how it would approach a task, rather than being asked for factual information.
The Role of LeoAI
The discussion also touched on the distinction between Rafiki and the upcoming LeoAI system. While Rafiki is an open-access AI assistant, LeoAI is envisioned as a more premium, feature-rich chatbot that will leverage a vector database of information from the Hive blockchain. The host speculated that LeoAI may have capabilities like real-time data access, image generation, and video creation that Rafiki currently lacks.
Multilingual Support and Cultural Context
Another key point raised was the importance of engaging Rafiki in different languages, not just English. The group noted that Rafiki seemed to perform well when tested in languages like Arabic, potentially capturing nuances of language and cultural context that could improve its overall capabilities.
Conclusion
Overall, the episode participants provided a nuanced look at the current state of Rafiki and the future potential of AI assistants within the InLeo ecosystem. While Rafiki has limitations, the host emphasized the value in continuing to experiment and provide feedback to help advance the AI's abilities. The upcoming LeoAI system was also highlighted as a promising next step that could deliver more robust, real-time capabilities for InLeo users.