ChatGPT API Tips Guide and Best Practices
Learn how cloud-native JuiceFS empowers quantitative hedge funds to enhance AI training and achieve elastic throughput in the cloud.
This is not a programming or ChatGPT API integration tutorial, these are some key ideas and points to keep in mind when using the ChatGPT API.
Large-scale language models provide unprecedented functionality for your software products. As developers, it is crucial to learn this technology and integrate it into our projects. Platforms like OpenAI provide APIs for interacting with these models, which we can leverage to enhance our software.
Recently, I completed a fascinating course, "ChatGPT Tips Engineering for Developers." I learned some incredible techniques to get the most out of OpenAI’s ChatGPT API, and I’m happy to share those insights with you in this post.
Guidelines
Clarity is critical when interacting with ChatGPT. Here are some useful strategies:
- Separate directives and input text: This allows you to test your directives using a variety of inputs. For example, if you ask ChatGPT to summarize an article, separate the "Summary the following article:" directive from the article text itself.
- Use delimiters: They help specify the boundaries between instructions and input, reducing the possibility of prompt injection. A colon or newline character can serve as a simple but effective delimiter.
- Request Structured Output: If you need to programmatically parse ChatGPT's response, ask it to structure its output in a specific way. For example, you might ask, "List the key points in the following text as key points:".
- Check Input Conditions: To reduce the chance that the model will produce irrelevant or inaccurate responses (a phenomenon called "hallucinations"), specify any conditions that the input must meet. For example, "If the text contains a date, please provide the day of the week that date is."
- Few-Shots Prompting: If possible, providing some examples of the required input and output patterns can guide the model to produce Similar results.
Iterative Prompt Development
Crafting the perfect prompt with ChatGPT is truly an iterative process that requires a keen understanding of the goal and a willingness to try and learn.
Let’s break it down into actionable steps:
- Defining the Goal: The first step to rapid production is to have a clear understanding of the desired output. Ask yourself, what do you want ChatGPT to generate? For example, if your goal is to extract key points from text, then your goal is to list the main ideas in the input text.
- Writing an Initial Prompt: Based on your goals, write an initial prompt. Prompts are commands or questions given to ChatGPT to guide its response. For the above goal, an initial prompt could be "List the main ideas in the following text:".
- Test the prompt: Now, it’s time to test the prompt. Run it through the model and see what kind of output you get. Does it fit your goals? If not, it's time to iterate.
- Analysis output: The output of the analysis model. Which aspects of the response meet your goals, and which parts miss them? This will give you clues on how to adjust your prompts.
- Optimization Tips: Adjust your tips based on your analysis. For example, if the model doesn't capture the main idea exactly as you expected, you can refine the prompt to be more specific, such as "Summarize the following text into bullet points:".
- Repeat the process: Continue testing, analyzing, and refining your prompt until it consistently produces the desired output. Remember, this is an iterative process. It may take a few rounds of refinement to get it just right.
- Generalization Tip: Once you have a tip that applies to a specific case, try to generalize it to other similar cases. This means testing the prompt with a variety of input text to make sure it works for a wide range of scenarios.
Summary
Summary is a powerful use case for the ChatGPT API, but it is crucial to understand how to use it effectively. Here are some guidelines:
- Define Purpose: If you want to use the abstract in a specific way, make sure you make it clear in the description.
- Keep Focused: Let the model focus on a specific part of the input if needed. For example, if you are only interested in the financial aspects of a business report, you might ask, "Summary the financial information in the following report:".
- Extract rather than summarize: In some cases, it may be more useful to extract key information rather than summarize. For example, you can prompt "List the names of all people mentioned in the following text:".
Inference
ChatGPT can also infer insights from text, performing tasks such as sentiment analysis, classification, categorization and tagging. Here are some ways you can take advantage of this feature:
- Multi-tasking: You can ask the model to perform multiple tasks in the same instruction and produce output in a specific format. For example, "Analyze the sentiment of the following comments and classify them as positive, negative, or neutral:".
- Test multiple examples: A prompt that works well on one set of inputs may not perform well on other inputs. To ensure that your directive works properly across a wide range of inputs, test it with a variety of examples.
- Include your own labels: If you have a predefined set of categories, you can include them in the directive and ask the model to choose the most relevant category for a given input. For example, "Place the following text into one of the following categories: technical, environmental, political, or cultural:".
Transformation
ChatGPT is also able to perform a variety of text transformations, including language translation and format conversion.
- Language Identification and Translation: You can ask the model to identify the language of the input text, or translate it into another language. For example, "Translate the following Spanish text into English:".
- Tone Conversion: You can convert the input text into a different tone, such as formal, casual, or conversational. For example, "Rewrite the following formal text in a casual tone:".
- Format Conversion: This model can convert text from one format to another, such as JSON to HTML or CSV to JSON. For example, "Convert the following JSON data to HTML table format:".
- Proofreading: You can ask the model to proofread the text and make corrections to grammar, punctuation, and spelling. For example, "Proofread and correct any errors in the following text:".
Extensions
ChatGPT can also expand short input text into longer, more detailed posts, such as blog posts, articles, or email responses. Here are some things to consider:
- Provide context: If the output is used in a specific context, be sure to provide that context in the prompt. For example, "Write a reply to the following email, thanking the sender for the suggestion and agreeing to implement it:".
- Disclosure of AI Involvement: If the output is communicated to users, it is recommended to disclose that it was generated by AI to maintain transparency.
- Adjust temperature: The temperature parameter controls the creativity of the model. Lower temperatures (closer to 0) make the model's output more deterministic, while higher temperatures (closer to 1) allow for more creative responses.
Developing Chatbots
You can use ChatGPT to create a chatbot with specific behaviors:
- Set Role: Use " System” role to structure the conversation. This message tells the model how it should respond to "user" messages. For example, "You are a helpful assistant who always provides detailed answers:".
- Provide user context: Include the user's name, details, and any other relevant context in the initial user message. For example, "The user is a junior programmer looking for help with a Python syntax error:".
- Keep in mind the limitations of this model: The ChatGPT model cannot remember previous interactions, so you must provide previous messages each time you make a request. For example, if the user asked a question in a previous message, include that message in the next request if it's relevant to the ongoing conversation.
- Controlling Heat: For output intended to be presented to the user, you can use higher heat to get a more unpredictable response. For output intended to be parsed programmatically, use lower heat for more reliable results.
Understanding these tip engineering concepts can significantly improve your interactions with the ChatGPT API, making your applications more efficient and user-friendly.
The above is the detailed content of ChatGPT API Tips Guide and Best Practices. For more information, please follow other related articles on the PHP Chinese website!
Hot AI Tools
Undresser.AI Undress
AI-powered app for creating realistic nude photos
AI Clothes Remover
Online AI tool for removing clothes from photos.
Undress AI Tool
Undress images for free
Clothoff.io
AI clothes remover
Video Face Swap
Swap faces in any video effortlessly with our completely free AI face swap tool!
Hot Article
Hot Tools
Notepad++7.3.1
Easy-to-use and free code editor
SublimeText3 Chinese version
Chinese version, very easy to use
Zend Studio 13.0.1
Powerful PHP integrated development environment
Dreamweaver CS6
Visual web development tools
SublimeText3 Mac version
God-level code editing software (SublimeText3)
Hot Topics
Bytedance Cutting launches SVIP super membership: 499 yuan for continuous annual subscription, providing a variety of AI functions
Jun 28, 2024 am 03:51 AM
This site reported on June 27 that Jianying is a video editing software developed by FaceMeng Technology, a subsidiary of ByteDance. It relies on the Douyin platform and basically produces short video content for users of the platform. It is compatible with iOS, Android, and Windows. , MacOS and other operating systems. Jianying officially announced the upgrade of its membership system and launched a new SVIP, which includes a variety of AI black technologies, such as intelligent translation, intelligent highlighting, intelligent packaging, digital human synthesis, etc. In terms of price, the monthly fee for clipping SVIP is 79 yuan, the annual fee is 599 yuan (note on this site: equivalent to 49.9 yuan per month), the continuous monthly subscription is 59 yuan per month, and the continuous annual subscription is 499 yuan per year (equivalent to 41.6 yuan per month) . In addition, the cut official also stated that in order to improve the user experience, those who have subscribed to the original VIP
Context-augmented AI coding assistant using Rag and Sem-Rag
Jun 10, 2024 am 11:08 AM
Improve developer productivity, efficiency, and accuracy by incorporating retrieval-enhanced generation and semantic memory into AI coding assistants. Translated from EnhancingAICodingAssistantswithContextUsingRAGandSEM-RAG, author JanakiramMSV. While basic AI programming assistants are naturally helpful, they often fail to provide the most relevant and correct code suggestions because they rely on a general understanding of the software language and the most common patterns of writing software. The code generated by these coding assistants is suitable for solving the problems they are responsible for solving, but often does not conform to the coding standards, conventions and styles of the individual teams. This often results in suggestions that need to be modified or refined in order for the code to be accepted into the application
Seven Cool GenAI & LLM Technical Interview Questions
Jun 07, 2024 am 10:06 AM
To learn more about AIGC, please visit: 51CTOAI.x Community https://www.51cto.com/aigc/Translator|Jingyan Reviewer|Chonglou is different from the traditional question bank that can be seen everywhere on the Internet. These questions It requires thinking outside the box. Large Language Models (LLMs) are increasingly important in the fields of data science, generative artificial intelligence (GenAI), and artificial intelligence. These complex algorithms enhance human skills and drive efficiency and innovation in many industries, becoming the key for companies to remain competitive. LLM has a wide range of applications. It can be used in fields such as natural language processing, text generation, speech recognition and recommendation systems. By learning from large amounts of data, LLM is able to generate text
Can fine-tuning really allow LLM to learn new things: introducing new knowledge may make the model produce more hallucinations
Jun 11, 2024 pm 03:57 PM
Large Language Models (LLMs) are trained on huge text databases, where they acquire large amounts of real-world knowledge. This knowledge is embedded into their parameters and can then be used when needed. The knowledge of these models is "reified" at the end of training. At the end of pre-training, the model actually stops learning. Align or fine-tune the model to learn how to leverage this knowledge and respond more naturally to user questions. But sometimes model knowledge is not enough, and although the model can access external content through RAG, it is considered beneficial to adapt the model to new domains through fine-tuning. This fine-tuning is performed using input from human annotators or other LLM creations, where the model encounters additional real-world knowledge and integrates it
Five schools of machine learning you don't know about
Jun 05, 2024 pm 08:51 PM
Machine learning is an important branch of artificial intelligence that gives computers the ability to learn from data and improve their capabilities without being explicitly programmed. Machine learning has a wide range of applications in various fields, from image recognition and natural language processing to recommendation systems and fraud detection, and it is changing the way we live. There are many different methods and theories in the field of machine learning, among which the five most influential methods are called the "Five Schools of Machine Learning". The five major schools are the symbolic school, the connectionist school, the evolutionary school, the Bayesian school and the analogy school. 1. Symbolism, also known as symbolism, emphasizes the use of symbols for logical reasoning and expression of knowledge. This school of thought believes that learning is a process of reverse deduction, through existing
To provide a new scientific and complex question answering benchmark and evaluation system for large models, UNSW, Argonne, University of Chicago and other institutions jointly launched the SciQAG framework
Jul 25, 2024 am 06:42 AM
Editor |ScienceAI Question Answering (QA) data set plays a vital role in promoting natural language processing (NLP) research. High-quality QA data sets can not only be used to fine-tune models, but also effectively evaluate the capabilities of large language models (LLM), especially the ability to understand and reason about scientific knowledge. Although there are currently many scientific QA data sets covering medicine, chemistry, biology and other fields, these data sets still have some shortcomings. First, the data form is relatively simple, most of which are multiple-choice questions. They are easy to evaluate, but limit the model's answer selection range and cannot fully test the model's ability to answer scientific questions. In contrast, open-ended Q&A
SOTA performance, Xiamen multi-modal protein-ligand affinity prediction AI method, combines molecular surface information for the first time
Jul 17, 2024 pm 06:37 PM
Editor | KX In the field of drug research and development, accurately and effectively predicting the binding affinity of proteins and ligands is crucial for drug screening and optimization. However, current studies do not take into account the important role of molecular surface information in protein-ligand interactions. Based on this, researchers from Xiamen University proposed a novel multi-modal feature extraction (MFE) framework, which for the first time combines information on protein surface, 3D structure and sequence, and uses a cross-attention mechanism to compare different modalities. feature alignment. Experimental results demonstrate that this method achieves state-of-the-art performance in predicting protein-ligand binding affinities. Furthermore, ablation studies demonstrate the effectiveness and necessity of protein surface information and multimodal feature alignment within this framework. Related research begins with "S
SK Hynix will display new AI-related products on August 6: 12-layer HBM3E, 321-high NAND, etc.
Aug 01, 2024 pm 09:40 PM
According to news from this site on August 1, SK Hynix released a blog post today (August 1), announcing that it will attend the Global Semiconductor Memory Summit FMS2024 to be held in Santa Clara, California, USA from August 6 to 8, showcasing many new technologies. generation product. Introduction to the Future Memory and Storage Summit (FutureMemoryandStorage), formerly the Flash Memory Summit (FlashMemorySummit) mainly for NAND suppliers, in the context of increasing attention to artificial intelligence technology, this year was renamed the Future Memory and Storage Summit (FutureMemoryandStorage) to invite DRAM and storage vendors and many more players. New product SK hynix launched last year
