Top-k vs Top-p (Nucleus) Sampling the Secret Behind How ChatGPT Chooses Words

Most people think AI models like ChatGPT always pick the word with the highest probability.

But that's not true.

If they always picked the most likely word, every response would sound repetitive and robotic.

Instead, Large Language Models (LLMs) use sampling techniques to decide which word (token) to generate next.

Two of the most important sampling techniques are:

• Top-k Sampling
• Top-p (Nucleus) Sampling

If you're preparing for AI/ML interviews or learning how LLMs work, understanding these two concepts is essential.

Let's understand them from the beginning.

Step 1: How Does an LLM Generate Text?

Imagine you type:

"I love to drink"

The model doesn't immediately know the next word.

Instead, it predicts probabilities for every possible next token.

Notice something?

The model knows coffee is most likely.

But there are thousands of possible tokens.

Some make sense.

Some don't.

If the model randomly selected from every token, it might generate:

"I love to drink dinosaur."

Obviously, that makes no sense.

So we need a way to remove unlikely words.

That's exactly what Top-k and Top-p do.

Understanding Probability Distribution

Think of probability like a pie chart.

The model distributes 100% probability among all possible next words.

Example:

• Coffee → 40%
• Tea → 30%
• Water → 15%
• Juice → 8%
• Milk → 4%
• Others → 3%

Together they add up to 100%.

Sampling methods decide which part of this probability distribution we should consider before choosing the next word.

What is Top-k Sampling?

Top-k Sampling keeps only the k most probable tokens.

Everything else is discarded.

After removing the remaining tokens, the probabilities are recalculated (renormalized), and one token is randomly selected.

Suppose:

If:

k = 3

We keep only:

• Coffee
• Tea
• Water

Everything else is removed.

Now the model chooses only among these three.

Real-Life Example: Restaurant Menu

Imagine you're visiting a restaurant with 100 dishes.

The chef tells you:

"Today's Top-5 Special Dishes."

Instead of reading all 100 dishes, you choose only from those five.

That's exactly what Top-k does.

It says:

Ignore everything except the top k options.

Why is Top-k Useful?

It removes:

• Weird words
• Extremely unlikely tokens
• Random mistakes

This makes responses more natural.

The Limitation of Top-k

Here's the problem.

The value of k never changes.

Sometimes that's good.

Sometimes it's bad.

Imagine:

The model is extremely confident.

Probabilities:

If k = 50

The model still keeps 50 words.

But only 3 actually make sense.

The remaining 47 are unnecessary.

Now consider another situation.

The model is uncertain.

Suppose:

100 words all have similar probabilities.

But k = 20.

Now we're throwing away many reasonable choices.

So a fixed value isn't always ideal.

What is Top-p (Nucleus) Sampling?

Instead of keeping a fixed number of words,

Top-p keeps enough words until their combined probability reaches p.

For example:

Suppose:

Now set:

p = 0.90

Start adding probabilities:

Coffee = 40%

Coffee + Tea = 70%

Coffee + Tea + Water = 85%

Coffee + Tea + Water + Juice = 93%

We crossed 90%.

So we keep:

• Coffee
• Tea
• Water
• Juice

Everything else is removed.

Notice something?

We didn't decide beforehand to keep four words.

The data itself decided.

That's why Top-p is called dynamic sampling.

Real-Life Example: Election

Imagine a class election.

Candidates receive votes.

Suppose you only want candidates representing 90% of all votes.

Start adding:

A → 45%

A+B → 75%

A+B+C → 90%

Done.

You only keep A, B, and C.

The number of candidates changes automatically depending on vote distribution.

That's exactly how Top-p works.

Why is Top-p Better?

Because it adapts.

When the model is confident:

Maybe only two tokens cover 90%.

When the model is uncertain:

Maybe twenty tokens are needed.

The candidate set automatically grows or shrinks.

No manual adjustment is required.

Another Real-Life Example: Ice Cream Shop

Imagine an ice cream shop with 100 flavors.

Top-k

The owner says:

"Choose only from today's Top 10 flavors."

Even if only two flavors are actually popular, you still look at ten.

Top-p

Instead, the owner says:

"Choose from flavors that together account for 90% of today's sales."

If only three flavors sold most of the ice cream,

You only consider those three.

If customers bought many different flavors,

You consider more options.

This changes automatically.

Visual Comparison

Suppose the probabilities are:

Coffee → 40%

Tea → 30%

Water → 15%

Juice → 8%

Milk → 4%

Soup → 3%

Top-k = 3

Keeps:

Coffee

Tea

Water

Top-p = 90%

Keeps:

Coffee

Tea

Water

Juice

Different situations produce different numbers of candidates.

Can We Use Both Together?

Yes.

Many modern LLMs combine them.

Typical settings:

Temperature = 0.7

Top-k = 50

Top-p = 0.9

The process is usually:

1. Apply temperature to adjust randomness.
2. Keep only the top-k tokens.
3. From those, keep the smallest set whose cumulative probability reaches top-p.
4. Randomly sample one token from the remaining candidates.

This balances creativity with coherence.

Where Are These Used?

These sampling methods are used in:

• ChatGPT
• Claude
• Gemini
• Llama
• Mistral
• Qwen
• DeepSeek
• Most modern Large Language Models

Whenever an LLM generates text, these techniques help it choose the next token intelligently.

When Should You Use Which?

Use Top-k when:

• You want a simple and predictable limit.
• You know the number of candidates you want.

Use Top-p when:

• You want the model to adapt automatically.
• You want better quality and more natural responses.

Today, Top-p is generally preferred because it adjusts to the model's confidence instead of relying on a fixed number.

Common Interview Questions

Q1. Why not always pick the highest probability token?

Because the output becomes repetitive and deterministic. Sampling introduces controlled randomness, making responses more natural and creative.

Q2. What is the biggest drawback of Top-k?

Its candidate size is fixed, so it cannot adapt to different probability distributions.

Q3. Why is Top-p called Nucleus Sampling?

Because it keeps only the "nucleus" (core) of the probability distribution — the smallest group of tokens whose combined probability reaches the chosen threshold.

Q4. Which is better?

Top-p is generally preferred because it adapts to the model's confidence. However, many production systems combine Top-k and Top-p for balanced performance.

Key Takeaways

• LLMs predict probabilities for the next token.
• Sampling decides which tokens are allowed before making the final choice.
• Top-k keeps a fixed number of the highest-probability tokens.
• Top-p keeps a dynamic set of tokens whose cumulative probability reaches a threshold.
• Top-p adapts to different situations and usually produces more balanced outputs.
• Many modern LLMs combine Temperature, Top-k, and Top-p to generate fluent, coherent, and creative text.

Understanding these decoding strategies is one of the first steps toward mastering how Large Language Models actually generate language.

If this article helped you understand Top-k and Top-p Sampling, feel free to share it with others who are learning AI and LLMs. 🚀