Whether you’re building creative tools or deploying factual assistants, understanding how language models generate text is key.

Here’s a quick guide I created to demystify LLM sampling strategies — from temperature tuning to top-k/top-p decoding and token-specific tactics like repetition penalties and logit biases.

🔧 Tips to get started:
1️⃣ Adjust temperature and top-p based on your domain (creative vs. factual).
2️⃣ Use greedy decoding for debugging or deterministic needs.
3️⃣ For richer outputs, consider higher temperature and top-p.
4️⃣ Fine-tune with repetition penalties or logit biases as needed.

📌 Greedy decoding?
✅ Deterministic
✅ Can get stuck
✅ Useful for debugging

📌 Temperature?
0 = deterministic
1 = balanced

1 = creative
5 = chaos 😄

📌 Top-k vs. Top-p?
Top-k = fixed number of top tokens
Top-p = dynamic cutoff based on cumulative probability

This visual is part of my ongoing learning at rachellearns.com. Hope it helps clarify your next LLM experiment!

🔁 Save, share, and let me know which decoding strategies you’ve found most useful in your work!

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