Did you know? Two people can give the same AI writing prompt and receive different stories. Even the same person can repeat a prompt and see another wording. One reason is that generating language often involves choosing among several possible next tokens, rather than following one permanently fixed sentence.
This is where temperature, top-k and top-p sampling enter the picture. They influence how a generative model chooses the next token from scores it has already calculated. These settings can change variety and predictability, but they cannot turn an unsupported fact into a verified one.
Quick answer: read the current context → score possible next tokens → optionally adjust or filter those scores → select a token → append it → repeat. The selected sequence becomes the readable answer.
The 30-second explanation
- Token: a vocabulary unit that might be a whole word, part of a word or punctuation.
- Logit: an unnormalised numerical score a model produces for a token.
- Softmax: a mathematical transformation that can convert scores into a distribution.
- Temperature: a parameter that changes how concentrated the next-token distribution is.
- Top-k: keep up to k of the highest-scoring tokens when sampling.
- Top-p: keep the smallest set of leading tokens whose combined probability reaches a chosen threshold.
- Greedy decoding: choose the highest-scoring next token, without random sampling.
The names can be confusing because they all affect generation, but they do different jobs. Temperature reshapes relative probabilities; top-k and top-p filter candidates; sampling makes the final choice from what remains.
Step 1: The model considers what comes next
Imagine the fictional unfinished sentence, “The little robot walked into the…” A language model may assign scores to endings such as “garden,” “library,” “room” and other possibilities. Their ranking depends on the earlier prompt and the model’s learned parameters.
The model does not have to choose one complete word in a single operation. A text tokenizer may divide a word into several units, and the generation loop works at the model’s token level.
For a beginner’s explanation of those units, visit How AI Breaks Language Into Tokens. It helps explain why the visible text and internal token choices do not always line up neatly.
Step 2: Convert model scores into a distribution
The model calculates logits for its vocabulary. A softmax transformation can turn them into non-negative values that sum to one. For teaching, suppose our invented four-token distribution is garden 0.55, library 0.25, room 0.15 and station 0.05.
These fictional numbers sum to 1.00. They describe a distribution for the next choice in the toy example; they are not measurements from a named chatbot. A probability assigned to the next token is not a validated probability that a paragraph about the world is factually correct.
A model may have thousands of candidate tokens, so developers need efficient ways to choose among them. Different decoding methods lead to different text-generation behaviour.
Step 3: What happens with greedy decoding?
Greedy decoding simply picks the token ranked highest at each step. In our toy distribution, it selects “garden” because 0.55 is larger than the other values. On another step it again selects whichever token has the highest score under the newly extended context.
This may be suitable for certain predictable tasks, but a string of individually highest-scoring choices is not guaranteed to be the best complete sentence. It can also become repetitive in some generation settings.
If sampling is disabled in a library such as Hugging Face Transformers, temperature and candidate sampling settings should not be assumed to operate exactly as they would in a sampling-enabled generation call.
Step 4: Temperature controls how sharp the choices are
Temperature is commonly applied by scaling token logits before softmax. A lower positive temperature tends to concentrate more probability on the leading options; a higher temperature tends to flatten the distribution and permit more variety. The exact effects depend on the starting logits and decoding configuration.
Think of four possible words. With a very concentrated distribution, the leading choice appears most of the time. With a flatter one, less common alternatives can be selected more often. Neither setting ensures a more meaningful answer.
A setting described as “temperature zero” in a product may be implemented as deterministic decoding or another special case rather than literal division by zero. The documentation for that particular service determines how it works.
Important: A low temperature makes output selection more predictable; it does not make the underlying facts more trustworthy.
Step 5: Top-k limits how many options can be sampled
For top-k = 2, our toy example retains only “garden” and “library,” the two most probable candidates. “Room” and “station” are excluded for that step. The remaining distribution can be renormalised before a sample is drawn.
If the original probabilities are 0.55 and 0.25, their total is 0.80. After renormalising this simplified example, garden would receive 0.55 ÷ 0.80 = 68.75% and library 31.25% among the remaining candidates.
Top-k uses a fixed number of candidate tokens. That can be a useful constraint, but a fixed number may be too generous in a context with one obvious choice or too restrictive when many plausible options exist.
Step 6: Top-p adjusts the candidate set to the distribution
Top-p, also called nucleus sampling, keeps the smallest group of leading tokens whose cumulative probability reaches or exceeds the chosen p value. The number of retained tokens therefore varies with how the model distributes its scores.
Using the same four toy probabilities, top-p = 0.80 keeps garden at 0.55 and library at 0.25, reaching exactly 0.80. At top-p = 0.90, the first two total only 0.80, so the third token, room at 0.15, must also be included, making the cumulative value 0.95.
That difference shows why top-p and top-k are not synonyms. Top-k limits candidate count. Top-p determines a candidate set from cumulative probability. Some applications can combine filters; their precise order and defaults are implementation-specific.
Step 7: Match the method to the task
A fictional story prompt might benefit from controlled variety. A task that must produce a specific code, date format or mathematical result may need a stricter output procedure, but no temperature setting replaces checking the result.
For factual questions, clear evidence and suitable retrieval matter far more than merely lowering temperature. If the model lacks the source, a deterministic wrong claim remains wrong every time.
If you want two alternative story openings, requesting variants can be sensible. If you want the correct answer to a Secondary 1 equation, independent mathematical verification is a better safeguard than an especially confident writing style.
Step 8: Why early choices influence later sentences
After a token is selected, it becomes part of the context for the next step. This means a small early difference may affect later choices. “The robot entered the garden” naturally leads towards plants and paths, while “entered the library” leads towards books and shelves.
The model recomputes or updates its relevant internal state as the text grows. Some implementations use attention caches to avoid repeating selected calculations, but the language still depends on earlier generated content.
A different sampling seed may change the output, though repeating a seed is not a universal guarantee of identical text across changed models, hardware, software versions or sampling configurations.
How does beam search differ?
Beam search keeps multiple promising partial sequences instead of greedily choosing only one token at each step. It can be useful for some structured generation and translation tasks, though it has its own trade-offs and is not always the best option for open-ended conversation.
It is not simply “the most creative setting.” Search strategies need to be judged by the intended task, generation length, coherence and acceptable latency.
For the precise strategy flags and settings, the official Hugging Face generation documentation distinguishes greedy decoding, sampling, beam search, temperature, top-k and top-p.
Try it: be the token sampler on paper
Write four cards with our fictional token probabilities: garden 55%, library 25%, room 15% and station 5%. Imagine that one hundred draws reproduce those approximate frequencies, though real random samples will vary.
For top-k = 2, remove the two lower-scoring cards. For top-p = 0.90, keep three cards because the two leading ones sum to just 80%. Compare which endings remain possible.
Next finish a one-line story beginning “The robot walked into the…” under each candidate. The exercise shows how sampling influences variety without determining whether a story is factual or useful.
Five misunderstandings to avoid
- “Temperature makes the AI more knowledgeable.” It changes sampling behaviour, not training knowledge.
- “Top-p and top-k do the same thing.” One uses cumulative probability; the other uses a fixed candidate count.
- “A 70% next-token probability means the whole answer is 70% correct.” Those are different quantities.
- “Deterministic output is proof.” A model can repeat an incorrect statement consistently.
- “One setting is universally best.” Generation needs differ across code, creative writing, translation and factual explanation.
Frequently asked questions
What does temperature do in AI text generation?
It usually rescales token logits before sampling, influencing how concentrated the selection probabilities are.
What is the difference between top-p and top-k?
Top-k keeps a chosen number of leading token candidates; top-p keeps enough leading candidates to reach a cumulative probability threshold.
Is top-p = 0.9 a 90% chance the answer is correct?
No. It is a filtering threshold for next-token sampling, not a measure of factual correctness.
Can the same prompt produce different answers?
Yes, when the generation process includes sampling or other varying conditions. Exact reproducibility depends on implementation and settings.
Can I fix hallucinations by setting a low temperature?
Not reliably. Better evidence, appropriate retrieval, task checks and honest uncertainty are needed.
Your one-minute takeaway
- Language generation repeatedly chooses among next-token candidates.
- Temperature, top-k and top-p control different aspects of selection.
- Sampling diversity and factual reliability must be evaluated separately.
Authoritative reading and connected articles
- Hugging Face Transformers: Generation Configuration — official definitions for temperature, top-k and top-p.
- Hugging Face: Generation Strategies — greedy, sampling and beam-search methods.
- How AI Breaks Language Into Tokens — the units being selected.
- How Transformers Use Attention to Read Context — how token scores depend on context.
- Why AI Sometimes Gives Confident but Wrong Answers — why fluency and truth differ.
- Secondary 1 Mathematics Tutor Clementi — the importance of independently checked mathematical reasoning.
- How AI Works: Complete Overview — the broader reading path.