TL;DR: How LLMs Choose Words & Practical Tips for Entrepreneurs
Large language models (LLMs) select words by converting numerical scores called logits into probabilities with the softmax function. Sampling strategies, temperature scaling, top-k, and nucleus sampling, then guide their word choice, balancing creativity and predictability. Entrepreneurs can optimize effectiveness by tuning parameters to match desired outcomes, such as engaging chatbot dialogue or consistent marketing copy.
• Softmax Function: Turns logits into meaningful probabilities for token selection.
• Sampling Strategies: Control randomness and coherence (e.g., temperature, top-p).
• Real-World Application: Adjust key parameters for tailored business results.
Want smarter AI outputs for your business? Learn more with this guide.
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How LLMs Choose Their Words: A Practical Walk-Through
Understanding how Large Language Models (LLMs) generate text might seem esoteric, but it’s surprisingly practical, especially if you’re an entrepreneur who leans heavily on generative AI for insights or customer communication. In 2026, LLMs dominate industries ranging from design to software development, and yet few grasp the intricate mechanisms behind their ability to generate coherent, context-rich outputs. This guide aims to demystify how LLMs select words using logits, softmax functions, and sampling strategies like temperature scaling, top-k, and nucleus sampling.
As a serial entrepreneur, I’ve come to value practical applications of technology over technical jargon. So, buckle up as we take a closer look at how these models “decide” what to say, and how you can use interpretations of output distributions to shape creative and predictable results for your business.
How Do LLMs Generate Text?
LLMs don’t “think” about language the way humans do. Instead, they work through probabilities. At every step, they generate raw numerical scores, called logits, for every possible token in their vocabulary. These logits are then normalized into probabilities using a mathematical function called softmax, which creates a probability distribution. For instance, running softmax on logits might yield results like:
- “Effective”: 42%
- “Useful”: 25%
- “Predictable”: 18%
- “Random”: 10%
- “Unlikely”: 5%
The model “decides” which word to output next by selecting from this distribution based on the inference-time sampling parameters you set. Parameters like temperature, top-k, and top-p dictate whether the model’s choices lean toward creative, varied outputs or deterministic, safe selections. Let’s analyze these mechanisms further.
What Is the Softmax Function?
Softmax is essential to how LLMs process logits into usable probabilities. Without it, the raw logits generated by an LLM are merely scores, untethered to any sense of context or possibility. The softmax formula distills logits into normalized probabilities that sum up to 1:
p_i = e^(logit_i) / Σ e^(logit_j)
In practical terms, imagine a scenario where the model needs to complete this sentence: “The startup pitch was ___.” Raw logits might suggest “impressive, dull, groundbreaking, useless, generic.” By applying softmax, the system outputs probabilities proportional to the logits.
Here’s an important nuance: Softmax ensures the probabilities are relative. A word doesn’t stand on its own; its likelihood depends on competition from other tokens in that moment’s context. For entrepreneurs designing AI systems for content generation, this decision-making logic is critical to control final outputs.
How Does Sampling Work?
The sampling process determines which token the LLM selects from the probability distribution created by softmax. Unlike greedy decoding, which always selects the word with the highest probability, sampling introduces variation. There are three key sampling strategies that affect how randomized or predictable outputs become:
- Temperature Scaling: Adjusts the “sharpness” or “flatness” of the probability distribution. Low values (e.g., T=0.5) make the model deterministic; high values (e.g., T=2) foster creativity but risk incoherence.
- Top-k Sampling: Filters probabilities to include only the top k tokens before a word is selected. Ideal if you want smart diversity without sacrificing logical context.
- Top-p Sampling: Also known as nucleus sampling, this method dynamically selects tokens based on cumulative probabilities, ensuring natural diversity while avoiding tail errors.
Each strategy balances creativity and consistency, allowing application-specific customization for tasks like chatbot responses or marketing copy.
How Entrepreneurs Can Achieve Desired Outcomes
Here’s where the real-world application comes in. If you’re building an AI-powered product or leveraging LLMs daily, managing sampling parameters directly impacts customer satisfaction. Questions to ask when designing your LLM-powered systems include:
- Do you need outputs to feel predictable or creative?
- How much diversity can your use case tolerate?
- Is your audience more technical or artistic?
- What’s the trade-off between accuracy and engagement?
By tuning sampling parameters such as temperature (e.g., setting T=1 for balanced responses) or implementing top-p sampling with p=0.9, you’re controlling how the AI responds to prompts. For instance, check out Machine Learning Mastery’s guide for practical temperature visualizations and top-p examples.
Common Mistakes to Avoid
- Over-reliance on Greedy Sampling: Leads to repetitive and dull outputs.
- Setting Temperature Too High: Results in nonsensical responses that can alienate users.
- Ignoring Probabilities: Failing to visualize token likelihoods creates blind spots during implementation.
- Skipping Top-p: Missing out on adaptable randomness that balances coherence.
Key Takeaways for Business Owners
LLMs are versatile tools, but their effectiveness hinges on smart parameter tuning. Entrepreneurs should strive to understand how probabilistic models shape outputs, especially for applications demanding persuasion or consistency. Whether you’re fine-tuning a generative assistant or studying user interaction patterns, your tweaks matter.
Mastering logits, softmax, and sampling ensures you stay ahead of generative AI trends while delivering valuable, controlled user experiences. Ready to optimize your AI workflows? Dive into this comprehensive guide.
FAQ on How LLMs Choose Their Words
How do LLMs generate the next word in a sentence?
LLMs generate the next word by predicting it based on a probability distribution of possible tokens. First, they calculate logits, which are raw, unnormalized scores for every token in their vocabulary. These logits are processed through the softmax function, which transforms them into probabilities that sum up to 1. From there, a specific sampling strategy, such as greedy decoding, temperature scaling, or nucleus (top-p) sampling, determines which token is selected to form the next word or phrase in the sequence. Learn more about how LLMs generate their next token.
What is the function of the softmax algorithm in LLMs?
Softmax takes the raw logits (numerical scores) outputted by the LLM and normalizes them into probabilities. This is crucial because raw logits are not in a meaningful range for decision-making on their own. The softmax formula ensures each word has a probability between 0 and 1, with the total adding up to 100% across all potential tokens. The likelihood of a word is determined proportionally to its logit compared to others. Softmax therefore helps establish the context-specific probabilities required to decide on the most appropriate next token. Explore the mathematical process of softmax in this detailed guide.
What is temperature scaling, and how does it affect text generation?
Temperature scaling adjusts the sharpness or flatness of the probability distribution created by softmax. A lower temperature (e.g., T=0.5) results in sharper distributions, making the output deterministic, favoring the most probable token. A higher temperature (e.g., T=2) flattens the distribution, increasing randomness and allowing less probable tokens to have a higher chance of selection. This makes the text generation more creative but also increases the risk of incoherence. Discover practical examples of temperature scaling's impact on LLM outputs.
What is top-k sampling, and when should it be used?
Top-k sampling limits the model’s token selection pool to the k most probable tokens determined by softmax. For example, setting k=5 means the model will only consider the top 5 highest-probability words for output. This strategy balances creativity and predictability, as it filters out low-likelihood tokens while still adding variation to the generated text. Top-k is recommended when you want diverse output without sacrificing contextual relevance. Learn more about how top-k sampling can improve natural language generation.
How does nucleus sampling, or top-p, improve generative text?
Nucleus sampling dynamically narrows the token pool, selecting only enough tokens to meet a cumulative probability threshold (e.g., 90%). Unlike top-k, p-sampling adapts to the probability distribution, meaning the specific number of tokens considered may change depending on the context. This offers flexibility to the model, generating outputs that are creative and contextually coherent. Explore the functionality and applications of top-p sampling in machine learning.
Which sampling strategies are best for deterministic outputs?
For deterministic outputs, greedy decoding and low-temperature settings work best. Greedy decoding always selects the token with the highest probability, ensuring consistent, logical responses but sometimes resulting in repetitive language. Similarly, temperature values close to zero (T≈0.1) make distributions sharper, prioritizing the top token consistently. These methods are ideal for tasks that require accuracy, such as code generation or structured communication. Check out these advanced decoding techniques for LLMs.
How can businesses use LLM sampling strategies effectively?
Businesses can achieve different goals by tuning LLM sampling parameters. For instance, customer service chatbots may benefit from top-k sampling with moderate k-values to provide varied but accurate responses. Creative content like ad copywriting might require higher temperature settings or nucleus sampling (e.g., p=0.9) for richer, more engaging language. Understanding the trade-offs between safety, coherence, and creativity in text generation is vital for satisfying specific business needs. Learn how to adapt LLMs for unique business applications.
What are the biggest risks when using temperature or sampling?
A common risk of using high temperatures (T>1) is incoherence, where the output may include nonsensical or irrelevant tokens. Similarly, overly narrow nucleus sampling (low p-values like p=0.1) may limit diversity excessively, making responses predictable and bland. Businesses should test different parameter combinations and analyze output quality to strike the right balance. Check these common pitfalls and tips for fine-tuning LLMs.
How do logits influence LLM creativity?
Logits are the foundation of probabilities in text generation, determining how likely each token is to be selected as the next word. By modifying logits using scaling strategies (e.g., temperature), you can encourage the model to either focus on highly probable tokens (low creativity) or allow diverse, unexpected picks (high creativity). Analyzing logits and output distributions is essential for customizing models effectively. Learn how logits play a role in creative text outputs.
How can I better understand and visualize LLM outputs?
Visualizing LLM outputs often involves plotting probability distributions of tokens to understand their likelihoods during different sampling methods. Software frameworks like PyTorch or Hugging Face provide tools to apply softmax, sampling, and logits visualization. For a practical approach, experimentation with sample Python code is highly effective. Explore LLM visualization techniques and examples.
About the Author
Violetta Bonenkamp, also known as MeanCEO, is an experienced startup founder with an impressive educational background including an MBA and four other higher education degrees. She has over 20 years of work experience across multiple countries, including 5 years as a solopreneur and serial entrepreneur. Throughout her startup experience she has applied for multiple startup grants at the EU level, in the Netherlands and Malta, and her startups received quite a few of those. She’s been living, studying and working in many countries around the globe and her extensive multicultural experience has influenced her immensely.
Violetta is a true multiple specialist who has built expertise in Linguistics, Education, Business Management, Blockchain, Entrepreneurship, Intellectual Property, Game Design, AI, SEO, Digital Marketing, cyber security and zero code automations. Her extensive educational journey includes a Master of Arts in Linguistics and Education, an Advanced Master in Linguistics from Belgium (2006-2007), an MBA from Blekinge Institute of Technology in Sweden (2006-2008), and an Erasmus Mundus joint program European Master of Higher Education from universities in Norway, Finland, and Portugal (2009).
She is the founder of Fe/male Switch, a startup game that encourages women to enter STEM fields, and also leads CADChain, and multiple other projects like the Directory of 1,000 Startup Cities with a proprietary MeanCEO Index that ranks cities for female entrepreneurs. Violetta created the “gamepreneurship” methodology, which forms the scientific basis of her startup game. She also builds a lot of SEO tools for startups. Her achievements include being named one of the top 100 women in Europe by EU Startups in 2022 and being nominated for Impact Person of the year at the Dutch Blockchain Week. She is an author with Sifted and a speaker at different Universities. Recently she published a book on Startup Idea Validation the right way: from zero to first customers and beyond, launched a Directory of 1,500+ websites for startups to list themselves in order to gain traction and build backlinks and is building MELA AI to help local restaurants in Malta get more visibility online.
For the past several years Violetta has been living between the Netherlands and Malta, while also regularly traveling to different destinations around the globe, usually due to her entrepreneurial activities. This has led her to start writing about different locations and amenities from the point of view of an entrepreneur. Here’s her recent article about the best hotels in Italy to work from.