BiteSizedChunks.comLearn one small thing at a time.

Course contentsShow

125Zero-Shot Prompting Fundamentals
126Few-Shot Learning with Examples
127Task Decomposition and Step-by-Step Instructions
128Role-Based Prompting
129Context and Background Information
130Explicit Output Format Instructions
131Constraints and Negative Instructions
132Length and Verbosity Control
133Audience Targeting
134Tone and Style Guidance
135Prompt Clarity and Precision
136Iterative Prompt Refinement
137Temperature and Randomness Control
138Top-p (Nucleus) Sampling
139Top-k Sampling
140Max Tokens and Length Control
141Stop Sequences and Early Termination
142Frequency and Presence Penalties
143Seed for Reproducible Generation
144Logit Bias and Token Control
145Combining Parameters for Desired Behavior
146Parameter Trade-offs and Experimentation
147Why Templates Matter: Reusable Prompts at Scale
148Basic String Substitution and f-strings
149Template Engines: Jinja2 for Prompts
150Defining Prompt Variables and Type Safety
151Conditional Sections in Templates
152Loops and Lists in Prompt Templates
153Prompt Partials and Composition
154Escaping and Sanitizing User Input
155Template Versioning and Storage
156Testing Templates with Mock Data
157Structured Output Patterns
158Delimiters and Markers for Parsing
159Schema Specification in Prompts
160Handling Inconsistent Outputs
161Prompt Versioning Strategies
162Version Control and Storage
163Testing Prompt Changes
164Deprecation and Migration Patterns

125Zero-Shot Prompting Fundamentals
126Few-Shot Learning with Examples
127Task Decomposition and Step-by-Step Instructions
128Role-Based Prompting
129Context and Background Information
130Explicit Output Format Instructions
131Constraints and Negative Instructions
132Length and Verbosity Control
133Audience Targeting
134Tone and Style Guidance
135Prompt Clarity and Precision
136Iterative Prompt Refinement
137Temperature and Randomness Control
138Top-p (Nucleus) Sampling
139Top-k Sampling
140Max Tokens and Length Control
141Stop Sequences and Early Termination
142Frequency and Presence Penalties
143Seed for Reproducible Generation
144Logit Bias and Token Control
145Combining Parameters for Desired Behavior
146Parameter Trade-offs and Experimentation
147Why Templates Matter: Reusable Prompts at Scale
148Basic String Substitution and f-strings
149Template Engines: Jinja2 for Prompts
150Defining Prompt Variables and Type Safety
151Conditional Sections in Templates
152Loops and Lists in Prompt Templates
153Prompt Partials and Composition
154Escaping and Sanitizing User Input
155Template Versioning and Storage
156Testing Templates with Mock Data
157Structured Output Patterns
158Delimiters and Markers for Parsing
159Schema Specification in Prompts
160Handling Inconsistent Outputs
161Prompt Versioning Strategies
162Version Control and Storage
163Testing Prompt Changes
164Deprecation and Migration Patterns

← AI Engineering

Lesson 138 of 1,886·4. Prompt Engineering FoundationsPro lesson

Top-p (Nucleus) Sampling

Controlling diversity by sampling from the smallest set of tokens whose cumulative probability exceeds p.

This lesson is for subscribers

You've completed the free preview. Subscribe to unlock every lesson in every course.

See pricing Back to course