With the release of SORA, the development speed of large models has not only not encountered bottlenecks, but has further accelerated, and AGI is beckoning to us.
Behind this, whether it is text, audio, or large model processing, prompt, a seemingly simple concept, plays a vital role.
A well-designed prompt can not only help the model capture user intent more accurately, but also stimulate the model's creativity and imagination to generate more colorful content.
However, how to design an effective prompt is a challenging task. It requires a deep understanding of how the model works, as well as creativity and imagination. Therefore, mastering the design skills of prompts is essential to realize the full potential of large language models.
Next, we'll dive into prompt design in large language models. Starting from the basic principles and importance of prompt, we will share practical design tips and methods, and show the application of prompt in different scenarios through case studies. Whether you are an ordinary reader interested in artificial intelligence, or an expert and scholar who is engaged in research in related fields, I believe this series of articles will bring you new inspiration and harvest. Let's explore the mystery of prompt and witness the infinite possibilities of large language models together!
How prompt affects the output.
1.What is a prompt
A prompt is an input text or instruction given to a model to guide the model to produce a specific type of output or meet specific requirements. A prompt is more than just a simple start or introductory statement, it's actually the key to the model understanding and generating text.
The design of a prompt can be very varied, from a simple few words to a complete sentence or paragraph. The goal is to provide the model with enough information so that it can understand and produce the desired output.
The emergence of ChatGPT has changed the way traditional machines interact, making it easier and more efficient for everyone to interact with AI in a natural and intuitive way. However, prompts can also be good and bad. A good prompt can produce more creative and expected output, while a bad prompt may output off-topic or low-quality output.
How to create excellent prompts and achieve efficient AI communication requires a necessary understanding of how LLMs work.
2.How LLMs work
2.1. Text input and encoding.
When we provide a prompt to a large model, it is usually a short text description that tells the model what the intent is to implement and how the output will be done. For example:
The large model does not understand the content directly, and the necessary "translation" is required. The process involves text encoding and word embedding.
Text encoding (tokenizer).
The tokenizer will first tokenize the prompt and split it into a series of tokens. For Chinese text, word segmentation is a critical step because Chinese words do not have obvious space separation like in English.
Each token represents a word or punctuation mark.
Word embedding
Each token is mapped to a fixed-size vector. This vector is the result of word embedding, which captures the semantic information of the token.
In the case of the token "Xi'an", it is mapped to a vector that learns to semantically associate with words such as "China", "Shaanxi", and "Ancient Capital" during the training process.
These vectors serve as inputs to the model, helping the model understand the meaning of the text and being able to take these semantic associations into account when generating an account of the history of the Xi'an city wall.
2.2 Contextual Handling.
The model takes the encoded prompt as context input. This context is encoded into one or more vectors that capture the key information in the prompt.
The self-attention mechanism of the large model base transformer captures dependencies and complex patterns in the text.
2.3 Generation process.
Through greedy search or cluster search, the model considers candidate tokens and selects the generation with the highest probability.
When generating each token, the model is based on the previous context and the generated text.
2.4 Output text.
The generation process stops when the model reaches a preset length limit or end mark. Eventually, the model outputs the generated text, formats it, adjusts the length, and filters the information.
3.Requirements for the use of prompt
High-quality prompts are one of the key factors that inspire natural language models to generate high-quality text. A good prompt can help the model better understand the user's intent and needs, resulting in more accurate, natural, and useful text.
Provide clear contextual information
A good prompt should provide clear contextual information to help the model better understand the user's intent and needs. This can include the background of the problem, the goal of the task, the entities and relationships involved, etc.
Contains enough information
A good prompt should contain enough information to ensure that the resulting text accurately and completely answers the user's question or meets the user's needs. If the prompt contains insufficient information, the model may generate inaccurate or incomplete text.
Use natural language
A good prompt should use natural, fluent language to enable the model to better understand and generate text. If the prompt contains unnatural, ambiguous, or erroneous language, the model may generate inaccurate or unnatural text.
Meet specific task needs
A good prompt should be designed and optimized for the specific needs of the task to ensure that the resulting text meets the specific needs. Different tasks may require different prompt design and optimization strategies.
Versatility and stability
A high-quality prompt should also be able to produce good results by replacing the task body, and the same prompt word can generate content more stably for multiple times.
prompt.
1.prompt specification
The creation of prompt follows a certain format, and the common format is as follows:
Role: The role played by the large model.
Task: The task to be performed by the large model.
Details: More detailed requirements for large models to perform tasks can be added with multiple weights decreasing.
Format: Description of the format, typography, etc.
2.prompt development steps
2.1. Determine the foundation.
Use Persona + Task to determine if the correct answer can be generated, and then refine it step by step.
In the training process of LLM, most of the training data is on the Internet, and the correct answer may not be obtained due to the data and its labels. Due to the lack of clarity in the expression of the question in the prompt, such as separators, etc., the correct answer cannot be obtained.
2.2. Focus on the order.
During the model training process, the weights are set for keywords in order. Therefore, when writing prompts, it is necessary to put important keys first.
2.3 Add emphasis.
When writing prompts, sometimes more keywords are written in order to describe the problem more clearly and in detail, and LLMs sometimes omit them when analyzing, resulting in inaccurate results. In this case, you need to set some emphatic words to remind LLMs of some necessary keys.
2.4 Create a persona.
Use "Assuming you're ......"You play a ......"Imitation ......"I want you to act as a ......Such a keyword begins. During model training, the data will be classified according to the labels according to different scenarios, and the real-time settings will be set to be closer to the labels so that the results will be more accurate.
prompt advanced.
1. icl
In-Context Learning (ICL) was first proposed in GPT-3, which aims to select a small number of annotated samples from the training set, design task-related instructions to form a prompt template, and guide the test samples to generate corresponding results.
ICLs are divided into:
few-shot learning
one-shot learning
zero-shot learning
2. cot
The charm of large models lies in the conceptual reasoning ability displayed by large models. The process of being able to derive new conclusions based on several known premises. Unlike understanding, reasoning is generally a "multi-step" process, and the process of reasoning can form the necessary "intermediate concepts" that will assist in solving complex problems.
2.1 COT concept.
In 2022, it was first proposed in Google's "Chain-of-Thought Prompting Elicits Reasoning in Large Language Models" that the performance of large models can be significantly improved by involving them in the process of gradually decomposing a complex problem into step-by-step sub-problems and solving them in turn. The intermediate step in this series of reasoning is called the chain of thought.
2.2 COT workflow.
A complete prompt containing cot is often composed of three parts: instructions, rationale, and exemplars.
Instructions are used to describe the problem and inform the output format of the large model, the logical basis refers to the intermediate inference process of the cot, which can contain the solution to the problem, the intermediate inference step, and any external knowledge related to the problem, while the example refers to the basic format of providing input-output pairs for the large model in a few-shot manner, and each example contains: the problem, the inference process, and the answer.
cot can also be divided into zero-shot-cot and few-shot-cot depending on whether an example is needed. Whereas, zero-shot-cot simply adds "let" to the instruction's think step by step", you can "wake up" the reasoning ability of the large model.
2.3 COT example.
3.prompt template
After the prompt is completed through setting and optimization, it is possible to replace the role and task to check the stability of the answer and establish a template for subsequent use.
Summary. Earlier, we introduced the basic guidelines of prompts and how to write prompts. As technology continues to advance, large language models will continue to play an important role in various fields. Whether it's used to generate articles, summaries, or for scenarios such as chatbots and smart assistants, prompt will continue to play its charm.
Next, we will guide readers through specific cases and steps on how to write prompts, and share the challenges and solutions that may be encountered in practice.
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