At MarketReader, our goal is to provide our users with the most relevant, high-quality explanations of asset volatility. So, using an approach known as Retrieval-Augmented Generation (RAG) in conjunction with other technology, MarketReader has fine-tuned our GPT prompts to generate the best results. 

Fine-Tuning Our AI System

When explaining why an asset is moving, our process does more than just search for documents—we calculate live statistics and outputs via traditional quantitative computing methods, then, with our multi-stage fine-tuned prompts, distills the outputs into actionable insights. This tailored approach proves especially beneficial in volatile market conditions, because it allows GPT to focus on the curated, dependable data provided by MarketReader rather than sifting through a myriad of potentially inaccurate sources.

Skepticism About LLMs

Although AI is an increasingly common tool in finance and trading applications, many remain skeptical of the quality and ultimate usefulness of AI-generated insights. This concern stems from the fact that large language models (LLMs) like ChatGPT often produce inaccurate or outdated results to queries related to more technical subject matter. 

Generally, when given a query, LLMs scour the entirety of the internet for answers regardless of the source of the information, and often rely on outdated information that the models were trained on. LLMs are a “black box” in that users do not know where the information is coming from. (GPT-4 is known to have around 1 trillion parameters.) While this scan-the-entire-internet approach may seem comprehensive, the models lack discernment regarding the quality and reliability of the sources they draw upon.

Putting “Blinders” On AI Models For Better Results

MarketReader offers a different approach, leveraging the speed and efficiency benefits of AI while prioritizing accuracy and reliability using a set of specially designed “guard rails”. By employing the Retrieval-Augmented Generation (RAG) framework, MarketReader restricts the data available to GPT models, ensuring that responses are based on high-quality, institutional-grade data and model outputs rather than unfiltered internet searches.

In RAG, the retriever component searches through a large corpus of data (provided by MarketReader) to find relevant information, and then a generator component synthesizes new text based on the retrieved information. In other words, we put blinders on GPT models so the AI can only use MarketReader’s high-quality data as input sources. This approach allows the model to generate more informative and contextually relevant responses, but maintains the benefit of real-time delivery that is crucial for intraday market insights.

TL;DR — Advantages of RAG Approach Over Traditional LLMs

  • Up to the minute, high-quality, and holistic/replete explanations of the drivers of asset volatility 
  • No hallucinations, because we’re not asking LLMs to recall anything from pre-trained data sets
  • No non-public material information—RAG means that everything we provide to an LLM is publicly available information.

Benefits for MarketReader Users

One of the key advantages of MarketReader’s approach is the easily readable output generated by GPT. For instance, you can understand why an entire industry sector has moved within the past 10 minutes simply by reading a concise paragraph of text. And while the language of our summaries may be easy to digest, the underlying information stems from meticulously curated sources, heavily filtered and maintained for accuracy and thoroughness. This combination of AI-generated text and high-quality data ensures that users receive actionable insights that are both informative and reliable.

These institutional-quality insights can provide MarketReader users with multiple benefits: 

  • Real-time explanations of asset volatility incorporating multiple professional information sources 
  • Efficient, accurate content creation for financial advisors and platforms with news feeds and/or alert features
  • Auto-generated longer time horizon portfolio reports for RIAs and PMs
  • Enterprise-level insights at scale — massive efficiency improvements for large organizations

In essence, MarketReader bridges the gap between AI-driven innovation and the demands of the finance industry by offering a solution that combines the linguistic capabilities of AI with the reliability of institutional-grade data. Through this approach, MarketReader empowers users to make informed decisions with confidence in the accuracy and relevance of the insights provided.

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