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Large language models (LLMs) have revolutionized the way machines interact with humans. They are a subcategory of generative ai, with a focus on text-based applications, while generative ai is much broader and includes text, audio, video, images and even code.
amazon.com/what-is/generative-ai/”>AWS sums it up well: “Generative artificial intelligence (generative ai) is a type of ai that can create new content and ideas, including conversations, stories, images, videos and music. “Reuse training data to solve new problems.”
Generative ai has opened new frontiers in the ai landscape!
LLMs have the ability to generate human-like responses, but how should ai professionals use them? Is there a guide or approach to help the industry build trust with this cutting-edge technology?
That is precisely what we will discuss in this article. Then let's get started.
An assistant to start!!!
LLMs are essentially generative, so it is recommended to use them for purposes such as generating summaries and providing explanations and answers to a wide range of questions. Typically, ai is used to assist human experts. Similarly, LLMs can increase your understanding of complex topics.
Industry experts consider LLMs to be good sounding boards; Yes, they are good for asking validating questions, brainstorming, creating drafts, or even checking to see if there is a better way to articulate existing content. These recommendations give ai developers and enthusiasts the playing field to test this powerful technology.
Not just text, LLMs help generate and debug code as well as explain complex algorithms in an easy-to-understand way, highlighting their role in demystifying jargon to provide conceptual understanding tailored to different people.
Benefits!!
Now, let us look at some of the cases that underline the role of LLMs in generating efficiency. The following examples focus on generating reports and insights, and simplifying business processes.
Collaboration tools: Creating summary reports of data shared between apps like Slack is a highly effective way to stay informed about project progress. It can include details such as the topic, its current status, development so far, participants, action items, due dates, bottlenecks, next steps, etc.
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Supply chain: Supply chain planners are mostly in a firefighting situation to meet demand orders. While supply chain planning helps a lot, last mile delivery requires experts to gather in the war room to keep the supply chain plan intact. A lot of information is exchanged, often in text form, including knowledge that is also useful for future purposes. Additionally, the summary of such conversations keeps all interested parties informed about the status in real time.
LLM Adoption
With rapidly evolving technological advances, it is critical not to give in for fear of missing out, but rather approach it with a business-first mentality.
In addition to the suggestions proposed above, users should stay up to date and periodically check for new techniques and best practices to ensure effective use of these models.
Separate fact from fiction
Having discussed the benefits of LLMs, it is time to understand the other side. We all know there is no free lunch. So what does it take to make responsible use of LLMs? There are many concerns, such as model bias, potential misuse such as deepfakes, and their repercussions, which require greater awareness of the ethical implications of LLMs.
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The situation has worsened to the point that it has become increasingly difficult to separate human-generated responses from those of a machine.
Therefore, it is recommended not to take the information in such tools at face value; Instead, consider these tips:
- View models as tools that improve efficiency and not as a single point of truth.
- Get information from multiple sources and verify it before taking action – the set works very well in bringing together different points of view.
- While considering the importance and trust factor of information coming from multiple sources, always verify the source of information and citations, preferably those with the highest reputation.
- Please do not assume that the information provided is true. Look for contrary points of view, i.e. what if this is wrong? Gather evidence to help you refute that the information is incorrect, rather than trying to support its validity.
- The model answer often has gaps in its reasoning, read carefully, question its relevance and poke around to get to the right answer.
Tips to Consider When Prototyping LLMs
Let's go directly to the practical applications of LLMs to learn about both their capabilities and limitations. To start, prepare for multiple experiments and iteration cycles. Always stay informed about the latest industry developments to get maximum benefits from the models.
The golden rule is to start from business objectives and establish clear goals and metrics. Very often, performance metrics include multiple objectives in terms of not only accuracy, but also speed, computational resources, and cost-effectiveness. These are the non-negotiable items that must be decided beforehand.
The next important step is to choose the right LLM tool or platform that suits the needs of the business, which also includes considering the open or closed source model.
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The size of LLMs is another key deciding factor. Does your use case require a large model or small approximating models, which have fewer computational requirements, and are a good trade-off for the accuracy they provide? Note that larger models provide better performance at the cost of consuming more computational resources and, in turn, more budget.
Given the security and privacy risks that come with large models, companies need strong security barriers to ensure their end users' data is safe. It is equally important to understand the stimulation techniques to convey the query and obtain the information from the model.
These stimulation techniques are refined over time with repeated experiments, such as specifying the duration, pitch, or style of the response, to ensure that the response is accurate, relevant, and complete.
Summary
LLM is indeed a powerful tool for a variety of tasks, from summarizing information to explaining complex concepts and data. However, a successful implementation requires a business-focused mindset to avoid falling into ai hype and find a truly valid end use. Furthermore, awareness of ethical implications, such as verifying information, questioning the validity of answers, and being aware of potential biases and risks associated with LLM-generated content, promotes the responsible use of these models.
Vidhi Chugh is an ai strategist and digital transformation leader working at the intersection of product, science, and engineering to build scalable machine learning systems. She is an award-winning innovation leader, author and international speaker. Her mission is to democratize machine learning and break down the jargon so everyone can be a part of this transformation.
