Understanding Large Language Models: Beyond the Buzzwords
- POSTED ON
- January 4, 2025
- BY Natali E.
- POSTED ON
- January 4, 2025
- BY Cristina H.
Artificial Intelligence has taken center stage in technology discussions, with Large Language Models (LLMs) getting a lot of attention. ChatGPT, for example, became the fastest-growing application of all time, reaching 100 million users just two months after launch.
AI’s ability to generate human-like text and perform complex tasks has led many to view them as revolutionary tools. People see LLMs as a kind of magic box. Unless you are a technologist, most believe that AI can create wonders. However, this perception can lead to misunderstandings about what LLMs are and how they function.
To leverage their potential effectively, decision-makers must move past the buzzwords and evaluate these tools with a critical eye.
What Are Large Language Models (LLMs)?
LLMs are sophisticated AI systems trained on massive datasets to predict and generate outputs. They excel at pattern recognition and identifying sequences of data to provide likely responses. For instance, they can power chatbots, summarize lengthy documents, or assist with data analysis. However, their capabilities hinge on recognizing patterns, not understanding meaning.
“It doesn’t know what it says. It just looks at the patterns.” – Ravi Samtani – Chief Tech Creator, Intuitive Data Analytics
Rather than “understanding” in the human sense, LLMs rely on statistical models to analyze probabilities. When asked a question, they scan their training data to determine the most common or relevant response. This process enables AI tools to generate convincing outputs without understanding the underlying meaning.
In practical applications, this can be limiting.
Let’s say you are a university trying to figure out the underlying reason why students are dropping out. The LLM might give you some broad overviews, but lack the ability on its own to provide actionable recommendations and test various strategies.
- Correlations can get quite complex. Changing one variable often impacts others in unintended ways. You need a way to test all of these variables in concert with the ability to tweak each of them to find the right combination to solve your problem.
- Let’s say you are a university trying to figure out the underlying reason why students are dropping out. The LLM might give you some broad overviews, but lack the ability on its own to provide actionable recommendations and test various strategies.
In each case, you need a way to run what-if scenarios using real-world data to find the optimal mix of solutions. AI tools need human insight and intuitiveness to be effective.
LLMs in Action: Practical Applications
By proposing implementations based on data analysis.
Customer Service Automation
Chatbots driven by LLMs handling customer inquiries to reduce wait times.
Data Analysis
Chatbots driven by LLMs handling customer inquiries to reduce wait times.
Education and Training
Summarizing complex topics.
Code Generation
Creating code and identifying errors or structure problems.
However, LLMs continue to be prone to errors and hallucinations. These models can fill in details or information that doesn’t exist, but appear likely. As such, you need human oversight for all of the current iterations of AI models.
Intuitive Data Analytics (IDA) enhances these capabilities by focusing on actionable insights. Unlike generic AI solutions, IDA emphasizes real-world applicability, leveraging targeted data sets, and user-friendly interfaces. This approach ensures that AI outputs are relevant and actionable rather than vague or overly broad predictions.
You can also access data sets and drill down to key data points to ensure the accuracy and logic of your insights. And, IDA lets you play with underlying variables to see how everything interrelates.
The Limitations of LLMs
Despite their strengths, LLMs have significant limitations.
Misconceptions and Overhype
Many users believe these models can solve any problem or operate autonomously without human intervention. However, their effectiveness is limited to specific contexts. For example, while they excel at identifying patterns, they struggle with niche or highly specific scenarios where their training data may not apply. This is sometimes referred to as the “60/40 problem,” where LLMs favor the most common patterns and may fail in less typical cases.
“You cannot depend on 60% correct answers to run your business.” – Larry Tong, Innovation Strategist, Intuitive Data Analytics
Technical and Practical Challenges
There are also technical and practical challenges with LLMs:
- Accuracy and Context
Generating inaccurate or irrelevant responses, especially when tasked with analyzing unfamiliar datasets. - Transparency Issues
Lacking the ability to fully explain their reasoning, making it difficult to trust outputs. - Resource Intensiveness
Requiring substantial computational power and ongoing fine-tuning to ensure alignment.
Requiring substantial computational power and ongoing fine-tuning to ensure alignment.
Moving Beyond the Buzz
To leverage the value of LLMs, you need to integrate them into your existing systems and have oversight as part of your workflow. Think of it in terms of electric vehicle production. Simply replacing a traditional engine with an electric one doesn’t work unless you retool much of the rest of the vehicle. In a similar way, adopting LLMs requires aligning them with workflows, data processes, and goals.
IDA’s approach exemplifies this principle.
By tailoring AI tools to specific datasets and emphasizing usability, IDA bridges the gap between raw AI capabilities and practical business needs. This ensures you get meaningful insights without falling prey to the pitfalls of generic solutions.
“Decision-makers should focus on aligning AI tools like LLMs with organizational goals for meaningful results – and leverage human-powered intelligence.” Larry Tong – Innovation Strategist, Intuitive Data Analytics
Harnessing AI’s True Potential
- LLMs are powerful tools, but they are not magical solutions. Their value lies in the ability to enhance processes when implemented strategically and paired with human intelligence. As you navigate the evolving AI landscape, make sure you understand the potential and the limitations of LLMs and AI tools.
- By focusing on targeted use cases and leveraging platforms like IDA, you can move beyond the hype to harness AI’s true potential.
See IDA in action and start unlocking better business decisions today.
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Hi I'm Jane
I'm a techie and occasionally dabble in writing on all things IDA. I'm tasked to bridge the gap between technology and its users, making boring topics accessible and engaging. Beyond tech, you'll find me cooking, reading and going to the gym to find balance to fuel my creativity and nerdy-ness.
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