Exploring 5 Key LLM Parameters with Practical Examples

Large language models have become the backbone of modern AI applications, powering chatbots, content generators, and more. Yet their raw power can feel opaque: a single prompt can trigger wildly different outputs depending on subtle internal settings. That’s why developers rely on a handful of exposed parameters to steer the model’s behavior. In this post, we break down five of the most commonly tweaked options—max_completion_tokens, temperature, top_p, presence_penalty, and frequency_penalty—illustrating each with concrete examples from MarkTechPost’s tutorial. Understanding these knobs not only fixes unexpected results but also unlocks creative control over tone, length, and diversity.

max_completion_tokens caps the length of the model’s reply. For instance, setting max_completion_tokens to 50 may truncate a verbose answer, ensuring concise responses for mobile interfaces. Yet that is not enough — temperature controls randomness; a low value like 0.2 produces deterministic, safe text, while a high value such as 0.9 yields more varied, imaginative output—useful for brainstorming. top_p, or nucleus sampling, limits token selection to the smallest set whose cumulative probability exceeds a threshold (e.g., top_p=0.9). This balances creativity and coherence, preventing unlikely word choices while still allowing diverse phrasing.

presence_penalty discourages the model's from repeating concepts it has already mentioned, encouraging fresh content. Setting it to 0.5 will slightly penalize repeated entities, useful when generating lists or recipes. frequency_penalty, on the other hand, reduces the likelihood of repetitive word usage regardless of context; a value of 1.0 can make a paragraph feel more varied. By combining these parameters thoughtfully, developers can sculpt LLM responses to match specific brand voices, regulatory constraints, or user expectations. In practice, many developers start with temperature 0.7 and top_p 0.95 as a balanced default, then tweak presence_penalty to avoid repetitive jargon. The key takeaway is that fine‑tuning these settings is as much an art as it is a science, and small adjustments can dramatically shift the model’s output.