Enterprises floating like a butterfly, but AI stinging like a bee- How enterprises are getting punched by LLMs daily!

Enterprises floating like a butterfly, but AI stinging like a bee- How enterprises are getting punched by LLMs daily!

Introduction

Muhammad Ali once said: Float like a butterfly, sting like a bee. In the fast-moving ring of Artificial Intelligence, enterprises are floating through innovation, only to be stung painfully—by the rapid evolution of AI models. The sheer speed of advancements, companies are finding themselves in a never-ending fight, trying to keep up with technology that evolves faster than their ability to adapt. And then there are mixture of experts (pun intended) who will add their own twist and interpretation (we include ourselves in that gang)

Just like a boxer stepping into the ring, enterprises don’t always know what kind of opponent they’re facing. One moment, AI is an agile sparring partner, helping them innovate. The next, it’s an unpredictable heavyweight, landing unexpected blows—hallucinations, misinterpretations, or outright contradictions. The challenge isn’t just adopting AI; it’s staying on their feet while AI keeps changing the rules of the fight.

The white papers published are very complex and hard to comprehend for all. One way to show how these models work is with an example. That’s why we put these AI models to test in a real-world scenario that anyone can grasp.

By analyzing how different AI models handle the same input, we can attempt to understand what really matters—how well these models interpret, summarize, and act on real customer concerns.

We tested three AI models—DeepSeek, LLaMA 3.2, and Gemini thinking model—to analyze the same customer feedback. The differences in their responses were striking. This article breaks down their performance in simple, real-world analogies, making it easy for everyone to understand how these models handle feedback differently.

Feedback: I had a terrible experience with the support team. It took them 3 days to respond to my issue, and even then, they did not provide a clear resolution. On the positive side, the product itself is great when it works. However, the billing department overcharged me twice, and when I contacted customer support, they were unhelpful. I spoke to Sarah from billing, but she just redirected me to another team. This kind of service is unacceptable. The technical department should improve their response time and clarity.

We provided the AI models with this customer review, containing both positive and negative sentiments. The models were asked to analyze the feedback across four key areas:

• Sentiment score (from -10 to +10)
• Summary of feedback
• Issue analysis (root cause, impact, risk level)
• Recommendations (immediate and long-term actions)

Like three different restaurant managers handling the same review, let’s see how each AI model performed.

Sentiment score: Who reads the mood best?

• DeepSeek (-5): recognized negativity but softened the blow)
• LLaMA 3.2 (-5): called the feedback extremely negative, exaggerating slightly
• Gemini (-8): Aligned closest with human judgment, recognizing strong dissatisfaction

Key takeaway: DeepSeek stays neutral, LLaMA over-explains, and Gemini captures emotion most accurately.

Summary generation: who keeps it crisp?

• DeepSeek: focused only on the service issue but missed billing errors
• LLaMA 3.2: added extra details not in the review, making it unnecessarily long (we kept the temperature, top_p, top_k to be deterministic)
• Gemini: balanced and concise, mentioning both customer service and billing complaints

Key takeaway: DeepSeek oversimplifies, LLaMA talks too much, and Gemini strikes the right balance. .

Issue analysis: Who diagnoses problems best?

• DeepSeek: Identified slow response time as the problem but ignored deeper frustration
• LLaMA 3.2: Suggested multiple, unrelated root causes, like overanalyzing an X-ray
• Gemini: Pin-pointed billing issues as the real source of dissatisfaction

Key takeaway: DeepSeek is basic, LLaMA is overly analytical, and Gemini gets straight to the root cause.

Recommendations: Who gives the best advice?

• DeepSeek: Generic suggestion: Improve customer service. No clear strategy
• LLaMA 3.2: Too ambitious—recommended overhauling processes entirely
• Gemini: Suggested clear, practical steps, like reducing response time and fixing billing issues

Key takeaway: DeepSeek gives vague advice, LLaMA overcomplicates, and Gemini provides actionable solutions

DeepSeek’s FP8 precision: fast but at a cost

Since DeepSeek has kicked the AI world into an excited orbit, we decided to take a closer look at one of the key features: the use of FP8 precision. While FP8 allows for faster processing and lower memory usage, it comes with trade-offs in accuracy and detail retention. Let’s break down how this impacts DeepSeek’s performance in real-world scenarios.

But what is Floating Point (FP): Imagine you’re watching a cricket match on an old TV with low resolution. You can still see the action, but the details—like the bowler’s grip or the batsman’s footwork are blurry. That’s how FP8 (Floating Point 8-bit) precision works in AI. It processes data faster and more efficiently, but in doing so, it sometimes loses finer details.

Most AI models, including LLaMA 3.2 and Gemini, use FP16 (16-bit) or BF16 (BFloat16- BF16 is like using shorthand writing—it keeps the important parts while skipping unnecessary details, making AI calculations faster without losing accuracy) precision, which allows them to capture subtle variations in language, sentiment, and tone of expression in words. DeepSeek, on the other hand, operates with FP8, which means it sacrifices accuracy for speed.

Here’s how FP8 impacted DeepSeek’s performance in our test scenario:

• Sentiment score: it underestimated the strength of emotions, much like a waiter failing to sense a customer’s frustration
• Summary generation: it missed key details in the review, focusing only on major points while ignoring smaller but critical complaints
• Issue Analysis: it simplified the root cause, failing to see how multiple factors—like billing and support—contributed to customer dissatisfaction
• Recommendations: instead of tailored solutions, DeepSeek provided generic, broad-stroke advice

FP8 helps DeepSeek run efficiently on lower computing power, making it a great option for companies needing fast and cost-effective AI. However, businesses that rely on deep insights and accuracy may find that FP16 models like LLaMA 3.2 and Gemini offer a more comprehensive and nuanced understanding of customer feedback.

Key takeaway: FP8 makes DeepSeek a speedy but shallow analyst—great for quick insights but not ideal for businesses that need precise and emotionally intelligent AI.

Conclusion: Surviving the knockout punch

Enterprises are in the ring with AI, trying to stay agile while AI throws punches at an unrelenting pace. Some businesses dodge and adapt, while others take hits head-on. Just like in boxing, it’s not just about how hard AI hits—it’s about how well businesses adapt to AI’s punches. The key isn’t picking the most powerful AI—it’s choosing the one that works best for your business and the team that can deliver for you.

Image courtesy: ChatGPT