Controlling Length and Structure in LLM Outputs: Practical Decoding Parameters

Ever typed a question into an AI chatbot and got back a wall of text when you just wanted a quick answer? Or worse - got the same sentence repeated five times? That’s not a bug. That’s a decoding parameter problem.

Large Language Models don’t think like humans. They predict the next word based on probability. Left unchecked, they’ll keep going until they hit a hard limit - sometimes rambling, sometimes looping, sometimes cutting off mid-sentence. The trick isn’t just asking better questions. It’s telling the model how to answer. That’s where decoding parameters come in.

Max Tokens: The Simplest Lever

The most basic control you have is max tokens. This sets the upper limit on how long the response can be. Tokens aren’t words. They’re chunks - sometimes a whole word like "apple," sometimes just "un" from "unhappy," or even a single punctuation mark.

Set it too low? You get cut-off answers. "The capital of France is Par" - that’s not helpful. Set it too high? You get essays when you asked for a bullet point.

For product descriptions, aim for 150-200 tokens. For customer service replies, 50-100 is usually enough. For summarizing a 10-page report? Go up to 500. But don’t just crank up the number and hope for the best. Pair max tokens with clear instructions: "Summarize in under 100 tokens" works better than just setting a limit.

Temperature: Controlling Creativity

Temperature is the dial for randomness. Think of it like a thermostat for imagination.

At 0.1, the model picks the single most likely next token every time. It’s predictable. Reliable. Perfect for legal summaries, medical advice, or code generation. You want accuracy, not surprises.

At 0.8, it starts exploring. It might pick the 3rd or 4th most likely option. This is where you get natural-sounding, slightly varied responses - great for marketing copy or casual chat.

At 1.2 or higher? You’re flirting with nonsense. The model might invent facts, mix metaphors, or write poetry that makes no sense. Useful for brainstorming, but dangerous if you need facts.

Start here: Use 0.2 for factual tasks. Use 0.7 for creative ones. Never go above 1.0 unless you’re writing fiction and don’t mind the chaos.

Top-K and Top-P: Smart Filtering

Temperature alone isn’t enough. That’s where Top-K and Top-P come in.

Top-K limits the model to choosing from only the K most probable next tokens. If K=1, it’s pure greedy decoding - always picks the #1 choice. If K=50, it has more freedom. IAPEP recommends starting with K=30 for balanced results.

Top-P (nucleus sampling) is smarter. Instead of picking the top K tokens, it picks the smallest set of tokens whose probabilities add up to P. So if P=0.95, it picks the smallest group of tokens that cover 95% of the probability mass. This adapts dynamically - sometimes it’s 10 tokens, sometimes 50.

For factual output: Top-P=0.9, Top-K=20. For creative: Top-P=0.99, Top-K=40. These settings let the model stay grounded but not robotic.

Using both together? That’s where the magic happens. High temperature + high Top-P = wild creativity. Low temperature + low Top-K = robotic precision.

Repetition: Why Your AI Keeps Saying the Same Thing

Ever seen an AI write: "The product is great. The product is great. The product is great."? That’s not creativity. That’s a loop.

Beam search, a common decoding method, often causes this. It favors sequences that feel "likely" - even if they repeat. Temperature alone won’t fix it. You need penalties.

Repeat_penalty reduces the chance of any token appearing again. Repeat_last_n checks the last 50-200 tokens and punishes repeats within that window. Presence_penalty encourages new words. Frequency_penalty dings words that show up too often.

For product descriptions, set repeat_penalty to 1.1-1.3 and repeat_last_n to 100. This stops the model from recycling phrases like "high-quality," "premium," or "cutting-edge" over and over.

Stop Sequences: When to Hit the Brake

What if you want the model to stop when it hits a certain phrase? Like "Thank you for your time" or "End of summary"?

Stop_sequences lets you define those triggers. You can set it to halt generation when it generates "\n\n" (two line breaks), "", or even "[END]".

This is critical for structured outputs - like JSON, HTML, or formatted reports. Instead of guessing where the response ends, you tell it exactly when to stop. It cuts waste, reduces errors, and makes parsing outputs easier.

Advanced Tools: Grammar, Bias, and Mirostat

Need the output to follow a strict format? Like a JSON object with keys: "title," "description," "price"? That’s where grammar constraints come in. Some systems let you define a grammar - like a mini programming language - that the model must follow. It’s not available everywhere, but when it is, it’s powerful.

Logit_bias lets you boost or suppress specific words. Want to avoid "free" in a paid service description? Lower its logit score. Want to ensure "sustainable" appears? Boost it.

Then there’s Mirostat - an advanced method that dynamically adjusts temperature based on coherence. It’s like a self-tuning system. Mirostat_tau=5 keeps things focused. Mirostat_tau=10 lets it wander. Mirostat_eta controls how fast it adapts. Most users won’t need this - but if you’re building a long-form narrative generator, it’s worth testing.

Real-World Settings: What to Use When

Here’s a quick cheat sheet:

• Customer Support Replies: max_tokens=100, temperature=0.2, top_p=0.9, top_k=20, repeat_penalty=1.2
• Product Descriptions: max_tokens=200, temperature=0.4, top_p=0.95, top_k=30, repeat_last_n=100
• Code Generation: max_tokens=500, temperature=0.1, top_p=0.9, top_k=10, stop_sequences=["\n\n\n"]
• Storytelling / Poetry: max_tokens=400, temperature=0.9, top_p=0.99, top_k=40, presence_penalty=0.5
• Technical Documentation: max_tokens=300, temperature=0.1, top_p=0.9, top_k=15, grammar=JSON schema

These aren’t magic numbers. They’re starting points. Test them. Change one variable at a time. Watch how the output shifts. Keep notes.

Why This All Matters

You can’t just throw a prompt at an LLM and expect perfect results. The model doesn’t know your intent. It doesn’t know if you need a tweet, a report, or a legal clause. You have to tell it - not just with words, but with settings.

Decoding parameters are the levers that turn raw prediction into useful output. They’re the difference between a useful answer and a frustrating mess. Master them, and you stop fighting the AI. You start guiding it.