How AI and Large Language Models Can Help You Access Your Data Naturally

As AI becomes more advanced, businesses are finding new ways to access their data without needing specialized technical skills. One exciting development is using Large Language Models (LLMs) like ChatGPT to interact with data using natural language. Imagine being able to ask questions like "How do I spend money when I’m stressed?" and receiving an insightful answer without scrolling through rows of spreadsheets.

In this blog, we'll explore how natural language processing (NLP) tools can make data more accessible, and we’ll look at a real-world example from our own work with Julep to show how companies can leverage this technology.

[We examined this topic and more in our recent webinar, Making Your Data Useful with AI. Watch the full replay here].

The Power of Natural Language Access to Data

If you want to access your data colloquially—meaning in everyday language rather than through complicated queries or reports—LLMs and NLP tools are a great fit. Instead of looking at a spreadsheet or a traditional chart, these tools allow users to interact with their data more conversationally, making insights more accessible.

For example, one of our clients, Julep, wanted to allow their users to access financial data and see it through a more intuitive, human-centric lens. Instead of presenting users with numbers and graphs, they aimed to let users query their financial habits using natural language, like asking, "How much do I spend when I’m feeling stressed?"

How We Built the AI Solution for Julep

To achieve this, we designed an AI solution where Julep’s users could interact with their data using natural language. The system took in both transactional data and information about users' emotions related to that data, then allowed them to query the system in flexible ways. This made it easier for users to explore their financial behaviors in a way that felt natural and accessible, without needing to comb through detailed spreadsheets or graphs.

On the backend, we wrapped an LLM like GPT around this data, allowing users to ask questions in simple terms. While the AI was effective in gathering insights, we also found that, in some cases, involving a human coach could complement the AI. For example, AI could give basic advice, but more nuanced, emotional coaching often required a human touch. However, over time, these coaching sessions could be fed into the AI to improve its advice-giving abilities.

Lessons Learned: Accuracy, Costs, and Practical Applications

While NLP tools make it easier to access data, there are some caveats:

1. Accuracy of Information: AI models, while powerful, aren't always perfect. We discovered that although the AI could process and present data, it sometimes stumbled when performing calculations. In cases like this, combining AI with human verification, or even doing some manual calculations on the backend, ensured more reliable results.

2. Cost Considerations: One challenge with deploying AI solutions, especially those powered by LLMs, is cost. Each interaction with the AI—each query a user submits—requires resources. This can add up quickly, especially if users are frequently engaging with the system. For companies looking to scale, keeping a close eye on how much each interaction costs and projecting those expenses is key to keeping the solution sustainable.

3. Future-Proofing Your AI Solution: As new technologies like Graph RAG (a more advanced version of RAG—Retrieval-Augmented Generation) come into play, they offer more powerful ways to search and relate data. However, they also introduce higher costs and slower performance. Depending on the size of your data and the complexity of the relationships you need to uncover, the best AI solution may vary. Companies should carefully assess their needs and the costs associated with cutting-edge tools before diving in.

Making AI More Accessible for Non-Technical Users

One exciting development is that as LLMs and custom AI tools become easier to use, businesses may not need a developer to build sophisticated AI solutions. By using pre-built tools like custom GPTs, non-technical teams can test and deploy basic AI functionality on their own, making it easier to test the viability of AI-driven data access before committing to larger-scale investments.

Conclusion: The Benefits of Conversational Data Access

AI tools like LLMs offer a powerful way to interact with your data, making it accessible and usable without needing a deep understanding of data science. Whether it’s querying financial transactions in human terms or asking how much you spend at coffee shops, these tools offer flexibility and ease of use.

However, it's crucial to weigh the trade-offs: accuracy, the cost of frequent AI interactions, and when to involve human expertise. As AI continues to evolve, the key to success will be understanding your specific needs and choosing the right tools for the job.

If you’re considering an AI solution, ask yourself:

• Do your users need easier, conversational access to data?

• Can AI streamline repetitive tasks like summarizing or searching through data?

• Is the cost of deploying and maintaining AI solutions manageable?

By answering these questions, you can determine whether using AI for colloquial data access is the right choice for your business.