Adriana, the AI that simplifies asset assessments

📅 Published on July 30, 2024

When six researchers from one of the world’s most prestigious universities (Stanford) publish a paper with an architecture similar to yours, you have every reason to be proud of your work.

Earlier this month, a groundbreaking paper titled “Hallucination-Free? Assessing the Reliability of Leading AI Legal Research Tools” was published, shedding light on the critical importance of precision in AI predictions. This is particularly essential in the legal field, where even the smallest error or approximation can lead to serious consequences. Accuracy is not just preferred—it’s mandatory.

Different use case, same goal: Zero errors

To achieve the highest possible accuracy, large language models (LLMs) require assistance, a concept the researchers describe as “hand-holding.” This approach is similar to the design we had in mind when developing Adriana’s architecture. Instead of solely relying on the model’s “intelligence,” the system is designed to work alongside the LLM, ensuring that predictions are consistently accurate.

While the researchers focused on AI-powered legal research tools, our focus with Adriana is on wealth balance calculations. Despite the different use cases, the ultimate goal is the same: error-free predictions. Whether in law or finance, precision is key.

Improving model accuracy

We’ve learned a lot about improving model accuracy over time, much like the research outlined in the paper. One of our techniques, similar to Adriana’s architecture, ensures that the LLM is guided throughout the process to minimize errors.

We also have another technique for improving model accuracy (an architecture similar to Adriana’s).

To save you time and avoid all the mistakes we made in using RAG.

Learn more about Adriana and read the Stanford Paper.