prompt-engineering

The CO-STAR framework is a structured prompt design methodology for LLMs that systematically defines context, objective, style, tone, audience, and response format to generate optimal model outputs.

Few-shot learning provides example inputs and outputs to guide LLM behavior, while chain-of-thought prompting structures those examples to include intermediate reasoning steps for more accurate complex task execution.

Prompt optimization requires model-specific tuning across GPT-4, Claude, and Gemini because each LLM interprets structural constraints and few-shot examples differently, requiring tailored strategies for optimal results.

Preventing prompt injection involves designing structured LLM prompts with clear boundaries and utilizing specific evaluation methodologies to test model responses against malicious inputs and unauthorized instruction overrides.

Use Tree-of-Thought prompting for LLM tasks requiring exploration of multiple reasoning branches, whereas ReAct is better suited for tasks requiring iterative interleaving of reasoning and external action execution.

Evaluating LLM prompt effectiveness involves applying systematic evaluation methodologies to assess how well structured frameworks like CO-STAR and chain-of-thought techniques consistently produce the desired target outputs.