Prompt design and engineering has become essential for maximizing the potential of large language models (LLMs). This paper introduces core concepts, advanced techniques like Chain-of-Thought and Reflection, and the principles behind building LLM-based agents. It also provides a survey of tools for prompt engineers. A prompt is the textual input provided by users to guide the model's output, ranging from simple questions to complex problem statements. Prompts can consist of instructions, questions, input data, and examples. Basic prompts include simple questions or instructions, while advanced prompts involve more complex structures like "chain of thought" prompting, which guides the model through logical reasoning steps. - **Instructions + Question**: Combining instructions with a question, such as asking for advice on writing a college essay. - **Instructions + Input**: Providing input data and instructions, like writing a college essay based on personal information. - **Question + Examples**: Using examples to guide the model, such as recommending TV shows based on preferences. Prompt engineering is a rapidly emerging discipline that shapes the interactions and outputs of generative AI models. It involves crafting optimal prompts to achieve specific goals, requiring a deep understanding of the model's capabilities and limitations. Techniques include creating templates, using special tokens, and explicitly ending prompts with `<endofprompt>`. - **Chain of Thought (CoT)**: Encourages the model to follow a series of logical steps, enhancing reasoning capabilities. - **Tree of Thought (ToT)**: Facilitates multi-faceted exploration of problem-solving pathways, mirroring human cognitive processes. - **Automatic Multi-step Reasoning and Tool-use (ART)**: Combines automated chain of thought prompting with external tools. - **Self-Consistency**: Enhances reliability by prompting the LLM to produce multiple answers and evaluate their consistency. - **Reflection**: Allows the LLM to self-evaluate and revise its outputs. - **Expert Prompting**: Empowers the LLM to simulate expert-level responses across diverse domains. - **Chains**: Breaks down complex tasks into manageable segments, enabling end-to-end processing. - **Rails**: Directs LLM outputs within predefined boundaries to ensure relevance and safety. - **Automatic Prompt Engineering (APE)**: Automates the prompt creation process, optimizing its design for desired responses. - **Retrieval Augmented Generation (RAG)**: Integrates external knowledge to enrich LLM responses with up-to-date or specialized information. - **LLM Agents**: Autonomous entities that perceive, decide, and act, incorporating decision-making and tool utilization capabilities. The paper explores foundational aspects and advanced applications of prompt engineering, focusing on its use in LLMs. It highlights the importance of understanding the model's capabilities and limitations, and provides a comprehensive guide to crafting effective prompts and leveraging advanced techniques.Prompt design and engineering has become essential for maximizing the potential of large language models (LLMs). This paper introduces core concepts, advanced techniques like Chain-of-Thought and Reflection, and the principles behind building LLM-based agents. It also provides a survey of tools for prompt engineers. A prompt is the textual input provided by users to guide the model's output, ranging from simple questions to complex problem statements. Prompts can consist of instructions, questions, input data, and examples. Basic prompts include simple questions or instructions, while advanced prompts involve more complex structures like "chain of thought" prompting, which guides the model through logical reasoning steps. - **Instructions + Question**: Combining instructions with a question, such as asking for advice on writing a college essay. - **Instructions + Input**: Providing input data and instructions, like writing a college essay based on personal information. - **Question + Examples**: Using examples to guide the model, such as recommending TV shows based on preferences. Prompt engineering is a rapidly emerging discipline that shapes the interactions and outputs of generative AI models. It involves crafting optimal prompts to achieve specific goals, requiring a deep understanding of the model's capabilities and limitations. Techniques include creating templates, using special tokens, and explicitly ending prompts with `<endofprompt>`. - **Chain of Thought (CoT)**: Encourages the model to follow a series of logical steps, enhancing reasoning capabilities. - **Tree of Thought (ToT)**: Facilitates multi-faceted exploration of problem-solving pathways, mirroring human cognitive processes. - **Automatic Multi-step Reasoning and Tool-use (ART)**: Combines automated chain of thought prompting with external tools. - **Self-Consistency**: Enhances reliability by prompting the LLM to produce multiple answers and evaluate their consistency. - **Reflection**: Allows the LLM to self-evaluate and revise its outputs. - **Expert Prompting**: Empowers the LLM to simulate expert-level responses across diverse domains. - **Chains**: Breaks down complex tasks into manageable segments, enabling end-to-end processing. - **Rails**: Directs LLM outputs within predefined boundaries to ensure relevance and safety. - **Automatic Prompt Engineering (APE)**: Automates the prompt creation process, optimizing its design for desired responses. - **Retrieval Augmented Generation (RAG)**: Integrates external knowledge to enrich LLM responses with up-to-date or specialized information. - **LLM Agents**: Autonomous entities that perceive, decide, and act, incorporating decision-making and tool utilization capabilities. The paper explores foundational aspects and advanced applications of prompt engineering, focusing on its use in LLMs. It highlights the importance of understanding the model's capabilities and limitations, and provides a comprehensive guide to crafting effective prompts and leveraging advanced techniques.