Prompt Engineering Questions

Designing prompts and instructions that steer large language models toward reliable, controllable outputs without changing model weights. Covers zero- and few-shot prompting and example selection, chain-of-thought and step-by-step reasoning prompts, system, developer and role instructions and instruction hierarchy, output formatting and structured output (JSON schemas, templates, constraints), task decomposition and prompt chaining, managing context and token budgets inside a prompt, prompt injection as a design constraint, and iterating on, versioning and regression-testing prompts against a test set. Also covers prompt-level ways to reduce hallucination (grounding in supplied text, quoting support, allowing abstention) and unsafe, toxic or off-policy output (scoped roles, refusal templates), and prompting a model to generate constrained, validated output such as SQL. Boundary: excludes how models are pretrained and behave internally, sampling and decoding internals, fine-tuning, retrieval pipelines, and general LLM evaluation and monitoring.