Large Language Models (LLMs) are now widely available from multiple providers. Businesses don’t need to build their own models—instead, the key advantage comes from using them effectively. This article covers how companies can create useful applications with LLMs, use A/B testing to measure engagement, and set up a feedback loop to find the best model and setup for their needs.
LLMs Are Becoming Commodities
LLMs have moved from cutting-edge innovations to widely available tools. Providers like OpenAI, Anthropic, Meta, and Google offer high-quality models via APIs, making it easy to integrate AI into applications. Since most models perform similarly, the value now comes from how they are applied, fine-tuned, and optimized for specific use cases.
How to Build Useful Applications with LLMs
To get the most out of LLMs, businesses should focus on:
- Understanding User Needs – Identifying real problems AI can help solve, such as customer support automation or content creation.
- Choosing the Right Model– Selecting a model based on factors like cost, speed, and accuracy.
- Optimizing with Fine-Tuning – Improving responses using prompt design, retrieval-augmented generation (RAG), and domain-specific training.
- Ensuring Scalability – Deploying the model in a way that handles varying user demand and integrates smoothly with existing systems.
Measuring Success with A/B Testing
Just deploying an LLM-powered application isn’t enough—ongoing evaluation is crucial. A/B testing helps businesses measure and refine model performance.
Steps to Run A/B Tests for LLMs:
- Define Success Metrics – Identify key indicators like response accuracy, speed, and user engagement.
- Set Up Experimentation – Run different model configurations side by side.
- Collect User Data – Analyze how users interact with each setup.
- Iterate Based on Results – Optimize prompts, model parameters, and fine-tuning based on A/B test data.
- Creating a Feedback Loop for Continuous Improvement - A/B testing is useful, but a continuous feedback loop ensures long-term success.
Key Elements of a Feedback Loop:
- User Feedback Collection – Gathering explicit (ratings, surveys) and implicit (clicks, time spent) feedback.
- A/B testing models – Compare two models or configurations.
- Model Fine-Tuning – Using feedback data to improve model accuracy and relevance.
- Deployment - Deploy new model and repeat the loop.
Conclusion
As LLMs become commodities, businesses must focus on how they apply and optimize them. Building useful applications, rigorously testing them, and continuously improving through feedback loops will ensure they stay competitive. By focusing on user engagement and data-driven iteration, companies can maximize AI’s potential and unlock new opportunities.
Top comments (1)
great insights