The Days of "Train and Forget" Are Over for AI and LLM Apps

Like most people, you probably shop for groceries.

Now, imagine if your local grocery store manager checked expiration dates just once a month instead of every other day. Would you still shop there?

It’s the same for AI/LLM apps.

Frequently monitoring the outputs of these apps ensures that outdated, "spoiled," or even harmful information is detected and corrected before reaching users. So, how frequent is too frequent when it comes to monitoring these apps? Let’s explore.

The Need for Monitoring

It’s well established that AI/LLM applications succeed or fail based on their ability to deliver
responses that are accurate, helpful, and harmless.

• Accuracy ensures users receive the right information.

• Harmlessness safeguards against delivering harmful responses.

• Helpfulness means users gain real value from their interactions.

These qualities build trust and empower businesses to scale their AI solutions with confidence. For the last few years, AI leaders and teams managing AI/LLM apps have recognized the importance of optimizing these response attributes, leading them to monitor their apps’ outputs every few days, or even every day.

From Daily to Hourly Monitoring

However, when it comes to AI/LLM apps that provide answers related to the world’s “fresh” or “fast-changing” data—like breaking news, social media trends, major geopolitical events, and more–AI leaders have realized that even daily monitoring isn’t enough. In addition to accuracy, harmlessness, and helpfulness - app responses must also be optimized for freshness and recency.

Users asking about the evening’s football game expect up-to-the-minute details—like which team won, the final score, and who scored the winning touchdown. If the app can’t deliver, it risks losing user trust, which then leads to declining usage.

Moreover, AI/LLM apps today don’t just process text—they handle audio, video, and multimedia inputs and generate similar outputs. The value of these outputs hinges on how fresh, recent, accurate, and helpful they are to users.

Given the pace at which the world generates new information and data (that is then ingested by these apps), monitoring their outputs once a day—or even a few times a day—is no longer sufficient. At the same time, monitoring every minute is impractical. This is why hourly monitoring is fast emerging as a practical solution.

The Importance of Continuous Benchmarking

A key aspect of effective continuous monitoring is continuous benchmarking. This is important
for AI leaders for two reasons:

1. Competitive Benchmarking: It’s not enough for AI/LLM apps to be good—they must be better than their competitors. Continuous benchmarking ensures that apps stay ahead of the competition across accuracy, speed, and user satisfaction.
2. Internal Benchmarking: Even without direct competition, comparing new versions of AI/LLM apps against previous iterations helps identify performance gaps and drives ongoing optimization.

Case Study: e2f’s Role in AI/LLM Monitoring and Benchmarking

Given e2f’s decade-long expertise in AI data and LLM monitoring, one of the world’s largest AI and cloud service providers engaged us for hourly monitoring, evaluation and benchmarking.

Objective: Use e2f’s expertise to continuously improve their model's response accuracy and timeliness.

Scope: Monitor and analyze a steady stream of “query-response” datasets from an AI app serving millions of users across the world, 24/7, and deliver results back to the client every hour.

Execution:

• Assembled a global team of 250+ domain experts to provide diverse perspectives across regions and time zones.

• Evaluated real-world query-response datasets for clarity, ambiguity, and potential harm.

• Classified queries based on time sensitivity and data change rates.

• Reviewed responses for hallucinations, natural flow, completeness, and readability.

Impact: The results from e2f’s continuous monitoring efforts were used to fine-tune the AI provider’s models and consumer app to improve accuracy, helpfulness, and freshness.

Conclusion

The era of "train and forget" is over for AI and LLM apps.

For AI/LLM apps serving millions of users around the clock, continuous monitoring, benchmarking, and fine-tuning are essential. These practices ensure output quality, support ongoing innovation, and differentiate your app from the competition. While weekly or daily updates may have worked in the past, hourly monitoring is now the standard.

At e2f, we’ve been helping the world’s largest AI builders optimize the quality of their AI/LLM apps through continuous monitoring, evaluation, and benchmarking. Contact us to learn how we can help you serve your users better and stand out from the competition.

About e2f:

e2f provides Human Intelligence for Smarter AI with a 24x7 global model. Supporting leading
LLM and AI app builders, our experts deliver high-quality training, fine-tuning, evaluation,
benchmarking and monitoring data services to optimize AI outputs.