Large language models (LLMs) rely on the input provided, known as prompts, to generate responses, which can significantly impact their output. Assigning a specific role or character to the model can help align its output with user intent. Using specific and concise prompts can better align LLM responses to the task by specifying context, length, and format of the output. Minimizing hallucinations by allowing the model to admit it doesn't know an answer when necessary is also crucial. Prompting strategies such as two-stage summarization and few-shot prompting can help improve efficiency and accuracy. Eliciting reasoning with Chain-of-Thought prompts can significantly improve complex reasoning abilities in LLMs, while leveraging no-code platforms like VectorShift can facilitate practical applications of prompt engineering.