How Developers Are Using LLMs in Production: From Data Parsing to SQL Agents

Large Language Models (LLMs) are rapidly moving beyond the familiar realms of chatbots and coding assistants into a diverse array of practical, production-ready applications. Professionals are leveraging their capabilities to solve tangible business problems, often by treating them as powerful engines for data processing and automation rather than just conversational partners.

The New Workhorse: Data Classification and Extraction

A dominant theme is the use of LLMs as highly effective text classifiers and data extractors, tackling tasks that were previously difficult or tedious to automate with traditional methods. This approach is being called "Software 3.0"—a paradigm shift from writing explicit, rule-based code (Software 2.0) to programming a model's behavior with natural language prompts and structured output requirements (e.g., JSON schemas). This allows for more flexible and robust solutions that can handle a wider variety of unstructured inputs.

Examples of this in action include:

Job & Content Tagging: Scraping job sites and using an LLM to automatically categorize listings by role (e.g., manager, individual contributor) and field (e.g., software, mechanical), making them easily searchable.
Nuanced Sentiment Analysis: Going beyond simple positive/negative sentiment to distinguish between criticism of a platform (like Uber Eats) and criticism of a specific service provider on that platform.
Automated Data Entry: Processing unstructured documents like invoices or schedules. One developer built a tool to extract billing rates from variously formatted invoices, saving users minutes of monotonous work daily. Another automates the conversion of schedules from PDFs into structured .ics calendar files.
Smart Onboarding: Scraping a new customer's website to pre-fill their account settings and preferences, creating a more seamless and personalized onboarding experience.

Boosting Internal Efficiency with Custom Tools

Companies are building powerful internal tools that directly impact productivity and streamline complex processes.

SQL Agents: Teams are creating custom SQL agents that have context of their database schemas. These agents can generate complex queries from natural language prompts, saving significant time for both technical and non-technical staff.
M&A and Directory Merging: One of the most unique use cases involved a company undergoing frequent mergers and acquisitions. They use an LLM to map employee roles from the acquired company's directory to their own predefined roles, drastically simplifying access control and IT integration.
Internal Research Assistants: LLMs are being hooked up to internal data lakes and knowledge bases, acting as "deep research" assistants that can answer complex questions about a company's proprietary data.
Call Center Automation: To improve efficiency without sacrificing quality, call centers use LLMs to interpret a customer's free-text input and classify it into a predefined intent. The system then responds with a carefully crafted, pre-approved message, keeping the LLM's generative capabilities "on rails" to ensure safety and consistency.

Practical Considerations and The Cost-Benefit Equation

While the possibilities are exciting, successful implementation requires a practical approach. A recurring pattern is the use of LLMs in a human-in-the-loop system. The model acts as "extra eyes" to surface signals from vast amounts of data or as an assistant to handle the first 98% of a task, with a human performing the final validation. This mitigates the risk of hallucinations and ensures high accuracy.

A key discussion point was cost. While processing a true "firehose" of data like high-volume server logs can become expensive, for many use cases, the cost is trivial compared to the value generated. The call center example is a perfect illustration: if a single deflected call saves the company $5, that one event can pay for millions of tokens of LLM inference, making the ROI exceptionally high.