To address the chronic problem of AI giving different answers to the same question, a new benchmark has emerged that verifies not just the data format, but the 'actual content' accuracy.
Introduction: What if Your Home Calculator Changed Its Answer Based on Its ‘Mood’?
Have you ever imagined this? You bought milk for 1,500 won and bread for 2,000 won at a convenience store this morning. Naturally, you stood at the counter ready to pay 3,500 won, but the calculator screen showed ‘3,500’ the first time, ‘Three thousand five hundred’ in words the second time, and ‘Roughly about 4,000’ the third time. That calculator would likely be returned immediately.
The fundamental principle of every computer program we use is that it must be ‘Deterministic.’ Simply put, if you input 1+1, it must result in the exact same ‘2’ yesterday, today, and tomorrow. That is the only way we can trust machines with important tasks.
| However, the Large Language Models (LLMs)—AI trained on vast amounts of data to converse like humans, such as ChatGPT—that are shaking the world today slightly deviate from this common sense. Even if you ask the same question and keep the internal settings identical, the answers change subtly every time. This is technically referred to as a ‘Non-deterministic’ characteristic [A Complete Guide to LLM Benchmark Categories | Galileo.ai](https://galileo.ai/blog/llm-benchmarks-categories). |
Recently, an attempt to stabilize the ‘fickle mouth’ of AI became a hot topic in the technology community ‘Hacker News.’ News broke about the emergence of a new ‘Benchmark’ (a standard exam to measure AI performance) that measures how consistent and accurate AI responses are Hacker News AI Digest 2026-04-30 · Issue #844…. Today, we will explore why AI answers keep changing and what solving this means for our lives.
Why It Matters
The Reason We Need a “Reliable Assistant” More Than a “Smart Friend”
If we only used AI as a conversation partner for fun, slight changes in answers wouldn’t matter much. In fact, it might be more interesting if it said something different every time. However, the story changes the moment AI enters our ‘work.’
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Reliability of Software Development: Imagine a company building a system that automatically organizes customer order data using AI. If you tell the AI, “Organize the order history in JSON format (a standard specification for efficient data exchange),” and it writes the date as ‘2026-05-04’ one time and ‘May 4th’ another time, the computer waiting for that data will throw an error and stop. To prevent such issues, ‘Unit Testing’ (the process of independently verifying if the smallest units of a program work correctly) is essential, but if the answer keeps changing, testing itself becomes impossible Unit Testing for LLMs: Why Prompt Testing is Crucial for Reliable….
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Format Compliance Doesn’t Mean the Answer is Right: Until now, AI tests mainly looked at how plausible the ‘tone’ or ‘format’ was. However, no matter how perfect the shell (format) is, it’s useless if the content (actual values) inside is wrong ShowHN: AnewbenchmarkfortestingLLMsfordeterministic….
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The Key to Accident Prevention: Throughout 2025, there were cases where AI was hastily introduced without proper performance evaluation, leading to unexpected accidents. These were man-made disasters that could have been prevented if a comprehensive and professional evaluation system had been in place [LLM Evaluation Benchmarks and Safety Datasets for 2025 Knowledge Hub](https://responsibleailabs.ai/knowledge-hub/articles/llm-evaluation-benchmarks-2025).
The Explainer
The Bungeo-ppang Mold is Pretty, but What if it’s Filled with ‘Soy Sauce’?
To understand the core of this new benchmark, let’s use the ‘Bungeo-ppang’ (Korean fish-shaped pastry) analogy.
Metaphorically speaking, existing performance measurement methods (like JSON Schema Bench) mainly inspected how sophisticated the ‘Bungeo-ppang mold’ was. They only checked if the pastry baked by the AI properly took the shape of a fish and if the tail was attached—meaning, whether the ‘Schema’ followed the agreement. If the AI baked it in a fish shape, it was given a “Pass!” ShowHN: AnewbenchmarkfortestingLLMsfordeterministic….
However, what really matters when we buy Bungeo-ppang is the ‘filling’ inside. What if the outward appearance is a perfect fish, but it’s filled with soy sauce instead of red bean or custard? It would be completely inedible. This new benchmark strictly examines whether this ‘filling’ (actual values) is accurate and whether the same taste (consistent answer) is maintained every time it’s baked.
Experts agree that “simply verifying if the format is correct (Parse) is only a minimum requirement and is not enough” Introducing SOB: A Multi-Source Structured Output Benchmark for…. It means we must go beyond AI that merely mimics the outward form and reach a level where the core content is also reliable.
Why Does AI Keep Saying Different Things?
| To use an analogy, the inside of an AI’s head is like a ‘sea of probability.’ When an AI receives a question, it calculates the next word to follow “Today’s weather is…”. If there’s an 80% chance of “sunny” and a 20% chance of “clear,” the AI sometimes chooses the 20% option. Because of this trait, developers are losing sleep trying to ensure ‘consistency of answers’ when applying AI to actual financial or medical services [A Complete Guide to LLM Benchmark Categories | Galileo.ai](https://galileo.ai/blog/llm-benchmarks-categories). |
Where We Stand
The Outcry from the Field: “Format Errors are Driving Me Crazy!”
When news of this benchmark broke on Hacker News, many developers expressed their strong agreement. In this discussion, which garnered 48 points and 21 comments Hacker News AI Digest 2026-04-30 · Issue #844…, many professionals welcomed the emergence of this performance standard, stating that “the problems caused by AI failing to output structured data properly have been a persistent pain.”
The AI industry is currently verifying AI ‘skills’ from various angles beyond this:
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Specialized Domain Testing: In the medical field, specific ‘Medical LLM’ measurement standards are being established to prevent misdiagnosis [A Complete Guide to LLM Benchmark Categories Galileo.ai](https://galileo.ai/blog/llm-benchmarks-categories). There are even unique attempts to test how logically AI plays Gomoku [VueHN2.0 I built abenchmarkfortestingLLMsplaying Gomoku](https://vue-hackernews-ssr-5cavbdjcta-ew.a.run.app/item/47930262). -
Algorithm Solvers: How well AI can solve complex coding problems (Leetcode) or algorithm competition problems has become a significant metric. Recently, OpenAI announced how high its latest models scored on these difficult problems, showcasing its technical prowess [Testing LLMs on Solving Leetcode Problems in 2025 HackerNoon](https://hackernoon.com/testing-llms-on-solving-leetcode-problems-in-2025). - Leveling Up the Exams: As standard exams (like MMLU) became too easy for AI, ‘enhanced versions’ that increase the number of choices to 10 or require much more complex reasoning are continuously being released LLM News Today (May 2026) – AI Model Releases.
What’s Next
Beyond “Smart AI” to “Error-Free AI”
In the future, the key criteria determining the value of an AI model will not be just “how well it speaks,” but “how consistently reliable it is.”
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The Era of Microscopic Verification: Starting from 2025, the global trend in evaluating AI is to verify it across seven core dimensions, including ethics, consistency, and accuracy, rather than just one or two indicators [LLM Evaluation Benchmarks and Safety Datasets for 2025 Knowledge Hub](https://responsibleailabs.ai/knowledge-hub/articles/llm-evaluation-benchmarks-2025). - A Showdown of Real Data: Models that only provide polished-looking data will fall behind. Only models whose figures and facts remain consistent will survive in the business field ShowHN: AnewbenchmarkfortestingLLMsfordeterministic….
- Predictable Daily Life: As developers gain complete control over AI behavior through prompt testing (the task of finely adjusting and verifying instructions given to AI), the instances of being flustered by AI saying something nonsensical in the apps or services we use will gradually disappear Unit Testing for LLMs: Why Prompt Testing is Crucial for Reliable….
MindTickleBytes AI Reporter’s Perspective
Have you ever thought “machines still have a long way to go” after seeing an AI say something nonsensical? In fact, that ‘nonsense’ is another face of ‘creativity,’ where AI generates new ideas like a human. However, in a professional setting where ‘accuracy’ is a hundred times more important than creativity, that nonsense becomes the most fearsome enemy.
The new benchmark introduced today is like asking the AI to “temporarily take off the fancy hat of creativity and put on the hat of a diligent archivist.” When AI begins to pass these rigorous ‘consistency exams’ with high marks, we will finally be able to entrust important tasks like bank transfers or hospital surgery appointments to AI with peace of mind. At that point, AI will no longer be an interesting toy to us, but an indispensable and reliable partner.
References
- ShowHN: AnewbenchmarkfortestingLLMsfordeterministic…
- Hacker News AI Digest 2026-04-30 · Issue #844…
- Introducing SOB: A Multi-Source Structured Output Benchmark for…
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[Testing LLMs on Solving Leetcode Problems in 2025 HackerNoon](https://hackernoon.com/testing-llms-on-solving-leetcode-problems-in-2025) -
[A Complete Guide to LLM Benchmark Categories Galileo.ai](https://galileo.ai/blog/llm-benchmarks-categories) -
[VueHN2.0 I built abenchmarkfortestingLLMsplaying Gomoku](https://vue-hackernews-ssr-5cavbdjcta-ew.a.run.app/item/47930262) - Unit Testing for LLMs: Why Prompt Testing is Crucial for Reliable…
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[LLM Evaluation Benchmarks and Safety Datasets for 2025 Knowledge Hub](https://responsibleailabs.ai/knowledge-hub/articles/llm-evaluation-benchmarks-2025) - LLM News Today (May 2026) – AI Model Releases
FACT-CHECK SUMMARY
- Claims checked: 19
- Claims verified: 19
- Verdict: PASS
- Deterministic
- Non-deterministic
- Automation
- They only check the data format and not the accuracy of the actual values
- AI response speed is too slow
- They don't understand JSON format at all
- Speed testing
- Prompt Unit Testing
- Design testing