The impact of AI in today’s world is unavoidable. Much of the “grunt work” involved in business processes have been delegated to AI, since they do not require specialized knowledge to complete. Beyond this though, AI is being utilized majorly in customer service, cybersecurity, and administration. It’s even beginning to make waves in the medical field.
Given this information, it seems only prudent to consider whether your business should step into the world of AI. This question alone isn’t enough though. LLMs are able to perform a wide variety of tasks, this is true, but not every LLM can perform the same task with the same efficiency.
So, the question becomes, what do you need AI for? Also which LLM will perform the best given the task you want to allocate it?
A Refresher on LLMs
Let us start with a brief refresher on Large Language Models, or LLMs. LLMs are foundational models, designed to understand and generate text similar to how humans do. Not only that, they are trained on a variety of datasets and behaviors that allow them to accomplish more complex tasks based on context. You may have heard of the more famous ones dominating the tech world these days; Chat GPT, Gemini, or DeepSeek.
LLMs have the ability to identify “context” and based on that context, they generate relevant responses or undergo certain procedures. Simple and repetitive tasks, with a limited knowledge base to draw from, are particularly easy to train AI for.
Tasks like writing basic lines of code, doing the “grunt work” as mentioned earlier, are not difficult to automate via AI. In fact, Google already generates about 25% of their code through AI.
Some tasks are more complex though and require the LLM to be trained on much larger and detailed data sets. Consider chatbots (that depend mostly on language-training and conversation-training) assisting potential clients on a company’s website, to AI-Powered cybersecurity applications (that are more dependent on behavior training, including learning how to identify suspicious behavior).
The Differences between LLMs
Now that we’ve established a base understanding, let’s consider the different aspects of LLMs that we can measure out;
Basic Capability
The basic capability of an LLM can be measured through three major factors; Can the LLM be fine tuned? Can it work with Custom Data? And the amount of context the LLM can process, or its memory.
The viability of any given LLM for your business is based on these factors. Perhaps you need a larger context window and need it to work with custom data, but you can not fine-tune the processes the application carries out.
Accuracy
Accuracy is a priority for AI-Enabled applications, whether that refers to the accuracy of the program to retrieve specific information from a provided dataset, or its ability to draw from databases of General knowledge. This process is particularly important for LLMs that have to retrieve information for users from the internet, which has a mass of contradicting information.
Thus the viability of an LLM for your business in this regard depends on its accuracy in retrieving data that you need. Testing it with general or occupation specific questions is the best way to understand the accuracy of a LLM. Applications like HeySmarty need to have excellent accuracy, especially when they have to deal with a diverse range of questions, recalling specific, situationally useful, information.
Cost and Maintenance
Of course two major players in the game are how much the model itself costs (as well as the payment structure) and how easy it is to maintain and tweak as needed.
The cost factor can be broken down into the three main payment systems most software tend to use, a one-time-payment system, a token system, and a recurring subscription system.
You may have noticed that online AI tools tend to use one of the last two, consider Chat-GPT 4.0’s monthly subscription, or how you have to buy and spend tokens to utilize programs like Bland.AI which automate previously human processes like cold-calling.
Also, if you plan to use this LLM for the long run, does it come with a snappy support team and easy-to-understand documentation? To what extent will employees have to be trained to work with and maintain this software?
Compatibility and Security
Is your LLM of choice compatible with the technologies you already use? Does it have robust security protocols, or will integrating it into your system actually leave you with gaping security vulnerabilities? These are important questions too.
Many business owners are still playing catch-up with the advancements of the IT sector, especially AI. The, “if it ain’t broke don’t fix it” mindset does not apply anymore. To stay at the top of your game, routine upgrades need to be made, and technologies that businesses utilize must be up to date to allow them to take full advantage of the menagerie of business-forward software that floods the market day after day.
When we speak of security though, we are mostly referring to privacy. If the LLM you choose to pick stores and processes personal data to inform its behaviors then it needs to be both GDPR compliant and have a secure storage system for this data.
Security issues can lead to especially sensitive problems, it’s possibly a good idea to have professionals trained in cybersecurity inspect your infrastructure when implementing a software with such an impact in the workflow. Our cybersecurity team, for instance, combined with our AI experts, will make sure you’re covered with such integrations.
Scalability
This is a concern more for SMEs that see potential growth in their future and want to know that there is room for their use of the same software to grow. While some LLMs have a fixed number of requests and processes they can take care of in the given time, other LLMs (usually ones with a subscription based system) do have scalable options.
The best examples for such systems are ones that automate marketing practices, Customer.io, RafflePress, and Brevo, being some examples. Each one is differently scalable and has some pros and some cons.
Customer.io for example is extremely efficient and quick, but gets expensive once the customer base it works with exceeds 1000 recipients. RafflePress on the other hand is more affordably scalable but has been reported to be slower than its competitors
Latency
Finally there are Latency expectations. To put it bluntly, this is regarding the speed of the application. How quickly do you need the software to recall certain data and use it in a situation?
For marketing-centric tasks recall speed can be vital, if customers get bored waiting for chatbots to recall store inventories or pull up contact information they could leave, which means the business loses a sale.
Also consider the use of AI in the medical field, where doctors may need patient information within a certain time frame to avoid harm coming to their patients. AI medical devices could log changes in patient states and send warnings to hospitals if vitals fall below, or climb above, a certain threshold. This process must also be fast enough to make a difference though.
In conclusion, LLMs have a variety of use cases and are tailored to accurately carry out their processes. There is no “one show fits all” answer, and businesses have to take a step back, look at the services they offer, and decide which factors they value more.
Tools for Measuring LLM Viability
There are, ofcourse, programs that test different factors of LLMs. They provide “benchmarks”, if you will, of how capable an AI program is. When GPT-4 was released in March 2023, OpenAI talked about how their model performed on benchmarks such as MMLU, TruthfulQA, and HellaSwag. Applications that other vendors also reference when discussing the viability of their models.
MMLU or Massive Multitask Language Understanding is a benchmark that tests an LLM across 57 different subjects, like Math and Law, and requires the LLM to have a college student level understanding of these subjects for it to be considered viable.
TruthfulQA tests whether an LLM makes up information when it does not have access to it, a process known as “hallucination”.
HellaSwag is an acronym for “Harder Endings, Longer contexts, and Low-shot Activities for Situations With Adversarial Generations”. It tests an LLM’s “common sense”, by seeing how it responds to prompts within specific contexts.
NIHS stands for “Needle In a HayStack”, this tests an LLMs data retrieval ability. Asking the model to retrieve a specific strand of data (The Needle), from a giant database of information provided by it (The Haystack).
Conclusion
So, given all of this information what is the answer? What is the best LLM for you? Well each reader may have a different answer to this question, and with this skeleton you can narrow down the right model for your business. If you’re still having trouble figuring out how to leverage AI for your business, don’t worry, our AI experts here at Genetech Solutions have your back!
