A Reddit thread in r/PromptEngineering asks the community to share their best prompt engineering tips and strategies. The responses cover a wide range of practical techniques, from self-critique prompting (asking the LLM to critique its own response and then apply fixes) to structured prompt frameworks like Role-Task-Context-Output and RAPTOR (Role, Aim, Parameters, Tone, Output, Review). Several users emphasize the importance of consulting first-party documentation from frontier labs like OpenAI and Anthropic rather than relying on third-party guides, while one contrarian suggests simply asking the LLM itself how to best prompt it.

Other notable tips include using a "refresher" symbol embedded in prompts to re-orient the model when it drifts off-task during long conversations, employing a two-pass generation flow (generate options first, then pick and expand), and grounding outputs by requiring the model to cite specific quoted lines. Multiple commenters stress that prompt engineering is only one piece of the puzzle — tooling, evals, domain expertise, and context engineering play equally critical roles in achieving high-quality AI outputs. The thread serves as a useful snapshot of community-tested practices for anyone looking to move beyond basic prompting.