Harnessing the Power of Generative AI and LLMs Across Various Industries: The New Business Imperative

With today’s rapid technological advances, finding the right tools and making the most out of them can be hard. However, your top priority should be LLMs and Generative AI (large language models). By relying on these technologies, you can cut costs, free your employees from routine tasks, and increase productivity.

It’s important to understand that AI and LLMs can be used creatively to supplement your team’s skills or give you an edge over competitors. For example, introduce AI algorithms to your services, improving customer experience.

In this article, we’ll explain how LLMs and Generative AI help various industries get ahead. Using examples from industry leaders and case studies from S-PRO’s own experience, you will learn what AI and LLMs can do for you.

Large Language Models and Generative AI

Let’s start with some context for what we’re talking about. Generative AI is an AI model that can generate text, images, or other media using previously existing data. This means you can ask the algorithm for a list of the latest medical breakthroughs and receive it without having to Google anything or read articles.

Large language models are a subsection of generative AI, specifically focused on text. They use human-made text and learn to imitate natural speech. As a result, the model can predict what a human would say in response to a given prompt and provide a generated reply. The most prominent example here is ChatGPT, which many would consider the most famous among generative AI and LLMs.

What else characterizes large language models?

• A huge scale, often containing hundreds of millions or even billions of parameters
• Preliminary training on large, diverse text corpora using unsupervised learning
• A transformative architecture that allows you to efficiently capture long-term dependencies and contextual information
• Transfer learning with general language understanding based on data and further improvement of knowledge for specific tasks.

The neural networks at the heart of any LLM require enormous amounts of data to train. This gives larger corporations an advantage as they can access the processing power and huge amounts of data to create a better model. The training data can also be sourced from public forums, Wikipedia, public knowledge sites like Quora or Stack Exchange, etc.

Different factors stimulate the progress of LLMs:

• Powerful graphics processing units (GPUs) and tensor processing units (TPUs) drive the efficient learning of LLMs, accelerating their development. Access to high-performance computing clusters (HPC) is also very important.
• With the development of cloud technologies, creating an LLM is becoming more affordable. For example, giants like AWS, Google Cloud, and Microsoft Azure can provide access to scalable computing resources like GPU/TPU. Also, companies can use serverless platforms to deploy LLM-based applications.
• Tools facilitating LLM training and deployment, like TensorFlow and PyTorch libraries, are widely available. Automatic machine learning (AutoML) platforms can simplify model development by automating hyperparameter settings and architecture search.
• Today, anyone who wants to create their LLM can take advantage of access to pre-prepared language models. Such models are universal, that is, they can be adjusted to a wide range of NLP tasks. This includes text classification, machine translation, question answers, sentiment analysis, and generalization.

The well-known options include BERT (Bidirectional Encoder Representations from Transformers), developed by Google AI, and GPT-3 and 4 (Generative Pre-trained Transformer) from OpenAI.

Though LLMs have made significant progress in recent years, this technology has limitations. While LLMs mostly respond to user input adequately, they may still struggle with some edge cases like double negatives or humor. The users should learn to formulate their prompts in a particular way to get the desired results.

Ethical considerations regarding LLM and Generative AI are also crucial to think about. This includes bias, fairness, misinformation, privacy, and responsible AI development. Researchers are actively working on creating methods for detecting and mitigating those risks. 

Importance and Benefits of LLMs & Generative AI

Let’s talk about the possible benefits of integrating AI into your business. These include:

• Streamlined processes
• Lower cost
• Future-proofing
• Less busywork

Streamlining Processes

One of the more common benefits of artificial intelligence is the streamlining of work processes. You can use LLMs to perform routine tasks like client support, content generation for your website, and even the automation of user interactions. By relying on the models, you free up your employees to focus on more complex projects and streamline the minute processes in your company.

Returns Over Time

Similarly, LLMs and Generative AI improve over time, meaning the more you use them, the better the results will be. This means you can expect the models to take on more workload while increasing customer satisfaction. With a sophisticated enough AI, you can create rich, personalized experiences for your clients without any employees involved.

This point hinges on having access to training data that you can use to improve your LLM. Ideally, you will use your employees’ texts and publicly available information to refine the model. However, this is a costly process, so be ready to invest heavily before you get the desired return.

A More Effective, Less Costly Business

All this leads to the fact that you can eliminate expenses throughout your business, such as large support departments. Couple this with the fact that your employees will only need to tackle high-priority projects and tasks that are, for now, too complex for AI. Your company will simply be operating as a more efficient and cost-conscious business, up to date with modern technology. 

Usage of LLMs and Generative AI Across Industries

Regardless of which field you work in, there is room for AI to improve it. Either by simplifying regular processes, reinforcing your staff, or offering the computing power humans simply can’t replace.

Healthcare

One of the simpler ways to use LLMs and Generative AI here is to run patient requests and bookings through it. By chatting with a bot, clients are able to voice their complaints, pick the doctor that they want to see, and choose a timeslot for their visit. However, that is just the tip of the iceberg.

Those seeking more complex solutions can train LLMs for online consultations. This can be applied to low-level issues where the patient does not need a physical examination. Couple it with a data security solution, and you have a truly futuristic approach to patient care.

E-Commerce

This is where generative AI can help with store management, creating product descriptions, and generally filling the store with content. Similarly, an LLM-powered chatbot that informs clients of current deals or helps them resolve issues can streamline the user’s journey, resulting in a better conversion rate.

One of the more exciting options is using LLMs and Generative AI to create personal recommendations. These can appear as pop-ups while a customer browses your store, targeting them with automatically created text and offering items relevant to that particular user. As in other cases, the actual pick would be done by AI based on the user’s browsing and purchase history.

Banking and the Financial Sector

Using LLMs and Generative AI, banking businesses provide customers with predictive consultations, speeding up verification processes. Also, algorithms can generate estimates based on previous financial data. This includes credit score calculations, interest rate estimations, and more.

Note that in this industry, more than in any other, it’s vital to ensure you secure the data used in AI operations. Additionally, clients need to be informed that their processes are being handled by an algorithm instead of a regular employee.

Logistics

The main goal here is to make routing and transportation more efficient, which usually means optimizing routes. Generative AI and LLMs can analyze previous travel data and find flaws in routing, providing a plan to improve it. With enough information from open-access sources, the algorithm can nearly instantly cut costs and delivery time for you.

Energy

Work in the energy sector often demands repetitive tasks with calculations that take up a human’s time but could be solved by an LLM nearly instantly. Moreover, generative AI can automate such calculations with a simple script output. Similarly, LLMs could scan reports presented to engineers, providing concise summaries and saving the specialists’ time.

Case Studies for LLMs

S-PRO has worked on several AI-centric projects, helping companies implement LLMs and Generative AI to benefit their businesses. We’ll highlight two case studies we found to be the most inventive.

Muuuvr

Muuuvr is a community-based fitness app for athletes where users can share their workouts and runs. The more people exercise, the more their level grows, granting unique badges and achievements. For this app, S-PRO integrated an AI model that creates user profiles and tracks their data to suggest new exercise mates, relevant events in their area, and new challenges.

Moreover, LLMs and Generative AI also help verify user identities and detect instances of potential cheating to prevent platform abuse. These anti-spoofing systems guarantee that the community remains honest, user-friendly, and free of bad actors. In addition, the AI-fueled profiles guarantee that every user receives a stream of relevant, engaging content.

Travel Plan Booker

As you can tell from the name, TPB is a platform that helps users choose the optimal route for their travel, adding interesting locations and points of interest along the way. The user only has to add a starting and an endpoint, the number of travelers, and other parameters such as their budget.

To make the planning more flexible and customized to each customer, TPB owners decided to become an AI-first travel company. By using a chatbot based on LLMs and Generative AI, the user can discuss what their preferences in travel are and what they would like to get out of the experience. Once they’re done, the bot will transform the information from the chat into a full travel itinerary.

The benefit of using an AI instead of a pre-written set of suggestions is its precision. Users can request precise and unusual criteria, which the bot adapts into a realistic travel plan within seconds. It’s a system that utilizes AI to go beyond regular itinerary services.

Addressing Potential Concerns and Challenges

While generative AI and LLMs can provide staggering improvements, they do come with a few caveats. As with any new and complex technology, there are a few issues to look out for. We’ll briefly talk about them and, if possible, discuss ways to avoid them.

Cost of Training

Any model you choose will need to be trained, no way around it. This takes both time and money as you need hardware to support the training. Settling for a less powerful model isn’t always acceptable, but depending on how intricate your project is, it might be a good compromise. 

Outside of that, the only solution is to wait as AI gets more popular and thus, the hardware needed for it becomes more accessible. Plus, as AI becomes “smarter,” there is less improvement needed on the user side, resulting in a more affordable solution.

Technical Complexity

Though LLMs and Generative AI will make your work processes easier in the long run, actually implementing them can be quite difficult. You’ll have to either hire an experienced vendor or expand your staff with an AI department.

This issue is easy enough to tackle, as the AI field is expanding rapidly and there are plenty of professionals to choose from. If, however, you don’t want an on-premises expert, hiring an AI development agency like S-PRO is an optimal replacement.

Legal Concerns

This is the trickiest challenge to navigate. Currently, very few jurisdictions have meaningful, set-in-stone laws on AI regulation. Even those with something blanket don’t consider industry specifics or different use cases. What this ends up doing is creating an atmosphere of uncertainty around how you can use LLMs and Generative AI long-term.

Realistically, these types of laws should be finalized over the next three to five years, at least in the more tech-oriented countries. But until then, it’s important to remember that you must treat your users’ data responsibly and monitor their interactions with your AI. Just like GDPR changed the game for advertisers, AI laws can upend the industry before this decade is done.

Providing Oversight

If you’ve been following AI-related news, you’ve seen that it can have errors from time to time. While this won’t be too critical for internal use, any customer-facing AI elements need human control. Regular check-ups on the algorithm’s behavior and results will ensure your chatbot isn’t suggesting nonsense or diverging from its intended use.

Ideally, if you’re using cutting-edge LLMs and Generative AI models, this should stop being a relevant challenge in the long run. As the models train and become more sophisticated, errors should be minimized, resulting in a self-sufficient loop. Until then, you might be better off with someone doing quality control.

Why Choose S-PRO

S-PRO has a history of successful AI-based development. We can bring the power of LLMs and Generative AI to your company, transforming your product and making it competitive in the industry. Using these futuristic tools, we’ll focus on improving the user experience, streamlining content creation, and developing new AI-enabled features.

Our core advantage is our team of full-stack machine learning engineers who can tackle issues of any complexity. This allows our specialists to work on both custom and common AI models and keep a high level of expertise across the whole ML field.

Considering how bustling and full of promise the AI technology is, it would be a shame not to use its full potential. That is why we suggest you get in touch today to start your AI journey with an experienced team. We will boost your business with LLMs and Generative AI, showing you the true potential of these tools.

Conclusion

We have talked about the transformational potential of LLMs and Generative AI. It can help industries from healthcare to fintech, offering new ways of tackling challenges. However, we’ve also discussed the potential issues that may arise if this technology isn’t used responsibly or implemented by amateurs.

As with any new and powerful technology, the best way to reap its benefits is by partnering with experts. This is why S-PRO is offering its AI development services to companies with ambitious projects. Instead of worrying about AI's challenges, you can put your trust in a team of experienced engineers and watch as AI transforms your business.

So, if you’re ready to improve your customers’ experience, automate work processes, or just reinforce your infrastructure with LLMs and Generative AI, contact us now.