prompting-patterns

Prompt injection prevention involves sanitizing user input and wrapping it with clear boundaries to isolate untrusted data from system instructions. This Skill provides patterns for input handling and safe formatting via Python classes to mitigate injection risks across system prompts and user queries.

Structured prompt templates using Pydantic classes define system messages, variables, and few-shot examples to guide LLM behavior consistently. This approach ensures reliable outputs and enables version control for tracking prompt performance metrics over time.

Context window management involves truncating message history and prioritizing recent or critical information to stay within token limits. This Skill covers strategies for managing conversation length while preserving context quality for long-running LLM applications.

Chain-of-thought prompting guides LLMs through step-by-step reasoning for complex tasks by structuring prompts to show intermediate thinking. Combined with few-shot examples, it produces more detailed and accurate outputs than direct instruction alone.

Yes. Pydantic classes enable structured prompt template design with validated fields, system messages, and variable injection. This provides type safety, composability, and consistent message assembly for LLM API integration.

Few-shot examples demonstrate the desired input-output pattern and reasoning style directly within prompts. They improve LLM accuracy for complex tasks without retraining and work alongside chain-of-thought for comprehensive instruction and demonstration.