🦃 LLM Word of the Week: Sampling
(Thanksgiving Edition — because even AI deserves a holiday metaphor)
Some weeks you want to go deep. This week? We go deep and we keep it fun — because it’s Thanksgiving.
Today’s word is Sampling — the process an LLM uses to choose the next word in a sentence. Think of it like a Thanksgiving buffet, but for language.
So what is Sampling (in plain English)?
When an LLM generates text, it doesn’t “know” the next word. Instead, it considers many possible next words — each with a probability.
Then it chooses one.
Sampling is the rule that decides which word gets picked.
And here’s where the Thanksgiving analogy fits perfectly:
The model stands at a buffet with many dishes (words).
- Turkey might have a 60% chance
- Mashed potatoes might have 20%
- Stuffing might have 10%
- Cranberry sauce: 5%
- Green bean casserole: 2%
- Tofurkey: 0.0001%
Sampling determines: Does it grab the predictable turkey… or take a wild scoop of something unexpected?
🥧 Why Sampling matters
Sampling is one of the biggest reasons LLMs feel human.
It creates:
- Variety (responses aren’t identical every time)
- Creativity (unexpected but valid choices)
- Personality (the “voice” of the model)
Without sampling? Models would sound robotic and repetitive — like reading the same recipe card over and over.
With sampling? Models can write poems, jokes, stories, metaphors, and answers that feel alive.
Sampling + Temperature = Flavor
Temperature controls how daring the model is when sampling.
-
Low temperature → predictable answers Like always choosing turkey, mashed potatoes, and gravy.
-
High temperature → creative answers Like putting mac & cheese on top of stuffing because it “just felt right.”
Sampling decides what’s available on the table. Temperature decides how brave the model is when choosing.
A visual way to understand it
You’re standing at a Thanksgiving buffet.
Each dish represents a possible next word. Bigger trays = higher-probability words. Smaller trays = rare words.
Sampling = how you choose. Temperature = your mood.
If you’re cautious → turkey every time. If you’re adventurous → sprinkle cranberry sauce on everything.
That’s literally how LLMs write.
Why Sampling is a big deal in AI
Sampling determines whether a model is:
- boring
- creative
- consistent
- chaotic
- useful
- unpredictable
Different tasks need different sampling styles:
- Coding or math → low randomness
- Brainstorming or storytelling → higher randomness
- Customer service → stable and predictable
- Naming startups → unhinged creativity allowed
It’s one of the simplest knobs… but one of the most powerful.
Recent research worth watching
Modern papers are exploring:
- Adaptive sampling (model adjusts temperature based on uncertainty)
- Top-k / Top-p sampling (filters probability space for better coherence)
- Classifier-free guidance (steers outputs in a direction without killing creativity)
- Mixture-of-samplers (switch strategies depending on the context)
Sampling isn’t just picking a word — it’s shaping the personality of AI-generated language.
Final thought
If training is the “cooking,” and inference is the “serving,” then sampling is the moment you choose what goes on your plate.
It’s the bridge between pure probability and genuine creativity.
It’s the reason models don’t just complete sentences — they express them.
Happy Thanksgiving from the world of LLMs! May your sampling always be flavorful and your outputs ever creative.