Mastering AI FinBot Development: Tutorial Guide to LangChain, Prompt Engineering, & Tree of Thoughts
Unlock Financial Insights with AI: Multi-Model FinBot Development using LangChain & Advanced Prompt Engineering.
Welcome back to our series on building a FinBot using AI and the LangChain framework with Richard Walker from Lucidate. In this comprehensive guide, we navigate the future of AI in Capital Markets, showcasing the potential of integrating multiple specialized Large Language Models (LLMs) to overcome limitations in knowledge recency and visual presentation.
Discover our innovative approach of assembling an AI ensemble to mimic high-performing business teams in finance, marketing, and product management. In this video, we use a simple architecture with three AI LLMs to illustrate the potential of multi-model AI in financial advice and market analysis.
TIMESTAMPS
00:00 Introduction
02:04 Building an AI Ensemble
05:09 Demo: Using Three AI LLMs
07:35 Introduce the Portfolio Chat LLM
08:27 Interacting with Starburst Charts
12:07 Future developments in AI powered apps
14:29 Closing thoughts
Follow along as we demonstrate how our FinBot engages the user, sources real-time market information, and uses interactive charts and tables to provide a personalized portfolio analysis and advice. You'll see how this FinBot not only engages with the user but also generates dynamic reports and insightful visual representations of the user's portfolio.
Playlist for Tree of Thoughts and Chain of Thought prompting: https://bit.ly/3NWZW38
Get exclusive access to the source code and a code walkthrough video for this demo application. Improve upon it and use it as a foundation for your own multi-model AI experiments. Join Lucidate's membership program to access more perks and privileges: https://bit.ly/3J5E5Ux
Stay tuned for our next video, where we will introduce LangChain agents and the ReAct prompting technique to enhance the FinBot's responses with real-time information from company financials.
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