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Have you ever asked a chatbot for critical financial data, only to question if the numbers it provided are accurate?
Since accuracy is crucial, FP&A leaders must ensure that the AI-driven solutions their team utilize provide reliable and consistent information. For instance, if a team is making decisions based on projections, trends and insights from an AI system, even small errors could lead to serious consequences and poor choices. This is why, at Precanto, we are working hard to improve the consistency of responses from Large Language Models (LLMs) when it comes to handling financial data.
LLMs, by their very nature, are not deterministic. Since they are designed to predict the probabilities of the next word in a sequence, each word that comes after could have an unexpected effect on the final response. So, how can we ensure that the numerical data, which is the foundation of financial decision making, is analyzed and presented by the LLM with very high accuracy?
Let’s examine our methods for accomplishing this.
The Challenge: Balancing Flexibility with Accuracy
LLMs excel in understanding and generating natural language, which enables them to have conversations with humans in a way that feels very natural. However, tasks requiring a high degree of precision, such as responding to inquiries regarding financial data, can be more challenging. The accuracy of their responses is largely dependent on the quality and relevance of data fed as context into these models.
Our team’s objective is not to make LLM responses deterministic in their wording–because that’s not how they are built– but to make sure that the numbers and insights they provide are as accurate and reliable as a human financial analyst.
Our Approach: A Multi-Faceted Strategy
We understand that in the world of finance, where every digit counts, ensuring the accuracy of our LLM, Ask Precanto, is very important. Through these innovations, our goal is not just to make LLMs smarter, but to make them truly dependable.
Understanding User Intent: The Foundation of Accuracy
Consider asking a financial analyst a question, if they don’t understand your requirements, their response may be partially or completely erroneous. The same goes for LLMs. We have developed advanced methods to make sure our chatbot, Ask Precanto understands user intent precisely, including the specifics of the user's question and the context needed for answering it.
LLM Chains and Agentic Behavior: The Right Tool for the Right Question
Think of Ask Precanto as a group of specialized analysts, each with expertise in areas like platform navigation, scenario modeling, or providing answers about financial data. By utilizing Langchain to create these expert chains, we ensure that every query is routed to the most appropriate model for accurate and relevant responses.
LangChain is an open-source framework that enables developers to build powerful applications by integrating large language models with external data sources, APIs, and custom workflows for more dynamic and context-aware solutions.
Transforming Questions for Precision: Speaking the LLM’s language
To make sure the Ask Precanto understands user questions correctly, we transform user input into clear standalone questions. This process also helps us maintain conversational context within an active session. Our objective is to rephrase questions in a way that specialists can easily interpret, thereby reducing uncertainty in Ask Precanto’s output.
Combining Vector Databases and APIs: The Best of Both Worlds
User queries vary in complexity, therefore we developed a system that can intelligently obtain the most pertinent information required to provide an answer. Certain queries are straightforward and can be answered with a simple API call, while others are more complex and require aggregating data from multiple sources or performing calculations. To ensure accuracy, we precompute answers for these complex queries and store results in a vector database. Ask Precanto then uses the highly efficient Retrieval-Augmented Generation (RAG) approach to extract information from a vector database or Precanto APIs to provide context for the language model.
Learn more about how Precanto is Utilizing RAG for Accurate Financial Reporting in Generative AI Applications in our blog
Informed Responses: Clear Attribution of LLM response
While our approach significantly enhances the determinism of responses, we acknowledge that there is always a slight chance that Ask Precanto will misinterpret a user’s question. To protect users from taking action on any misleading information, we make sure that numerical values presented in each response are clearly attributed to its underlying data source. This way, users know exactly what data is presented to them, allowing them to ask the question again with clearer instructions if needed. This adds an extra layer of confidence in the generated insights.
The Result: A Chatbot You Can Trust
By combining these techniques, Precanto has created a system that provides reliable and consistent answers. Though the language may change, our focus remains on providing accurate numerical values in our response. This blend of openness, flexibility, and precision sets our solution apart, giving finance teams the confidence to make decisions based on the insights we provide.
After all, in finance, the only thing worse than no information is the wrong information.
Discover how Ask Precanto, can elevate your FP&A experience by scheduling a demo today!
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