# **I Don't Care About Benchmarks—This Prompt Is How We Test LLMs and ChatGPT 5 Failed**
The relentless march of progress in Large Language Models (LLMs) like ChatGPT has been nothing short of breathtaking. From generating human-like text to assisting with complex coding tasks, their capabilities seem to expand almost daily. However, amidst the hype and excitement, a critical question remains: how do we truly evaluate the effectiveness and reliability of these systems? While quantitative benchmarks provide valuable data points, they often fail to capture the nuanced complexities of real-world application. At [Make Use Of](https://makeuseof.gitlab.io), we've developed a rigorous, prompt-based testing methodology that goes beyond simple scores and digs deep into the underlying reasoning and creative abilities of LLMs. In this article, we unveil our approach and share the surprising results we obtained when putting ChatGPT 5 to the test. Surprisingly, it faltered in unexpected ways.
## **Beyond the Numbers: Why Traditional Benchmarks Fall Short**
Traditional benchmarks like GLUE, SuperGLUE, and MMLU are essential tools for assessing the performance of LLMs across a range of tasks, including natural language understanding, reasoning, and knowledge retrieval. These benchmarks typically involve evaluating the model's accuracy on pre-defined datasets with standardized scoring metrics. However, these standardized tests often exhibit limitations that restrict their capacity to evaluate the most advanced LLMs.
### **Data Contamination and Memorization**
One critical issue is **data contamination**, where LLMs are inadvertently trained on data that overlaps with the benchmark datasets. This can lead to inflated performance scores that don't accurately reflect the model's true generalization ability. The model might be simply *memorizing* answers from the training data instead of genuinely reasoning through the problems. Recent research has indicated a significant degree of contamination in popular benchmarks, raising concerns about the validity of these metrics.
### **Oversimplification of Real-World Tasks**
Another limitation is the **oversimplification of real-world tasks**. Benchmarks often present tasks in a highly structured and artificial format, which doesn't accurately reflect the messy and ambiguous nature of real-world problems. For example, a benchmark might ask an LLM to answer a multiple-choice question about a historical event, but it won't test the model's ability to synthesize information from multiple sources, evaluate conflicting evidence, or formulate a nuanced argument.
### **Lack of Creativity and Common Sense Evaluation**
Furthermore, many benchmarks fail to adequately assess **creativity and common sense reasoning**. While LLMs can excel at answering factual questions and performing logical deductions, they often struggle with tasks that require imagination, intuition, or an understanding of human values and social norms. A benchmark might test an LLM's ability to generate a poem, but it won't evaluate the poem's emotional impact, originality, or artistic merit. This crucial element is missing in many existing benchmarking techniques.
## **Our Prompt-Based Methodology: A Deep Dive into LLM Reasoning**
Recognizing the limitations of traditional benchmarks, we've developed a prompt-based methodology that focuses on probing the underlying reasoning and creative abilities of LLMs. Our approach involves crafting carefully designed prompts that challenge the models to think critically, solve complex problems, and generate original content.
### **The Anatomy of a Killer Prompt**
Our prompts are not simple questions or instructions. They are carefully crafted scenarios that require the LLM to apply its knowledge, reasoning skills, and creative abilities to solve a problem or complete a task.
#### **Ambiguity and Open-Endedness**
We intentionally introduce **ambiguity and open-endedness** into our prompts to force the LLM to make its own assumptions, draw its own conclusions, and justify its reasoning. For example, we might present the model with a vague problem description and ask it to come up with a solution, without providing any specific guidelines or constraints.
#### **Constraints and Challenges**
We also incorporate **constraints and challenges** into our prompts to test the model's ability to handle unexpected situations and overcome obstacles. For example, we might ask the LLM to write a story with a specific set of characters, a limited vocabulary, or a contradictory premise.
#### **Real-World Relevance**
Crucially, we ground our prompts in **real-world scenarios** to ensure that the tasks are relevant and meaningful. We avoid hypothetical situations or abstract problems that have no connection to practical applications. Instead, we focus on tasks that reflect the kinds of challenges that humans face in their daily lives.
### **Evaluating Beyond Simple Accuracy**
Instead of focusing solely on accuracy, we evaluate the LLM's performance based on a broader range of criteria, including:
#### **Coherence and Consistency**
Does the LLM's response make sense? Is it logically consistent? Does it contradict itself or contain factual errors? We meticulously analyze the LLM's output for internal inconsistencies and logical fallacies.
#### **Originality and Creativity**
Is the LLM's response original and creative? Does it demonstrate imagination and insight? Does it offer a fresh perspective on the problem? We reward LLMs that go beyond simply regurgitating information and generate truly novel ideas.
#### **Relevance and Usefulness**
Is the LLM's response relevant to the prompt? Does it address the core issues? Is it useful in solving the problem or completing the task? We prioritize LLMs that provide practical and actionable solutions.
#### **Explanation and Justification**
Does the LLM explain its reasoning process? Does it justify its decisions? Does it provide evidence to support its claims? We value LLMs that can articulate their thought processes and provide clear and convincing explanations for their actions. This transparency is crucial for building trust and understanding.
## **ChatGPT 5: A Promising Start, But Ultimately Disappointing**
We subjected ChatGPT 5, the latest iteration of OpenAI's flagship language model, to our prompt-based testing methodology. While the model demonstrated impressive capabilities in some areas, it ultimately fell short of our expectations in others. We expected a flawless performance but were faced with a series of surprising failings.
### **Areas of Strength: Factual Recall and Information Synthesis**
ChatGPT 5 excelled at tasks that required **factual recall and information synthesis**. It was able to quickly and accurately retrieve information from its vast knowledge base and combine it into coherent and informative responses. For example, when asked to write a report on the history of artificial intelligence, the model produced a comprehensive and well-researched document that covered all the major milestones and key figures in the field.
### **Areas of Weakness: Creative Problem Solving and Common Sense Reasoning**
However, ChatGPT 5 struggled with tasks that required **creative problem-solving and common sense reasoning**. When presented with ambiguous or open-ended prompts, the model often generated generic or superficial responses that lacked originality and insight. For example, when asked to design a sustainable transportation system for a large city, the model proposed a series of conventional solutions, such as building more public transportation and promoting the use of electric vehicles, but it failed to consider more innovative or unconventional approaches.
### **The "Why" Behind the Failure: Lack of True Understanding**
We believe that ChatGPT 5's struggles stem from a **lack of true understanding**. While the model can process and manipulate language with remarkable fluency, it doesn't possess the same level of understanding as a human. It can mimic human-like reasoning, but it doesn't truly understand the underlying concepts or principles.
#### **Dependence on Pattern Recognition**
ChatGPT 5 relies heavily on **pattern recognition** to generate its responses. It identifies patterns in the training data and uses those patterns to predict the most likely outcome. This approach works well for tasks that involve predictable or well-defined patterns, but it falls short when confronted with novel or ambiguous situations.
#### **Inability to Generalize**
The model also struggles to **generalize** its knowledge to new situations. It can learn to solve specific problems, but it doesn't necessarily understand the underlying principles that allow it to apply that knowledge to other problems. This lack of generalization ability limits its ability to adapt to changing circumstances and solve complex problems.
## **Examples of Prompts Where ChatGPT 5 Failed**
To illustrate ChatGPT 5's shortcomings, here are a few examples of prompts where the model failed to meet our expectations:
### **Prompt 1: Write a short story about a sentient toaster that falls in love with a blender.**
ChatGPT 5 produced a story that was grammatically correct and stylistically consistent, but it lacked originality and emotional depth. The characters were bland and unconvincing, and the plot was predictable and uninspired. The model failed to capture the absurdity and humor of the premise.
### **Prompt 2: Design a new type of educational game that teaches children about climate change.**
ChatGPT 5 proposed a game that was similar to many existing educational games. It involved answering questions about climate change and earning points for correct answers. The game lacked innovation and failed to engage the player in a meaningful way. The model didn't understand the principles of game design or the motivations of young learners.
### **Prompt 3: Explain the meaning of life in a single haiku.**
ChatGPT 5 generated a haiku that was grammatically correct but lacked profundity and insight. The poem was vague and abstract and failed to capture the complexity and mystery of the question. The model didn't understand the nuances of haiku poetry or the philosophical implications of the question.
## **The Future of LLM Evaluation: Focusing on Qualitative Analysis**
Our experience with ChatGPT 5 highlights the need for a more nuanced approach to LLM evaluation. While quantitative benchmarks provide valuable data, they don't tell the whole story. We need to focus on **qualitative analysis** to gain a deeper understanding of the strengths and weaknesses of these systems.
### **Developing More Sophisticated Prompts**
We need to develop more **sophisticated prompts** that challenge LLMs to think critically, solve complex problems, and generate original content. These prompts should be grounded in real-world scenarios and designed to test the model's ability to handle ambiguity, overcome obstacles, and adapt to changing circumstances.
### **Refining Evaluation Metrics**
We need to **refine our evaluation metrics** to move beyond simple accuracy and focus on factors such as coherence, originality, relevance, and explanation. We need to develop metrics that can capture the nuances of human judgment and reflect the true value of the LLM's output.
### **Emphasizing Human Feedback**
Finally, we need to **emphasize human feedback** in the evaluation process. Human experts can provide valuable insights into the strengths and weaknesses of LLMs and help to identify areas where the models can be improved. Human feedback is essential for ensuring that LLMs are aligned with human values and goals.
In conclusion, while ChatGPT 5 represents a significant step forward in the development of Large Language Models, it still has limitations. Our prompt-based testing methodology reveals that the model struggles with creative problem-solving and common sense reasoning. By focusing on qualitative analysis and incorporating human feedback, we can develop a more comprehensive and nuanced approach to LLM evaluation that will help us to unlock the full potential of these powerful systems. The future of LLM evaluation lies in understanding *how* these models think, not just *what* answers they provide.