Case Study
The Daoist Sage AI Agent
A case study on grounding AI memory using a dedicated RAG to solve challenges of hallucination and unreliable memory.
The Core Challenge
I've been working on developing an AI Crypto trading agent. My biggest issue during development was memory and hallucination. Super annoying and totally fucked its ability to trade. Obviously this limits an agents reliability for other applications requiring accuracy and verifiability.
AI Hallucination
The tendency for an AI to confidently generate factually incorrect or nonsensical information. It fabricates "facts" when it doesn't know the answer.
Fickle Memory
An inability to recall specific, crucial details from a defined knowledge base, or maintain coherent context across long conversations. This leads to inconsistent and unreliable responses.
The Solution: Grounded Wisdom
To address these limitations, I built a Daoist Sage Agent using a Retrieval-Augmented Generation (RAG) system as its foundational memory and accuracy mechanism.
The Agent in Action
Here is a simulation of the agent, demonstrating its ability to provide a grounded, cited, and insightful answer.
Product Management Process
As Product Manager and Lead Developer for this MVPI did it all. From ideation to conceptual deployment.
Explore the Codebase
My full source code at the link below.
Future Vision
This project is a fun example of how RAG is used to improve memory and hallucination issues. Conceptually it can be used for many things:
Process Complex Internal Documentation
Analyze and cite information from legal contracts, regulatory guidelines, internal policy manuals, or scientific research with high fidelity.
Provide Specialized, Fact-Checked Assistance
Deliver highly specialized assistance in regulated industries where precision, auditability, and verifiability are important.