How to Improve Your Process For Diagnosing Problems using AI

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AI is a wonderful – albeit sometimes unfathomable tool. For businesses around the world, this technology has the potential to automate time-consuming and costly tasks such as data collection, market research, and content generation.

Through doing this, it can allow your company to make better decisions, and increase efficiency and productivity, while also helping your business to find new capabilities and expand the original model.

But of course, you already know all of this, because you’ve already integrated AI into your business model. The reason you’ve clicked on this article is that you have realized that AI tech is not completely foolproof.

Although the benefits that we’ve listed are all legitimate, there are issues with AI that can make it become more of a liability than an asset.

This technology is complex, after all, and that means that – when issues arise – it can be very difficult to diagnose them and find the appropriate solutions.

Diagnosing Problems With AI

Take LLMs – large language models – for instance. Even the most successful models such as ChatGPT and OpenAI have run into several challenges, including inaccuracy, transparency, and irresponsible use of personal data.  

For your company, the important thing about a LLM model is not just its training, but the application of LLM observability to keep it monitored. When it comes to LLMs, or any ML model for that matter, observability is crucial to ensure the performance remains strong, reliable, and responsible.

Key Areas That Require Observation

There may be a number of reasons why your AI is experiencing problems. For one, the data used to train it might have been insufficient, which has led to AI hallucinations, bias, and integrity issues.

As well as this, it could be that your AI is failing to comply with current GDPR regulations – whether that’s through a lack of explainability, transparency, or accuracy. If you are running a LLM model, it could even be that the LLM is receiving bad prompts, which similarly leads to inaccurate responses.

AI hallucinations, specifically, can completely derail an AI project, especially if you do not have the means to spot and diagnose it. So in order to improve your ability to diagnose, you need to be able to observe each of these areas and pinpoint the problem before it becomes an issue for the business. This can be done through a specific AI observability platform that enables efficient monitoring, identification and troubleshooting to resolve problems when they arise.

Safeguarding Your AI

As with any other tool, AI and ML have the potential to underperform. But it’s not such an issue if you have the platform to identify anomalies and behaviour traits that need to be fine-tuned. Fixing the problem becomes a lot easier when you have diagnosed the problem, and you cannot diagnose it without being able to observe. T

This is the best way to not only improve the process of diagnosis right now but subsequently safeguard your AI model for the long term. Remember, this is a problem that every company will face, but not every company knows how to deal with it. In this way, as more and more businesses integrate AI into their systems, now is the best time to create – and maintain – the perfect model to stay one step ahead of your competition.