1. Keep It DRY : Use templates to avoid repetition 2. Validate Early : Check variables before rendering 3. Version Templates : Track changes like code 4. Test Variations : Ensure templates work with diverse inputs 5. Document Variables : Clearly specify required/op. Use it to give an agent explicit responsibilities, steps and constraints.
1. Establish Baseline : Always measure initial performance 2. Change One Thing : Isolate variables for clear attribution 3. Test Thoroughly : Use diverse, representative test cases 4. Track Metrics : Log all experiments and results 5. Validate Significance : Use st. Use it to give an agent explicit responsibilities, steps and constraints.
You are an expert prompt engineer specializing in crafting effective prompts for LLMs and optimizing AI system performance through advanced prompting techniques. Use it to give an agent explicit responsibilities, steps and constraints.
Stable patterns for working with Stitch. This file covers concepts and taxonomies that remain consistent across SDK versions. For the current API surface (parameter names, return shapes, enum values), the authoritative source is the SDK repository linked in the Stitch Documentati. Use it to give an agent explicit responsibilities, steps and constraints.
This file covers how to design prompt-building code for effective caching. For language-specific syntax, see the Prompt Caching section in each language's README or single-file doc. Use it as a repeatable review, validation or hardening pass.
A practical guide to Claude Code — from your first prompt to multi-agent automation, hooks, MCP, and team workflows. Built around clear mental models and real examples, not marketing. Use it as a repeatable review, validation or hardening pass.
This repository centralizes and summarizes practical and proposed defenses against prompt injection. Use it to navigate the topic and choose relevant methods, papers or tools.
Bringing clarity to questions about Prompt Injection Security. Use it when a task needs concrete terminology, constraints or implementation detail.
This repository serves as a comprehensive resource for learning, building, and sharing prompt engineering techniques, ranging from basic concepts to a. Use it as a repeatable review, validation or hardening pass.
Prompt Decorators extend the functionality of large language models by allowing structured, modular control over reasoning, style, and behavior. Each decorator enforces specific response rules, enabling users to declaratively modify how the model thinks and writes — without retraining.
1. Keep It DRY : Use templates to avoid repetition 2. Validate Early : Check variables before rendering 3. Version Templates : Track changes like code 4. Test Variations : Ensure templates work with diverse inputs 5. Document Variables : Clearly specify required/op. Use it to give an agent explicit responsibilities, steps and constraints.
1. Establish Baseline : Always measure initial performance 2. Change One Thing : Isolate variables for clear attribution 3. Test Thoroughly : Use diverse, representative test cases 4. Track Metrics : Log all experiments and results 5. Validate Significance : Use st. Use it to give an agent explicit responsibilities, steps and constraints.
You are an expert prompt engineer specializing in crafting effective prompts for LLMs and optimizing AI system performance through advanced prompting techniques. Use it to give an agent explicit responsibilities, steps and constraints.
Stable patterns for working with Stitch. This file covers concepts and taxonomies that remain consistent across SDK versions. For the current API surface (parameter names, return shapes, enum values), the authoritative source is the SDK repository linked in the Stitch Documentati. Use it to give an agent explicit responsibilities, steps and constraints.
This file covers how to design prompt-building code for effective caching. For language-specific syntax, see the Prompt Caching section in each language's README or single-file doc. Use it as a repeatable review, validation or hardening pass.
A practical guide to Claude Code — from your first prompt to multi-agent automation, hooks, MCP, and team workflows. Built around clear mental models and real examples, not marketing. Use it as a repeatable review, validation or hardening pass.
This repository centralizes and summarizes practical and proposed defenses against prompt injection. Use it to navigate the topic and choose relevant methods, papers or tools.
Bringing clarity to questions about Prompt Injection Security. Use it when a task needs concrete terminology, constraints or implementation detail.
This repository serves as a comprehensive resource for learning, building, and sharing prompt engineering techniques, ranging from basic concepts to a. Use it as a repeatable review, validation or hardening pass.
Prompt Decorators extend the functionality of large language models by allowing structured, modular control over reasoning, style, and behavior. Each decorator enforces specific response rules, enabling users to declaratively modify how the model thinks and writes — without retraining.