Real-time Assurance of Financial Guidance AI using Explicit Guardrails

点击翻译按钮获取中文摘要

With the huge interest in ChatGPT, and other large language models (LLMs), it's clear to many in the business community that LLMs are very engaging and offer the possibility of providing smart solutions for customers at much lower cost. But it is also well reported that these models generate errors and hallucinations. These models are unsafe to use in many real enterprise applications without addressing this.If the hallucinations could be prevented and guarantees could be given as to the accuracy of responses by LLMs, then their flexibility, engaging UI, and low cost would unlock many possibilities. In our business we are particularly focused on unlocking digital financial guidance for consumers, particularly the mass market and low income segments which are not well served by financial services companies today.New tooling and methods emerging in the last three months from the open-source LLM community are starting to be able to augment and constrain the outputs of LLMs such as ChatGPT and GPT4\. The community is using them primarily to blend in different data sources and content. We see a way to modify these methods to create strong guardrails around LLMs for specific financial guidance tasks. This assurance approach promises to significantly improve quality and safety and we believe this is an enabler for financial services companies to using AI to support their customers and their financial decisions.This project is about engineering this assurance solution. The project will then apply this engineered assurance prototype to a real financial guidance pension use case.Beyond this initial use case, this assurance methodology has potential to be applied in other guidance domains where guardrails can be articulated clearly in natural language or logic and code, for example legal, taxes, subsidies and regulation use cases.