Large language models (LLMs) are becoming the bread and butter of modern NLP applications and have, in many ways, replaced a variety of more specialized tools such as named entity recognition models, question-answering models, and text classifiers. As such, it’s difficult to imagine an NLP product that doesn’t use an LLM in at least some fashion. While LLMs bring a host of benefits such as increased personalization and creative dialogue generation, it’s important to understand their pitfalls and how to address them when integrating these models into a software product that serves end users. As it turns out, monitoring is well-posed to address many of these challenges and is an essential part of the toolbox for any business working with LLMs.
Monitoring is often billed by SaaS companies as a general solution that can be commoditized and distributed en-masse to any end user. At Mona, our experience has been far different. Working with AI and ML customers across a variety of industries, and with all different types of data, we have come to understand that specificity is at the core of competent monitoring. Business leaders inherently understand this. One of the most common concerns we find voiced by potential customers is that there’s no way a general monitoring platform will work for their specific use-case. This is what often spurs organizations to attempt to build monitoring solutions on their own; an undertaking they usually later regret. Yet, their concerns are valid, as monitoring is quite sensitive to the intricacies of specific use cases. True monitoring goes far beyond generic concepts such as “drift detection,” and the real challenge lies in developing a monitoring plan that fits an organization’s specific use cases, environment, and goals. Here are just a few of our experiences in bringing monitoring down to the level of the highly specific for our customers.
Large language models (e.g., GPT-4) seem poised to revolutionize the business world. It’s only a matter of time before many professions are transformed in some way by AI, as GPT can already generate functional code, review and draft legal documents, give tax advice, and turn hand-sketched diagrams into fully-functioning websites. Among the roles most likely to be affected by GPT are those involving sales, marketing, customer support, and media, although it’s almost impossible to imagine a domain that won’t in some way be affected by GPT. While certain tasks invariably demand a human touch, it’s likely that the focus of many roles will shift toward these key human endeavors and away from those that can be automated. With all this in mind, it’s pertinent to ask what challenges organizations are likely to encounter as they begin to invest in advanced AI and which roadblocks developers are likely to run up against as they work to incorporate GPT APIs into software products. While it is still too early to anticipate all possible hurdles teams using GPT are likely to experience, our understanding of AI and large language models suggest at least a few that will be particularly prominent.
More than ever before, people around the world are impacted by the advancement in AI. AI is becoming ubiquitous and it can be seen in healthcare, retail, finance, government, and practically anywhere imaginable. We use it to improve our lives in many ways such as automating our driving, detecting diseases more accurately, improving our understanding of the world, and even creating art. Lately, AI is becoming even more available and “democratized” with the rise of accessible generative AI such as ChatGPT.