From the course: Python for AI Projects: From Data Exploration to Impact

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Bringing It All Together: Improving your Chatbot with ML

Bringing It All Together: Improving your Chatbot with ML - Python Tutorial

From the course: Python for AI Projects: From Data Exploration to Impact

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Bringing It All Together: Improving your Chatbot with ML

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- [Instructor] Here we have a familiar chat interface. Just type your question below and the LLM will respond in the main window. Let's try asking for some food recommendations in Los Angeles as an example. (no audio) You can click the view full LLM request payload expander below the response to see all the data returned from the Mistral LLM to our app. (no audio) Here you can view the full breakdown, including messages, prompts, context, and key parameters like max tokens and temperature used in the LLM request. We can also click on one of the follow-up questions, which are suggested below the response. These options are actually generated in the same LLM response that we've crafted using our prompt engineering strategy. In our example, let's just follow up with a question of our own and ask the app to recommend a tour product, which has good food and nice scenery. (no audio) When you're done chatting, you can reset…

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