Overview

In this lesson, learners will understand how large language models (LLMs) generate text, the concept of token prediction, and basic strategies to control outputs like temperature, top-K, and top-P sampling. This is foundational knowledge to prepare for effective prompt engineering.

Concept Explanation

1. LLMs as Prediction Engines

• LLMs don’t “know” or “think” like humans. They are probabilistic token predictors.
• Each token (word or piece of a word) is predicted based on previous tokens and learned patterns from training data.
• The model iteratively predicts one token at a time to build sentences, paragraphs, or documents.

Key Idea: Your prompt sets the context and constraints for the model’s predictions.

2. Output Configuration Settings

LLM outputs can be influenced with a few core parameters:

a) Temperature

• Controls randomness:
  • Low temperature (e.g., 0–0.3): More deterministic, safer outputs.
  • High temperature (e.g., 0.7–1): More creative or varied outputs.
• Analogous to “risk vs. creativity” in human decisions.

b) Top-K Sampling

• Limits the next token to K most probable tokens.
• Lower K → more deterministic (conservative).
• Higher K → more creative (exploratory).

c) Top-P / Nucleus Sampling

• Chooses tokens from the smallest set whose cumulative probability ≥ P.
• Dynamically adjusts the candidate pool to balance creativity and reliability.

3. Output Length Control

• LLMs generate tokens sequentially until reaching a max token limit.
• Short limits can truncate reasoning or summaries.
• Long limits may produce verbose outputs or require more computation and cost.

4. Putting It All Together

• Temperature, top-K, top-P, and max tokens work together.
• Example:
  • Temperature = 0 → deterministic output; top-K/top-P ignored.
  • Temperature high → top-K/top-P influence which tokens are sampled.
• Effective prompt engineering requires understanding these interactions.

Practical Examples

1. Deterministic Summarization

Prompt: "Summarize the following text in 2 sentences."
Temperature: 0
Top-K: 1
Top-P: 0.9

2. Creative Story Generation

Prompt: "Write a short fantasy story about a dragon and a wizard."
Temperature: 0.8
Top-K: 50
Top-P: 0.95
Max tokens: 300

3. Few-shot Classification

Prompt: "Classify the following movie review as Positive or Negative."
Examples:
- 'I loved the movie!' -> Positive
- 'The plot was boring.' -> Negative
Temperature: 0
Top-K: 5
Top-P: 0.9

Hands-on Exercise

Task: Experiment with LLM output settings.

Steps:

1. Pick a short prompt (e.g., “Explain blockchain in simple terms”).
2. Generate three outputs:
   • Deterministic: low temperature, low top-K.
   • Balanced: moderate temperature, moderate top-P.
   • Creative: high temperature, high top-K/top-P.
3. Compare results for clarity, creativity, and correctness.
4. Document observations on how settings affect output quality.

Tools & Techniques

• APIs: OpenAI GPT, Vertex AI, Claude.
• Temperature/top-K/top-P controls: Adjust for task-specific outputs.
• Max tokens: Balance length vs. cost.
• Few-shot examples: Combine with sampling controls for structured outputs.

Audience Relevance

• Students: Understand LLM mechanics for research or experimentation.
• Developers: Optimize prompts for reliability vs. creativity in apps.
• Business Users: Adjust AI outputs for marketing, summarization, or automation tasks.

Summary & Key Takeaways

• LLMs predict tokens one at a time; prompts set context.
• Temperature, top-K, top-P, and token limits control output randomness, creativity, and length.
• Understanding these fundamentals is essential before diving into advanced prompt engineering.
• Experimentation is key—there’s no one-size-fits-all configuration.