Prompt Engineering for ChatGPT Course by Vanderbilt University
DERA (see figure 27) introduces a collaborative agent framework where multiple agents, each with specific roles, engage in dialogue to resolve queries and make decisions. This multi-agent approach enables handling complex queries with depth and nuance, closely mirroring human decision-making processes. In addressing the constraints of pre-trained Large Language Models (LLMs), particularly their limitations in accessing real-time or domain-specific information, Retrieval Augmented Generation (RAG) emerges as a pivotal innovation. RAG extends LLMs by dynamically incorporating external knowledge, thereby enriching the model’s responses with up-to-date or specialized information not contained within its initial training data. The accuracy of self-evaluation is contingent upon the LLM’s inherent understanding and its training on reflective tasks.
Automatic Multi-step Reasoning and Tool-use (ART)[10] is a prompt engineering technique that combines automated chain of thought prompting with the use of external tools. ART represents a convergence of multiple prompt engineering strategies, enhancing the ability of Large Language Models (LLMs) to handle complex tasks that require both reasoning and interaction with external data sources or tools. Prompt design and engineering has rapidly become essential for maximizing the potential of large language models. In this paper, we introduce core concepts, advanced techniques like Chain-of-Thought and Reflection, and the principles behind building LLM-based agents.
How Prompt Keywords (Magic Words) Optimize Language Model Performance
Goodside included screenshots of him asking a chatbot, „What NFL team won the Super Bowl in the year Justin Bieber was born?“ The chatbot first said the Green Bay Packers. (Bieber was born in 1994, the year the Dallas Cowboys won the Super Bowl.) Goodside then prompted the chatbot to „enumerate a chain of step-by-step logical deductions“ to answer the question. When Goodside asked the question for the third time, the chatbot spit out the correct answer. Discover a comprehensive framework for mastering prompt engineering, including detailed and simplified lifecycles for effective AI prompt management. AI agents are transforming work across industries through advanced language models and automation. This article explores their capabilities, implications, and the future of AI-powered software experiences.
Prompt engineering gives developers more control over users‘ interactions with the AI. Effective prompts provide intent and establish context to the large language models. They help the AI refine the output and present it concisely in the required format. Generative artificial intelligence (AI) systems are designed to generate specific outputs based on the quality of provided prompts. Prompt engineering helps generative AI models better comprehend and respond to a wide range of queries, from the simple to the highly technical. Prompt engineering is a relatively new discipline for developing and optimizing prompts to efficiently apply and build with large language models (LLMs) for a wide variety of applications and use cases.
Prompt iteration strategies
This can be a great tool when brainstorming and understanding different possible points of views on a topic. We will see how this can be used in our favor in different ways by applying more advanced Prompt Engineering techniques in the next section. In the following example, we feed an article found online and ask ChatGPT to disagree with it. One of the most important problems with generative models is that they are likely to hallucinate knowledge that is not factual or is wrong. You can improve factuality by having the model follow a set of reasoning steps as we saw in the previous subsection. And, you can also point the model in the right direction by prompting it to cite the right sources.
- In the following example, we feed an article found online and ask ChatGPT to disagree with it.
- Prompt engineering helps generative AI models better comprehend and respond to a wide range of queries, from the simple to the highly technical.
- Effective prompts help AI models process patient data and provide accurate insights and recommendations.
- For example, writing prompts for Open AI’s GPT-3 or GPT-4 differs from writing prompts for Google Bard.
- For example, if the question is a complex math problem, the model might perform several rollouts, each involving multiple steps of calculations.
This process is not only about instructing the model but also involves a deep understanding of the model’s capabilities and limitations, and the context within which it operates. In image generation models, for instance, a prompt might be a detailed description of the desired image, while in LLMs, it could be a complex query embedding various types of data. Because generative AI systems are trained in various programming languages, prompt engineers can streamline the generation of code snippets and simplify complex tasks.
Injecting Domain Expertise in LLMs – A Guide to Fine-tuning & Prompting
For example, to find opportunities for process optimization, the prompt engineer can create different prompts that train the AI model to find inefficiencies using broad signals rather than context-specific data. In healthcare, prompt engineers instruct AI systems to summarize medical data and develop treatment recommendations. Effective prompts help AI models process patient data and provide accurate insights and recommendations.
Expert users, who understand how to write good prompts, are orders of magnitude more productive and can unlock significantly more creative uses for these tools. This course introduces students to the patterns and approaches for writing effective prompts for large language models. Anyone can take the course and the only required knowledge is basic computer usage skills, such as using a browser and accessing prompt engineer training ChatGPT. Students will start with basic prompts and build towards writing sophisticated prompts to solve problems in any domain. Large technology organizations are hiring prompt engineers to develop new creative content, answer complex questions and improve machine translation and NLP tasks. Creativity and a realistic assessment of the benefits and risks of new technologies are also valuable in this role.
Prompt Engineering for Generative AI
A lot of fun, and it takes you from the basics of prompt engineering to complex but extremely useful patterns and formulae. Listings on the freelance-work platform Upwork seek contracted prompt engineers, who could get paid up to $40 an hour to generate website content like blog posts and FAQs. Some academics question how effective prompt engineers really are in testing AI.
Prompt engineering is a powerful tool to help AI chatbots generate contextually relevant and coherent responses in real-time conversations. Chatbot developers can ensure the AI understands user queries and provides meaningful answers by crafting effective prompts. Generative AI relies on the iterative refinement of different prompt engineering techniques to effectively learn from diverse input data and adapt to minimize biases, confusion and produce more accurate responses. Discover how carefully chosen prompt keywords enhance the effectiveness of language models. Learn how to craft precise prompts to improve the reliability and usefulness of AI responses. By automating the prompt engineering process, APE not only alleviates the burden of manual prompt creation but also introduces a level of precision and adaptability previously unattainable.
Balance simplicity and complexity in your prompt to avoid vague, unrelated, or unexpected answers. A prompt that is too simple may lack context, while a prompt that is too complex may confuse the AI. This is especially important for complex topics or domain-specific language, which may be less familiar to the AI.
This multiplicity allows the LLM to traverse through diverse hypotheses, mirroring the human approach to problem-solving by weighing various scenarios before reaching a consensus on the most likely outcome. Affordances are functions that are defined in the prompt and the model is explicitly instructed to use when responding. E.g. you can tell the model that whenever finding a mathematical expression it should call an explicit CALC() function and compute the numerical result before proceeding. It is worth keeping in mind that LLMs like GPT only read forward and are in fact completing text. Furthermore, even the order the examples are given makes a difference (see Lu et. al[4]). In the example in 12, we make ChatGPT discuss worst-case time complexity of the bubble sort algorithm as if it were a rude Brooklyn taxi driver.
You can also phrase the
instruction as a question, or give the model a „role,“ as seen in the second
example below. Provide adequate context within the prompt and include output requirements in your prompt input, confining it to a specific format. For instance, say you want a list of the most popular movies of the 1990s in a table. To get the exact result, you should explicitly state how many movies you want to be listed and ask for table formatting. In this technique, the model is prompted to solve the problem, critique its solution, and then resolve the problem considering the problem, solution, and critique. The problem-solving process repeats until a it reaches a predetermined reason to stop.
Uloženo dne 28.12.2022.
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