Prompt design and engineering have become essential for maximizing the potential of large language models (LLMs). This paper introduces core concepts, advanced techniques like Chain-of-Thought (CoT) and Reflection, and the principles behind building LLM-based agents. It also provides a survey of tools for prompt engineers. Prompt engineering in generative AI models is a rapidly emerging discipline that shapes the interactions and outputs of these models. A prompt is the textual interface through which users communicate their desires to the model, ranging from simple questions to intricate tasks. The essence of prompt engineering lies in crafting the optimal prompt to achieve a specific goal with a generative model. This process involves a deep understanding of the model's capabilities and limitations, and the context within which it operates. LLMs have several limitations, including transient state, probabilistic nature, outdated information, content fabrication, resource intensity, and domain specificity. These limitations underscore the need for advanced prompt engineering and specialized techniques to enhance LLM utility and mitigate inherent constraints. Advanced prompt design techniques include Chain of Thought prompting, encouraging factual responses through reasoning steps, explicitly ending prompt instructions, being forceful, using the AI to correct itself, generating different opinions, keeping state and role-playing, teaching algorithms in the prompt, and using affordances. These techniques help in creating more effective prompts that guide LLMs to produce accurate and relevant responses. Advanced techniques in prompt engineering include Chain of Thought (CoT), Tree of Thought (ToT), and Automatic Multi-step Reasoning and Tool-use (ART). These techniques enhance the ability of LLMs to handle complex tasks that require both reasoning and interaction with external data sources or tools. Additionally, methods like Self-Consistency and Reflection improve the reliability and accuracy of LLM outputs by ensuring consistency and self-evaluation. Prompt engineering tools and frameworks, such as Langchain, Semantic Kernel, Guidance, Nemo Guardrails, LlamaIndex, FastRAG, and Auto-GPT, provide resources for developing complex LLM applications. These tools help in streamlining the implementation and enhancing the capabilities of prompt engineering methodologies, enabling researchers and practitioners to leverage prompt engineering more effectively.Prompt design and engineering have become essential for maximizing the potential of large language models (LLMs). This paper introduces core concepts, advanced techniques like Chain-of-Thought (CoT) and Reflection, and the principles behind building LLM-based agents. It also provides a survey of tools for prompt engineers. Prompt engineering in generative AI models is a rapidly emerging discipline that shapes the interactions and outputs of these models. A prompt is the textual interface through which users communicate their desires to the model, ranging from simple questions to intricate tasks. The essence of prompt engineering lies in crafting the optimal prompt to achieve a specific goal with a generative model. This process involves a deep understanding of the model's capabilities and limitations, and the context within which it operates. LLMs have several limitations, including transient state, probabilistic nature, outdated information, content fabrication, resource intensity, and domain specificity. These limitations underscore the need for advanced prompt engineering and specialized techniques to enhance LLM utility and mitigate inherent constraints. Advanced prompt design techniques include Chain of Thought prompting, encouraging factual responses through reasoning steps, explicitly ending prompt instructions, being forceful, using the AI to correct itself, generating different opinions, keeping state and role-playing, teaching algorithms in the prompt, and using affordances. These techniques help in creating more effective prompts that guide LLMs to produce accurate and relevant responses. Advanced techniques in prompt engineering include Chain of Thought (CoT), Tree of Thought (ToT), and Automatic Multi-step Reasoning and Tool-use (ART). These techniques enhance the ability of LLMs to handle complex tasks that require both reasoning and interaction with external data sources or tools. Additionally, methods like Self-Consistency and Reflection improve the reliability and accuracy of LLM outputs by ensuring consistency and self-evaluation. Prompt engineering tools and frameworks, such as Langchain, Semantic Kernel, Guidance, Nemo Guardrails, LlamaIndex, FastRAG, and Auto-GPT, provide resources for developing complex LLM applications. These tools help in streamlining the implementation and enhancing the capabilities of prompt engineering methodologies, enabling researchers and practitioners to leverage prompt engineering more effectively.