ChatGPT in Medicine by MEDIROBOT

* Cost: AI companies often charge based on how much text the AI has to process (measured in "tokens"). When an AI "thinks step-by-step," it generates a lot more text (all the reasoning steps) before giving the final answer. This increase in text directly translates to a higher cost for the user.

Therefore, especially when the accuracy benefit is small or non-existent (as with newer models), you are paying a high price in time and money for very little gain.

---

### Question 5: What is the authors' main purpose for writing this report, and who is their intended audience?

Simple Answer:

The authors' main purpose is to provide practical, data-driven advice to people who actually use AI in their daily work. They want to move beyond hype and give clear guidance on what works and what doesn't.

The report explicitly states its audience is "business, education, and policy leaders." Essentially, this is for anyone who isn't an AI researcher but needs to make informed decisions about how to use AI effectively and efficiently. They are trying to correct a common misunderstanding and help people save time and money by not using an outdated or inappropriate technique.

---

### Question 6: Based on the report's findings, what is the practical advice for someone using AI today? When should they use CoT, and when should they avoid it?

Simple Answer:

Here is the practical advice from the report, boiled down into simple rules:

#### When you SHOULD consider using "Think Step-by-Step":

* You are using an older or less advanced AI model that isn't great at reasoning on its own.
* The task is very complex, and a slightly better *average* performance is more important than getting every single simple part perfect.
* You are not concerned about speed or cost.

#### When you SHOULD AVOID using "Think Step-by-Step":

* You are using a state-of-the-art AI model (like the latest from Google, OpenAI, etc.), as these models likely already do this by default. Telling them to do it is redundant.
* Perfect consistency is crucial. If you need the AI to be 100% reliable on tasks it can easily do, CoT might introduce random errors.
* Speed and cost are important. If you need a fast, cheap answer, CoT is one of the worst things you can do.

One final, very important piece of advice from the report: Be careful about forcing the AI to *only* give you the final answer (e.g., "Just provide the letter of the correct answer and nothing else"). The report found that many modern AIs think step-by-step by default, even when not asked. Forcing a direct answer can prevent this helpful internal process and actually make the AI's answer *worse*. The best approach is often to just ask the question naturally and let the AI respond as it sees fit.

### Analysis of "The Decreasing Value of Chain of Thought in Prompting"

---

### Question 1: What is the central argument of this report regarding the 'Chain of Thought' (CoT) prompting technique?

Simple Answer:

The main point of this report is that the popular technique of telling an AI to "think step-by-step" — known as Chain-of-Thought (CoT) — is not the magic fix for getting better answers that many people believe it is. Its usefulness is decreasing, especially with newer and more advanced AI models. Whether you should use it depends entirely on which AI you are using and what you are asking it to do. It is not a one-size-fits-all solution.

---

### Question 2: How does the effectiveness of CoT prompting change depending on the type of AI model used?

Simple Answer:

The authors tested two different categories of AI models and found a clear difference in how they responded to the "think step-by-step" instruction.

* For "Non-Reasoning" Models (like standard GPT-4o, Sonnet 3.5): These are the general-purpose AI models that most people use today. For these, telling them to think step-by-step can still be useful. It generally boosts their *average* performance, helping them get more complex questions right. However, the report found a strange side effect: it can also cause them to make mistakes on easy questions they would have otherwise answered correctly.
* For "Reasoning" Models (like the experimental o3-mini, o4-mini): These are newer, more powerful models specifically designed to be good at complex reasoning from the start. For these models, telling them to "think step-by-step" provides almost no benefit. They are already thinking in a structured way internally. Forcing them to write it all out just wastes time and money without making the answers any more accurate.

In short, the trick is becoming less useful as AI models get smarter and more capable on their own.

---

### Question 3: What is the key trade-off the authors discovered when using CoT prompting?

Simple Answer:

The key trade-off is a classic case of "higher average vs. perfect consistency."

Imagine a student taking a math test.
* Without showing their work (Direct Answer): The student might quickly answer all the easy questions correctly but get stuck on the hard ones. They get a decent score but miss the high-value problems.
* By showing their work (CoT / Step-by-Step): The student now carefully works through every problem. This helps them solve the *hard* problems they would have missed. However, in the process, they might make a simple calculation error on an *easy* problem, which they would have gotten right before.

This is exactly what the report found with some AI models. Using "step-by-step" prompting:

* Benefit: The AI's *average score* across all questions went up, because it could solve more difficult problems.
* Cost/Downside: The AI's *perfect consistency* went down. It started making occasional errors on questions it would have aced every single time when asked for a direct answer. It became less reliable on the "easy stuff."

So, you have to decide what's more important for your task: a better average performance, or guaranteed correctness on the simpler parts?

---

### Question 4: What is the impact of using CoT prompting on the cost and speed of getting an answer from an AI?

Simple Answer:

The impact is significant and negative. Using the "think step-by-step" method makes the process much slower and more expensive.

* Time: The report's charts (Figure S1) show that getting an answer took anywhere from 35% to 600% longer when using the step-by-step prompt compared to a direct request. A task that takes 2-3 seconds could suddenly take 15-20 seconds.

🤖✨ Discovering How AI “Thinks” Behind the Scenes! ✨🤖
Hey everyone! 🎉 Have you ever wondered how those smart AI assistants (like ChatGPT) come up with their answers? A recent study digs deep into just that—and the results are super cool, even if you’re not a techie! Here’s the scoop in plain language:
🌟 What’s the Big Idea?
Researchers wanted to see two things separately:
1️⃣ Knowledge – What facts or “stuff” the AI already knows.
2️⃣ Reasoning – How the AI uses those facts to connect the dots and solve problems.
Instead of only checking whether the AI’s final answer was right or wrong, they looked at every step of the AI’s “thinking process.” Think of it as watching a student write out all their steps on a math or medical exam, rather than just seeing the final grade.
📊 Why Does That Matter?
More Transparency:
We often see only the AI’s final answer. By breaking down its process, the study shows exactly where the AI is strong (knowing facts) and where it might stumble (logical reasoning).
Better in Medicine vs. Math:
In medicine, having correct facts is crucial—one tiny mistake can lead to the wrong diagnosis. The study found that teaching AI more medical facts (through a process called “fine-tuning”) made it much better at medical questions.
In math, logic and step-by-step problem solving matter more. Here, a different training method (called “reinforcement learning”) helped the AI sharpen its reasoning skills.
Customized AI Training:
Since different fields (medicine, math, law, finance, etc.) have different needs, this research shows we can train AIs in a smarter way: focus on facts where it matters most, or focus on reasoning where that’s the priority.
🔍 Key Findings—Simplified!
When the AI was only taught medical facts, it got better at medical quizzes but its “how did you get that answer?” steps became a bit messy.
When the AI was trained to think more carefully through each step, it made fewer mistakes in its reasoning—even though it didn’t learn new facts as thoroughly.
For medical tasks, knowing the right facts was actually more important than fancy reasoning tricks. (Because medicine relies on accurate information!)
For math challenges, reasoning clearly was the key to success.
💡 What’s So Awesome About This?
Safer, More Trustworthy AI: In fields like medicine, we need to trust that the AI isn’t just guessing. This study’s approach shows exactly where the AI might be confident in a fact or where it might be on shaky logical ground.
Tailored AI Helpers: Imagine AI doctors that are really solid on medical facts, or AI tutors that excel at guiding you through each step of a tricky math problem. This research is a big step toward that future.
Understanding AI “Thought”: By breaking down how AI thinks, we can spot and fix mistakes earlier, making AI more reliable for everyone.
🎈 In a Nutshell:
This research peels back the curtain on AI decision-making, separating the “what it knows” from “how it reasons.” Whether it’s helping doctors or solving math puzzles, we can now train AIs to be smarter and safer in exactly the ways we need them to be. How cool is that? 🤩
Feel free to share and spread the word—AI is only going to get more amazing! 🚀✨

36. ☯️Create Stunning AI Presentations for FREE! 🚀

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👉 IMPORTANT FIRST STEP: You'll need to sign in to Google AI Studio at the link above before you can use the app. It's quick and easy!
What can it do for you? So much! 👇

📄 Multiple Input Options: Upload your text files, PDFs, DOCX, HTML, JSON, or simply paste your content. You can even just give it a topic!

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Generate images with AI (powered by Imagen 3.0!) right within the app based on your descriptions.
Quickly search Google Images for inspiration.

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✨ Iterative Refinement: Get an initial draft, then enhance it! Add images, provide further text instructions (like "make it more formal" or "add a slide about X"), and even use the full history of your changes for better AI context.

🎤 Voice-to-Text: Dictate your content for topic queries, pasted text, and image descriptions using your microphone!

🎲 Experimental Interactive Slides: Feeling adventurous? Let the AI design a unique, engaging interactive slide for your presentation!

📝 Plain Text for PowerPoint: Export the slide content as plain text, perfect for manually creating or importing into PowerPoint or other editors.

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🛠️ AI Error Helper: If something goes wrong, the app can even try to explain the error in simple terms using AI!


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RADDOC ☯

Difference between previous llms(gpt4o/claude 3.5 sonnet/meta llama) and recent thinking/reasoning llms(o1/o3)


Think of older LLMs (like early GPT models) as GPS navigation systems that could only predict the next turn. They were like saying "Based on this road, the next turn is probably right" without understanding the full journey.

The problem with RLHF (Reinforcement Learning from Human Feedback) was like trying to teach a driver using only a simple "good/bad" rating system. Imagine rating a driver only on whether they arrived at the destination, without considering their route choices, safety, or efficiency. This limited feedback system couldn't scale well for teaching more complex driving skills.

Now, let's understand O1/O3 models:

1. The Tree of Possibilities Analogy:
Imagine you're solving a maze, but instead of just going step by step, you:
- Can see multiple possible paths ahead
- Have a "gut feeling" about which paths are dead ends
- Can quickly backtrack when you realize a path isn't promising
- Develop an instinct for which turns usually lead to the exit

O1/O3 models are trained similarly - they don't just predict the next step, they develop an "instinct" for exploring multiple solution paths simultaneously and choosing the most promising ones.

2. The Master Chess Player Analogy:
- A novice chess player thinks about one move at a time
- A master chess player develops intuition about good moves by:
* Seeing multiple possible move sequences
* Having an instinct for which positions are advantageous
* Quickly discarding bad lines of play
* Efficiently focusing on the most promising strategies

O1/O3 models are like these master players - they've developed intuition through exploring countless solution paths during training.

3. The Restaurant Kitchen Analogy:
- Old LLMs were like a cook following a recipe step by step
- O1/O3 models are like experienced chefs who:
* Know multiple ways to make a dish
* Can adapt when ingredients are missing
* Have instincts about which techniques will work best
* Can efficiently switch between different cooking methods if one isn't working

The "parallel processing" mentioned (like O1-pro) is like having multiple expert chefs working independently on different aspects of a meal, each using their expertise to solve their part of the problem.

To sum up: O1/O3 models are revolutionary because they're not just learning to follow steps (like older models) or respond to simple feedback (like RLHF models). Instead, they're developing sophisticated instincts for problem-solving by exploring and evaluating many possible solution paths during their training. This makes them more flexible and efficient at finding solutions, similar to how human experts develop intuition in their fields.