Harnessing LLMs to Transform Data Applications: Insights and Best Practices

By Dan McCarey

The rapid evolution of large language models (LLMs) has opened up transformative possibilities in the world of data applications. As someone who specializes in bridging the gap between complex data and user comprehension, I’ve spent the last year exploring how LLMs can make data tools smarter, more intuitive, and, ultimately, more useful. Below, I share some lessons and insights from integrating LLMs into data apps, highlighting their strengths, limitations, and where they shine the brightest.

Why LLMs in Data Apps

At their core, LLMs are incredibly adept at capturing user intent. They can translate vague or unstructured input into meaningful actions, opening up a new realm of possibilities for data applications. Traditionally, users of data apps needed to know how to write SQL queries, navigate rigid interfaces, or interpret complex data schemas. LLMs offer a more intuitive alternative: enabling users to express their needs in plain language and letting the app do the heavy lifting.

In my work, LLMs have been particularly valuable for:

• Turning questions into structured data: Instead of asking users to write a SQL query, the app translates their natural-language questions into structured database queries or API calls.
• Extracting narratives from data: LLMs excel at interpreting data and identifying patterns, enabling them to describe trends, insights, and anomalies in charts or dashboards.
• Personalizing responses: They can incorporate user sentiment, preferences, and contextual nuances into how data is presented.

Building with LLMs: Lessons Learned

1. Chaining LLM Instances with Different Instructions

One of the most powerful techniques I’ve used is chaining separate instances of LLMs with tailored prompts. Each instance has a specific role in the pipeline:

• Parsing user intent: The first instance interprets what the user wants and translates it into a structured intermediate format.
• Generating API calls or queries: A second instance uses the structured intent to construct precise database queries or interact with external APIs.
• Synthesizing results: A final instance explains the results to the user, often adding insights or highlighting key points.

This approach allows you to balance the strengths of LLMs (natural language understanding) with the precision of deterministic logic in your application.

2. Letting Applications Handle Logic

While LLMs are incredible for capturing intent, they aren’t as reliable for tasks that require rigid adherence to defined schemas or API documentation. For example, I’ve found that having an LLM directly generate a SQL query can lead to errors, especially when schemas are complex or not well-documented.

A more robust approach is to use LLMs to interpret intent and then pass structured data (e.g., JSON objects) to the application’s logic layer. The application can validate the structure, make database or API calls, and ensure compliance with the schema. This method significantly reduces errors and ensures more predictable performance.

Preventing Hallucinations: The Power of Explicit Instructions

One of the biggest risks of using LLMs is their tendency to “hallucinate” or confidently generate incorrect information, such as errant data values or made-up metrics. Through trial and error, I’ve found that this issue is largely avoidable with explicit, well-defined instructions.

When you’re clear about what the LLM should and shouldn’t do—whether it’s sticking to a specific set of metrics or refraining from making assumptions—it’s surprisingly reliable. However, if you’re lax with instructions, don’t be surprised when the model invents metrics or returns nonsensical data. Precision in prompt design is key to minimizing hallucinations and building trust in your application.

Challenges & Limitations

1. Schema & Documentation Issues

LLMs struggle when they need to pull specific details from schema documentation or API docs, especially if the schema is poorly defined or extensive. While tools like embeddings and retrieval-augmented generation (RAG) can help surface relevant sections of documentation, they’re not perfect.

2. Handling Edge Cases

Natural language queries often include ambiguities or edge cases that LLMs can misinterpret. For example, users might ask questions with implicit assumptions, like “What were last quarter’s sales trends?” The intent here could vary depending on the context—region, product category, or time range. Building in additional logic to clarify intent is often necessary.

3. Data Sensitivity

Even with explicit instructions, there’s always a risk of minor inaccuracies creeping into responses. It’s essential to validate outputs when accuracy is paramount, particularly in data-intensive applications.

Where LLMs Truly Shine

Despite these challenges, LLMs bring transformative capabilities to data apps:

1. Humanizing Data
   They excel at weaving narrative insights from raw data. For instance, instead of showing a flat line chart, an LLM can explain, “Revenue dipped slightly in Q3 due to a seasonal slowdown, but sales rebounded strongly in Q4.”

2. Lowering the Barrier to Entry
   By enabling natural language interactions, LLMs make data tools accessible to non-technical users. This democratization of data is critical for organizations aiming to foster a data-driven culture.

3. Enhancing UX
   LLMs can enhance user experiences by incorporating sentiment analysis, personalization, and context. They can adjust how results are framed based on the user’s tone or preferences, creating a more engaging interaction.

Takeaways for Building with LLMs

• Leverage LLMs for what they do best: natural language understanding, capturing intent, and synthesizing narratives.
• Let your application handle logic and validation: Use structured outputs from LLMs to drive deterministic processes.
• Use explicit instructions to avoid hallucinations: Be clear and precise in prompts to minimize errors or fabricated data.
• Invest in user experience: Test edge cases, clarify ambiguous queries, and build feedback loops to refine model performance.
• Prepare for iteration: Integrating LLMs into data apps isn’t a one-and-done effort. It requires constant tuning and testing to adapt to user needs.

Final Thoughts

Integrating LLMs into data applications is an exciting frontier. While they aren’t a silver bullet for every problem, they bring immense value in making data more accessible and actionable. By understanding their strengths and limitations—and taking the time to craft explicit instructions—you can design smarter systems that empower users, reduce complexity, and unlock the stories hidden in your data.