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Prompt engineering is not limited to text-to-image generation and has found a fitting application in AI-generated art. Various templates and "recipes" have been created to optimize the process of providing the most effective textual inputs to the model. OpenAI has published such "recipes" for their language model that can be adapted to different downstream tasks, including grammar correction, text summarization, answering questions, generating product names, and functioning as a chatbot. [1]
In language models like GPT, the output quality is influenced by a combination of prompt design, sample data, and temperature (a parameter that controls the “creativity” of the responses). Furthermore, to properly design a prompt, the user has to have a good understanding of the problem, good grammar skill, and produce many iterations. [2]
Therefore, to create a good prompt, it’s necessary to be attentive to the following elements:
Besides this, a prompt can also have other elements such as the desired length of the response, the output format (GPT-3 can output various code languages, charts, and CSVs), and specific phrases that users have discovered that work well to achieve specific outcomes (e.g. “Let's think step by step,” “thinking backwards,” or “in the style of [famous person]”). [3]
Prompt Engineering or ChatGPT should be avoided in certain scenarios. Firstly, when 100% reliability is required. Secondly, when the accuracy of the model's output cannot be evaluated. Finally, when generating content that is not in the model's training data, these techniques may not be the best approach to use. [4]
The composition of a prompt typically consists of various parts, although not all prompts incorporate every component. However, effective prompts often include two or more of these components for better results. In general, the components of a prompt are arranged in a specific order. The prompt structure starts with instructions/task description, followed by context/external information (if applicable), followed by examples (if applicable), then the user input/query, and finally, the output indicator. This organized structure ensures the prompt is clear and comprehensible, resulting in more accurate and relevant responses from the language model.
Instructions or Task description tells the model what to do. What do you want, and how to create the output. How to use the context or examples. What to do with the query. Clear instructions enable the model to deliver more accurate and relevant responses.
Context or external information is the additional information for the model that might not exist in the model. They can be manually inserted into the prompt, retrieved via a vector database (retrieval augmentation), or gathered from other sources such as APIs. Providing context helps the model generate more informed and precise responses.
Example(s) are a few instances of user input/query along with corresponding output provided by the user and shown to the language model. It is often utilized in few-shot prompting and is entirely optional.
The user input or query is typically submitted by a human user. Although not always the case, most prompts involve a query input from the user. This input serves as the basis for the model's response and assists in tailoring the output to the user's specific needs.
The output indicator signifies the beginning of the generated text. For instance, when generating Python code, the word "import" may be used to indicate that the model should start writing Python code, as many Python scripts begin with the "import" statement. The output indicator helps to maintain the desired format and structure of the response.
Example using ChatGPT with GPT-4
Instruction: Identify the president of the United States of America for the given year.
Context:
Considering that GPT-4's knowledge is limited to information up until September 2021. We need to provide some context to help with the task with Joe Biden.
Examples:
Query and output indicator:
In a text-to-text model, the user can insert diferent parameters in the prompt to modulate its response. The following parameter and prompt examples are taken from Matt Night's GitHub:
The process of prompt refinement is a method to improve the quality of written content by transforming it into a compelling, imaginative, and relatable piece, fixing "soulless writing". The aim is to make the content engaging and impactful by focusing on storytelling, using persuasive language, emphasizing emotion and sensory details, making the content concise and highlighting key points. To create a sense of urgency and make the content relatable, the language can be personalized to the reader and potential objections can be addressed. [4]
To increase the readability, there are several strategies that can be employed in the prompt. First, it's important to request answers with a clear and concise language. Additionally, visual aids such as diagrams can be requested (e.g "Using mermaid.js you can include diagrams to illustrate complex concepts (low reliability)") Asking the AI to use of headings and subheadings is also recommended to divide the document into sections with clear organization. Important information should be emphasized using bold or italic text, and real-life examples such as case studies or real-world examples can be included to make concepts more relatable. Consistent formatting, including a consistent font, font size, and layout, should be used throughout the document. Analogies or comparisons can be employed to explain complex ideas, and asking for writing in active voice can make sentences more engaging and easier to follow. [4]
The model can also be asked to act as a technical advisor, mentor, quality assurance, code reviewer, debugging assistant, compliance checker, code optimization specialist, accessibility expert, search engine optimization specialist, and performance analyst. Examples of prompts for the use cases are available here.
Zero-shot prompting involves giving the model a task description/instruction and directly asking it for a result. It does not show the model any examples.
Input (task description/instruction): Who was the president of United States in 1999?
Output:
Few-shot prompting have a task description, a few examples and then a prompt.
What are the 2-letter abbreviations for the following U.S. states?
New York is beautiful during autumn: NY
I went to school in California: CA
Vacationing in Florida is fun:
The task description:
The examples:
The prompt:
Vacationing in Florida is fun: FL
In few-shot prompting, the model is presented with high-quality demonstrations, including input and desired output, for the target task. This approach enables the model to understand the human intention better and the desired criteria for answers, often resulting in improved performance compared to zero-shot prompting. However, this comes at the expense of increased token consumption and may reach the context length limit for longer input and output texts.
Numerous studies have explored how to construct in-context examples to maximize performance. Prompt format, training examples, and example order can lead to dramatically different performance outcomes, ranging from near-random guessing to near state-of-the-art (SoTA) results.
Zhao et al. (2021) investigated few-shot classification using LLMs, specifically GPT-3. They identified several biases that contribute to high variance in performance: (1) majority label bias, (2) recency bias, and (3) common token bias. To address these biases, they proposed a method to calibrate label probabilities output by the model to be uniform when the input string is N/A.[5]
Liu et al. (2021) suggested choosing examples that are semantically similar to the test example by employing k-nearest neighbors (KNN) clustering in the embedding space.[6]
Su et al. (2022) proposed a graph-based approach to select a diverse and representative set of examples: (1) construct a directed graph based on the cosine similarity between samples in the embedding space (for example using SBERT or other embedding models), and (2) start with a set of selected samples and a set of remaining samples, scoring each sample to encourage diverse selection.[7]
Rubin et al. (2022) suggested training embeddings through contrastive learning specific to one training dataset for in-context learning sample selection. This approach measures the quality of an example based on a conditioned probability assigned by the language model.[8]
Zhang et al. (2022) explored using Q-Learning for sample selection in LLM training.[9]
Diao et al. (2023) proposed identifying examples with high disagreement or entropy among multiple sampling trials based on uncertainty-based active learning. These examples can then be annotated and used in few-shot prompts.[10]
A general recommendation is to maintain a diverse selection of examples relevant to the test sample and present them in random order to avoid majority label bias and recency bias. Increasing model sizes or including more training examples does not necessarily reduce variance among different permutations of in-context examples. The exact order may work well for one model but poorly for another.
When the validation set is limited, Lu et al. (2022) suggested choosing the order such that the model does not produce extremely unbalanced predictions or exhibit overconfidence in its predictions.[11]
Give ChatGPT a role it can emulate and roleplay as by starting with the text "Act as a...".
For example, if you have a history exam about Rome, you can ask the AI to
Act as a historian and tell me about Julius Caesar
For additional roles, check out this website: https://prompts.chat/
Give your prompt personality by giving it a style and adding descriptors. Styles include tones, formality, and the domain of the writer. Descriptors are adjectives that you add to tweak your prompt.
By giving your prompt personalities, the generated content would be harder for AI content detectors to detect.
Self-consistency sampling is a method for generating multiple outputs using a temperature greater than 0 and selecting the best candidate from the generated outputs. The criteria for choosing the best candidate may vary according to the task. A common approach is to use majority vote. In tasks that are easy to validate, such as programming questions with unit tests, the outputs can be run through an interpreter and their correctness can be verified using unit tests.[12]
Chain of Thought Prompting (CoT prompting) is a technique introduced by Wei et al. (2022) to generate a sequence of short sentences describing step-by-step reasoning, known as reasoning chains or rationales, leading to the final answer. CoT prompting is particularly useful for complex reasoning tasks when applied to large language models (for example those with over 50 billion parameters), while simpler tasks may benefit only marginally.[13]
There are two main types of CoT prompting:
Few-shot CoT prompting involves providing the model with a limited number of demonstrations, each containing either manually written or model-generated high-quality reasoning chains. Examples of such demonstrations are provided in the original article, showcasing how this type of prompting is used to solve various mathematical reasoning problems.
Zero-shot CoT prompting uses natural language statements, such as "Let's think step by step" or "Let's work this out step by step to be sure we have the right answer," to explicitly encourage the model to generate reasoning chains. Following this, a statement like "Therefore, the answer is" is used to prompt the model to produce the final answer.[14][15]
Several techniques have been proposed to improve the accuracy and effectiveness of CoT prompting:
| Number | Prompting Principle |
|---|---|
| 1 | No need to be polite with LLM so there is no need to add phrases like "please", "if you don't mind", "thank you", "I would like to", etc., and get straight to the point. |
| 2 | Integrate the intended audience in the prompt, for example the audience is an expert in the field. |
| 3 | Break down complex tasks into a sequence of simpler prompts in an interactive conversation. |
| 4 | Employ affirmative directives such as 'do', while steering clear of negative language like 'don’t'. |
| 5 | When you need clarity or a deeper understanding of a topic, idea, or any piece of information, utilize the following prompts:
|
| 6 | Add "I'm going to tip $xxx for a better solution!" |
| 7 | Implement example-driven prompting (Use few-shot prompting). |
| 8 | When formatting your prompt, start with "###Instruction###", followed by either "###Example###" or "###Question###" if relevant. Subsequently, present your content. Use one or more line breaks to separate instructions, examples, questions, context, and input data. |
| 9 | Incorporate the following phrases: "Your task is" and "You MUST". |
| 10 | Incorporate the following phrases: “You will be penalized”. |
| 11 | Use the phrase "Answer a question given in a natural, human-like manner" in your prompts. |
| 12 | Use leading words like writing "think step by step". |
| 13 | Add to your prompt the following phrase “Ensure that your answer is unbiased and does not rely on stereotypes". |
| 14 | Allow the model to elicit precise details and requirements from you by asking you questions until he has enough information to provide the needed output (for example, “From now on, I would like you to ask me questions to…”). |
| 15 | To inquire about a specific topic or idea or any information and you want to test your understanding, you can use the following phrase: “Teach me the [Any theorem/topic/rule name] and include a test at the end, but don't give me the answers and then tell me if I got the answer right when I respond”. |
| 16 | Assign a role to the large language models. |
| 17 | Use Delimiters. |
| 18 | Repeat a specific word or phrase multiple times within a prompt. |
| 19 | Combine Chain-of-thought (CoT) with few-Shot prompts. |
| 20 | Use output primers, which involve concluding your prompt with the beginning of the desired output. Utilize output primers by ending your prompt with the start of the anticipated response. |
| 21 | To write an essay text/paragraph/article or any type of text that should be detailed: “Write a detailed essay/text/paragraph for me on [topic] in detail by adding all the information necessary”. |
| 22 | To correct/change specific text without changing its style: "Try to revise every paragraph sent by users. You should only improve the user's grammar and vocabulary and make sure it sounds natural. You should not change the writing style, such as making a formal paragraph casual". |
| 23 | When you have a complex coding prompt that may be in different files: “From now and on whenever you generate code that spans more than one file, generate a [programming language] script that can be run to automatically create the specified files or make changes to existing files to insert the generated code. [your question]". |
| 24 | When you want to initiate or continue a text using specific words, phrases, or sentences, utilize the following prompt:
|
| 25 | Clearly state the requirements that the model must follow in order to produce a valid sample, include the in the form of the keywords, regulations, hint, or instructions |
| 26 | To write any text, such as an essay or paragraph, that is intended to be similar to a provided sample, include the following instructions: *Please use the same language based on the provided paragraph/title/text /essay/answer]. |
Genearte code using models like the OpenAI Codex.
Give the coding model a high-level task description. To improve the quality of the generated code, it's recommended to start the prompt with a broad description of the task at hand. For example, if you want to generate Python code to plot data from a standard dataset, you can provide a prompt like this:
# Load iris data from scikit-learn datasets and plot the training data.
However, sometimes the generated code may not be optimal, in which case you can provide more specific instructions such as importing libraries before using them. By combining a high-level task description with detailed user instructions, you can create a more effective prompt for coding model to generate code.
Gives the coding model examples. Imagine you prefer a unique style of writing Python code that differs from what model produces. Take, for instance, when adding two numbers, you prefer to label the arguments differently. The key to working with models like Codex is to clearly communicate what you want it to do. One effective way to do this is to provide examples for Codex to learn from and strive to match its output to your preferred style. If you give the model a longer prompt that includes the example mentioned, it will then name the arguments in the same manner as in the example.
See also zero shot, one shot and few shot learning
If you want to use a library that the coding model is not familiar with, you can guide it by describing the API library beforehand.
For instance, the Minecraft Codex sample uses the Simulated Player API in TypeScript to control a character in the game. Since this is a newer API that the model does not know about yet, When given the prompt, the model attempts to make an educated guess based on the terms "bot" and "Simulated Player". However, the resulting code is not correct.
To correct this, you can show the model model the API definition, including function signatures and examples, so that it can generate code that follows the API correctly. As demonstrated in the example, by providing high-level context in the form of the API definition and examples, the model can understand what you want it to do and generate more accurate code.
Describe YOUR SCENE. Use sensory language and detail to describe the OBJECTS IN THE SCENE vividly. Describe SPECIFIC DETAILS and any other sensory details that come to mind. Vary the sentence structure and use figurative language as appropriate. Avoid telling the reader how to feel or think about the scene.
These are user-created parameters. They serve to convey the intent of the users in a more concise way. These are not part of the model API but patterns the LLM has picked up through its training. These parameters are just a compact way to deliver what is usually expressed in natural language.
Prompt: Write a paragraph about how adorable a puppy is.
Temperature: 1.0
Sarcasm: 0.9
Vividness: 0.4
We add "Prompt: " to the start of our prompt to make sure ChatGPT knows where our prompt is. We add the GPT parameter temperature, which goes from 0 to 1 to indicate the following parameters also range from 0 to 1. Then we list our parameters along with their values which go from 0 to 1 (0 is the smallest, and 1 is the largest). Note that having too many or contradictory parameters may lower the quality of the response.
