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Have you ever wondered how to get the most relevant responses from LLM-based chatbots like ChatGPT and Claude? Enter prompt engineering – the art and science of crafting the perfect input to persuade these AI brains to give you precisely what you want. It is like learning to speak ‘AI’ fluently.
It involves the suitable input text to guide these models towards producing desired outputs with enhanced accuracy and relevance. It goes beyond simple queries, leading to a deep understanding of LLM architectures and their response patterns. You can significantly improve model performance across various tasks by manipulating prompts’ context, format, and content.
Prompt engineering offers a non-invasive way to fine-tune model behavior without altering the underlying architecture, opening new pathways for AI application and research.
We will explore the principles and strategies behind effective, prompt engineering, different techniques, research directions, and much more. We will also examine its real-world applications and discuss how it’s reshaping our interactions with AI systems.
Generative AI is an artificial intelligence system that generates novel content based on training data and input prompts, such as text, images, audio, or code. Large language models (LLMs) like GPT-4 are an example of generative AI for text.
Prompt engineering involves carefully crafting the input instructions or ‘prompts’ given to generative AI models to elicit desired outputs. We will discuss the methods and applications of several prompt engineering techniques later in this article.
The importance of prompt engineering stems from several factors:
As generative AI systems become more integral to different domains, prompt engineering will be crucial to maximize the utility of these powerful but indeterministic tools.
While prompt engineering is powerful, it can be time-consuming to address every potential error in an AI application’s master prompt. Aporia’s Guardrails offers a solution by providing out-of-the-box safeguards that can replace the need for extensive prompt engineering when attempting to mitigate hallucinations, prompt injections, data leakages and more.
Few-shot prompting can enable in-context learning, where we provide demonstrations of the prompt to guide the model to an accurate response. The objective is to allow the model to adapt to specific tasks without fine-tuning.
Unlike zero-shot prompting, which relies solely on instructions, few-shot prompting uses examples to guide the model’s behavior. This approach is beneficial for complex tasks where zero-shot performance falls short, as it helps the model better understand the task requirements and expected output format.
To illustrate the concept, consider this example from Brown et al. (2020) to use a new word in a sentence correctly:
Prompt: A "whatpu" is a small, furry animal native to Tanzania. An example of a sentence that uses the word whatpu is:
We were traveling in Africa, and we saw this very cute whatpus.
To do a "farduddle" means to jump up and down fast. An example of a sentence that uses the word farduddle is:
Output: When we won the game, we all started to farduddle in celebration.This demonstrates how the model can learn to use a novel word in context after seeing one example.
Recent research has revealed key findings about effective few-shot prompts. Min et al. (2022) found that the label space and the distribution of input text specified by the demonstrations are important, regardless of whether the labels are correct for individual inputs.
They also discovered that the format of the demonstrations plays a crucial role in performance, with even random labels being better than no labels.
Interestingly, Touvron et al. (2023) noted that few-shot capabilities first emerged when models were scaled to sufficient size, as predicted by Kaplan et al. (2020). This suggests that the effectiveness of few-shot prompting is closely tied to the model scale.
Despite its usefulness, few-shot prompting has some limitations. It may need help with complex reasoning tasks that require multiple steps or deeper understanding.
Let’s illustrate a task that requires an understanding of prime numbers.
Prompt:
Determine if the product of the prime numbers in the list is even or odd: 2, 7, 11, 4, 6, 13, 9
A: The product is odd.
5, 3, 8, 11, 15, 7, 2 A: The product is even.
6, 17, 4, 19, 8, 23, 10 A: The product is odd.
3, 11, 2, 13, 7, 5, 9 A: The product is even.
7, 13, 3, 11, 2, 17, 19 A:
Output:
The product is odd.The output is incorrect. The correct answer should be the product is even because the list contains 2, the only prime number.
The method fails to break down multi-step processes, provide comprehensive demonstrations, or explain the underlying logic. These shortcomings make few-shot prompting inadequate for tasks requiring nuanced mathematical understanding or intricate logical reasoning.
This highlights the need for more advanced prompting techniques or specialized training approaches to achieve reliable performance on such complex tasks.
Chain-of-thought (CoT) prompting guides the model through intermediate reasoning steps before arriving at a final answer. This method involves providing the model with a structured sequence of thought processes, mimicking how a human might break down and solve a complex problem step by step. The goal is to enhance the model’s reasoning capabilities by explicitly outlining the logical steps to conclude.
The large LLMs can do the same if we provide a few examples (Wei et al., 2022). CoT can be done conveniently without readjusting model weights (i.e., in-context learning).
Let’s see an example from the research paper that illustrates how CoT prompting works for arithmetic reasoning:
This example shows the model breaking down the problem into smaller steps, calculating the total number of tennis balls by determining the number of balls in the cans and then adding them to the initial count.
Wei et al. believe that expressing intermediate steps in natural language enhances LLMs’ ability to express their problem-solving process.
This articulation improves LLM performance across multiple domains, as demonstrated in their experiments. While Wei et al. do not conclusively state whether LLMs truly “reason,” this finding is significant. It indicates that effective prompt engineering can substantially increase the utility of existing LLMs for specific tasks.
Least to Most prompting (LtM) takes CoT prompting further by breaking a problem into subproblems and then solving each subproblem. It is a technique inspired by real-world educational strategies for children.
Compared to CoT prompting, the problem to be solved is decomposed into subproblems that build upon each other. In the second step, these subproblems are solved one by one. Contrary to a chain of thought, the solution of previous subproblems is fed into the prompt trying to solve the next problem.
The purpose of least-to-most prompting is to help the model build up its reasoning incrementally, reducing the cognitive load and improving the accuracy of the final output.
Standard prompting with text-davinci-002 (the model used in the paper) results in 6% of successful SCAN problems solved, while Least to Most prompting results in an impressive 76% success rate. The results are more significant with code-davinci-002, where Least to Most prompting achieves a 99.7% success rate. (Denny et al. 2023)
Prompting, while useful, may not be the ideal approach for developing reasoning abilities in LLMs. A logical evolution would be to transition from prompting to fully interactive dialogues, allowing for real-time feedback to the models. This shift could potentially enhance learning efficiency and effectiveness.
The least-to-most prompting technique helps the model operate in the dynamic, bidirectional method of model instruction. This progression raises intriguing questions about the future of AI learning methodologies and the potential for more sophisticated human-AI interactions in problem-solving contexts.
Self-consistency prompting, proposed by Wang et al. (2022), is a strategy used with chain-of-thought (CoT) prompting to improve the reasoning abilities of large language models (LLMs). This technique involves generating multiple reasoning paths from a single prompt and then selecting the most consistent or probable answer from these paths. It contrasts with simpler, single-path decoding strategies by allowing the model to explore possible solutions before settling on the most coherent one.
Self-consistency is most effective when the tasks have clear, determinable answers and may not be suitable for open-ended or creative tasks where multiple equally valid outputs are possible. Furthermore, this method can be computationally intensive, as it involves generating multiple outputs and evaluating their consistency, which might not be practical for real-time applications or when computational resources are limited.
Let’s look at examples from the paper on how self-consistency can help repair greedy decode issues.
Self-consistency is also useful for collecting rationales when reasoning tasks with language models, providing uncertainty estimates and improved calibration of language model outputs. One limitation of self-consistency is that it incurs more computation costs. They suggest trying a small number of paths (e.g., 5 or 10) as a starting point to realize most of the gains while not incurring too much cost.
Maieutic Prompting is introduced as a new few-shot inference method inspired by Socratic dialogue. It generates a tree of logically related explanations and determines truth values that maximize the satisfaction of these relations. This method involves asking questions that lead the respondent to discover answers and insights independently rather than being directly provided with information.
Maieutic Prompting provides intrinsic interpretations of its inference process, enhancing transparency in decision-making.
The Maeutic prompting method has a few limitations. Generating Maieutic explanation trees and using MAX-SAT for truth value determination likely incur higher computational costs than simpler prompting methods. The current model focuses on relationships within individual maieutic trees.
It does not account for potential relationships between different trees or questions. Extending the method to broader task types and modeling inter-question relationships presents significant challenges that require further research.
Given these constraints, maieutic prompting is best used when the goal is to explore topics in-depth and when the user can afford the time for iterative prompt refinement.
Complexity-based prompting is a technique that involves adjusting the complexity of prompts to match the difficulty level of the task or the proficiency level of the model. This method aims to optimize the performance of language models by providing prompts that are neither too simple nor too complex to improve the model’s ability to generate accurate and relevant responses.
Using complexity-based prompts to include numerous reasoning steps significantly enhances the performance of language models like GPT-3 on tasks that require deep, multi-step reasoning. This method has shown substantial improvements in accuracy across several benchmarks, particularly in mathematics and other logical reasoning domains.
However, the effectiveness of complexity-based prompting is closely tied to the model’s underlying capabilities and the nature of the task. One limitation is that while it excels in structured environments or domains with well-defined reasoning steps, it may not be as effective for more open-ended or creative tasks where multiple correct outcomes are possible. Additionally, preparing complex prompts requires careful curation and understanding of the task, which can be resource-intensive (Yao Fu et al., 2023).
It is best suited for contexts where precision and accuracy in logical reasoning are critical and where the need for high-quality outcomes can justify the computational overhead.
As proposed by Liu et al. 2022, generated knowledge prompting asks the LLM to generate task-relevant knowledge about a given question/prompt and augment the prompt with it before generating a final response.
Liu et al. (2022) provide an example of their approach applied to a CommonsenseQA 2.0 task:
Input: Part of golf is trying to get a higher point total than others. Knowledge:
Output (Generated Knowledge): The objective of golf is to play a set of holes in the least number of strokes. A round of golf typically consists of 18 holes. Each hole is played once in the round on a standard golf course. Each stroke is counted as one point, and the total number of strokes is used to determine the winner of the game.
The generated knowledge is then used to augment the original question for the inference model:
Prompt:
Question: Part of golf is trying to get a higher point total than others. Yes or No?
Knowledge: The objective of golf is to play a set of holes in the least number of strokes. A round of golf typically consists of 18 holes. Each hole is played once in the round on a standard golf course. Each stroke is counted as one point, and the total number of strokes is used to determine the winner of the game.
Explain and Answer:
Output:
No, the objective of golf is not to get a higher point total than others. Instead, the objective is to play a set of holes in the least number of strokes. The total number of strokes is used to determine the winner of the game, not the total number of points.
Li et al. (2023) introduced Directional Stimulus Prompting, a novel technique to guide LLMs in generating the desired summary. This method employs a trainable policy language model to produce stimuli or hints. It demonstrates an increasing trend of using reinforcement learning to optimize LLMs.
The figure illustrates that Directional Stimulus Prompting offers advantages over standard prompting techniques. Notably, the policy model can be compact and optimized to generate guiding hints for a fixed, black-box LLM.
Let’s see how the policy model is trained using the RL strategy to guide the black box LLM.
The policy model can be trained with SFT and/or RL, where the reward is defined as the downstream task performance measure, such as the ROUGE score for the summarization task or other alignment measures like human preferences. The model can be trained with SFT and RL, where the reward is typically defined as the downstream task performance measure, such as the ROUGE score for the summarization task or other alignment measures like human preferences.
Tree of Thoughts (ToT), proposed by Yao et al. 2023 and Long (2023), addresses the limitations of conventional prompting methods in handling complex tasks that require exploration or strategic planning. This framework extends the concept of chain-of-thought prompting, facilitating a more comprehensive exploration of intermediate steps in language model-based problem-solving.
ToT operates by constructing and maintaining a tree structure of thoughts, where each thought represents a coherent language sequence serving as an intermediate problem-solving step.
This approach enables the language model to self-evaluate its problem-solving progress through these intermediate thoughts, employing a deliberate reasoning process. The framework combines the language model’s capacity for thought generation and evaluation with search algorithms, such as breadth-first search and depth-first search. This integration allows for systematically exploring the thought space, incorporating lookahead and backtracking capabilities.
You must first set up a few things when using Tree of Thoughts for different tasks. You must decide how many options to consider and how many steps to break the problem into.
Let us take the Game of 24 as an example in the paper. The researchers used this to show how ToT handles math problems. They broke it down into 3 steps, each a math equation. At each step, they kept the best 5 options to work with. Now, for the Game of 24, they used Breadth-First Search.
Here is how it works: The AI looks at each possible thought and decides if it is “sure,” “maybe,” or “impossible” to reach 24 from there. The researchers did this to keep the reasonable partial solutions that could be quickly checked, throw out the ones that were too big or too small, and hold onto the “maybe” ones for further consideration. They had the AI check each thought three times to be thorough. A picture shows this whole process – it helps to see how it all fits together.
Retrieval Augmented Generation (RAG) is an intuitive way to make language models smarter and more up-to-date.
The large language models are good at creative and reasoning tasks like writing code, short essays, planning, etc. However, when it comes to specific knowledge, they struggle and hallucinate. That is where RAG comes in.
RAG was first introduced by Lewis et. al 2020, and the idea was to give the language model a way to look up fresh information, like how we might Google something if we are unsure about a task.
When you ask RAG a question, it tries to find relevant information (eg. from Wikipedia). Then, it combines the question with the retrieved context to find an answer. This guides the model to use the latest facts without having to be constantly retrained. It is like giving the model a constantly updated cheat sheet from which to work.
Lewis and his team figured out a way to set this up to work for all sorts of tasks. They use a pre-trained model for the language part and a special index on Wikipedia to look things up.
Let us talk about a clever new way to use language models called Automatic Reasoning and tool use, or ART for short. Paranjape et al., (2023) propose a new framework, ART, that automatically uses a frozen LLM to generate intermediate reasoning steps as a program.
We have seen how the Chain of Thought helps models think step-by-step. ART mixes it with the ability to use tools along CoT. It writes out specific instructions for each task without human intervention.
Here is how ART works:
1. When the model gets a new task, it looks at examples of similar tasks that were solved using step-by-step thinking and tools.
2. As it works on the problem, it pauses whenever it needs to use a tool. It uses the tool, then takes that information and keeps going.
ART can figure out how to break down new tasks and use tools in the right places, even if it has never seen that exact task before. Moreover, if it makes mistakes? No problem. Humans can quickly fix the reasoning steps or add new tools by updating the examples ART learns from.
This approach is flexible and can handle different tasks without needing to be retrained each time. ART is also extensible as it enables humans to fix mistakes in the reasoning steps or add new tools by simply updating the task and tool libraries.
In 2023, Diao et al. introduced a new method called Active-Prompt, which refines how language models (LLMs) are tailored to specific tasks using example prompts that include chain-of-thought (CoT) reasoning designed by humans.
Below is an illustration of the approach. The process begins by presenting the LLM with a few available CoT examples. The model then produces several possible responses to training questions. An uncertainty metric, which assesses the level of agreement among the responses, is calculated.
Questions with the highest uncertainty are chosen for human annotation to create more effective examples. These newly annotated examples are then used to improve the model’s responses to each question, making the approach dynamic and responsive to the task’s needs.
Prompt engineering for text models like ChatGPT involves crafting precise and detailed prompts to guide the model in generating the desired output. Here are some key examples and techniques:
Place instructions at the beginning of the prompt and use delimiters like `###` or `”””` to separate instructions from the context. This helps define the task for the model clearly.
Example:
Summarize the text below as a bullet point list of the most important points.
Text: """
{text input here}
"""Be specific and detailed about the desired context, outcome, length, format, and style.
Example:
Write a short inspiring poem about OpenAI, focusing on the recent DALL-E product launch in the style of a famous poet.Output Format Articulation
Clearly articulate the desired output format through examples.
Example:
Extract the important entities mentioned in the text below. First extract all company names, then extract all people names, then extract specific topics which fit the content and finally extract general overarching themes.
Desired format:
Company names: <comma_separated_list_of_company_names>
People names: <comma_separated_list_of_people_names>
Specific topics: <comma_separated_list_of_specific_topics>
General themes: <comma_separated_list_of_general_themes>
Text: {text}
Start with zero-shot prompts and, if necessary, provide few-shot examples to guide the model.
Zero-shot Example:
Extract keywords from the below text.
Text: {text}
Keywords:Few-shot Example:
Extract keywords from the corresponding texts below.
Text 1: Stripe provides web developers with APIs to integrate payment processing into their websites and mobile applications.
Keywords 1: Stripe, payment processing, APIs, web developers, websites, mobile applications
Text 2: OpenAI has trained cutting-edge language models that are very good at understanding and generating text. Our API provides access to these models and can solve virtually any task involving processing language.
Keywords 2: OpenAI, language models, text processing, API.
Text 3: {text}
Keywords 3:Reducing Ambiguity
Avoid vague and imprecise descriptions. Instead, provide clear and concise instructions.
Example:
Use a 3 to 5 sentence paragraph to describe this product.Image Models like DALL-E
Prompt engineering for image models like DALL-E involves crafting prompts that precisely describe the desired image. Here are some effective techniques:
1. Specific Descriptions: Clearly describe the objects, their relative sizes, colors, and actions in the image.
Example:
Generate an image of a red apple on a wooden table with a glass of water next to it.2. Thematic Prompts: Use themes if the details are unknown. This helps in guiding the model towards a conceptual idea.
Example:
Generate an image of a multicultural group in a bright, open office space, engaging in an energetic work discussion.3. Size and Resolution: Specify the size and resolution of the image if necessary.
Example:
Generate an image of a beach scene in a 1920x1080 resolution.4. Iterative Refinement: Use iterative prompts to refine the generated images. If the initial result is not satisfactory, adjust the prompt and regenerate.
Example:
Initial Prompt: Generate an image of a bat.
Refined Prompt: Generate an image of a bat with wings spread, flying in a night sky.5. Avoiding Unwanted Elements: Explicitly state what should not be included in the image to avoid unwanted elements.
Example:
Generate an image of a serene forest scene without any animals or humans.Express Your Query as Clearly as Possible
Crafting effective prompts for generative AI requires precision and clarity. Here are some best practices:
Experimentation is crucial to refining prompt engineering techniques. Here are some strategies:
Effective prompt engineering often involves follow-up instructions or questions to fine-tune the AI’s output:
Prompt engineering is used in the NLP domain to enhance the capabilities of LMs without training the parameters. By designing specific prompts, engineers can guide LLMs to perform various tasks such as text classification, sentiment analysis, and question-answering. For instance, in the study on job type classification, prompt engineering enabled a zero-shot GPT-3.5 model to outperform traditional supervised approaches by 6% in Precision@95% Recall. Additionally, prompt engineering has been used to mitigate biases in NLP tasks, as seen in the privacy-preserving prompt engineering survey, which highlights techniques to address privacy risks in in-context learning.
Prompt engineering has helped address challenges like few-shot learning and demographic biases for image recognition tasks. The Semantic Prompt (SP) approach leverages semantic information to tune visual feature extraction networks, significantly improving average few-shot learning accuracy by 3.67%. Another notable application is PreciseDebias, which uses prompt engineering to rectify demographic biases in image generation. By transforming generic text prompts into specific instructions, PreciseDebias produces images that accurately reflect specified demographic distributions, demonstrating robustness and effectiveness in generating diverse and balanced images.
Prompt engineering also plays an important role in predictive analysis to enhance the performance of models in tasks such as automated program repair and requirement classification. For example, in automated program repair, prompt engineering combined with fine-tuning of pre-trained models like PLBART and CodeT5 has shown notable improvements in transforming erroneous programs into correct ones. Similarly, in requirement classification, the PRCBERT approach uses flexible, prompt templates to accurately classify, outperforming previous models on small and large-scale datasets.
Engineers can use advanced prompt engineering techniques to unlock the full potential of generative AI across various domains, ensuring more accurate, relevant, and unbiased outputs.
Prompt engineering provides enhanced control over generative AI outputs by allowing engineers to specify the desired format, style, and content. This precision ensures that the AI-generated responses align closely with user expectations and requirements, thereby improving the overall utility and relevance of the AI system.
By carefully crafting prompts, engineers can significantly improve the relevance of AI outputs. This is useful in applications such as paraphrase generation and sentiment analysis, where the specificity of prompts can lead to more contextually appropriate and accurate results. For example, using engineered prompts in chatbots has enhanced paraphrase quality by ensuring they are contextually relevant and semantically equivalent.
While prompt engineering aims to streamline the generation of high-quality outputs, it often requires ongoing refinement and manual corrections. Aporia’s Guardrails offers a more comprehensive solution by eliminating the need for these constant adjustments. Providing out-of-the-box and custom Guardrails that work at sub-second latency ensures that user prompts and LLm responses remain secure and reliable without requiring changes to the base prompt or manual interventions.
The versatility of prompt engineering allows it to be applied across various domains, from NLP and image recognition to healthcare and predictive analysis. This adaptability makes it a valuable tool for enhancing the performance of AI systems.
Prompt engineering enables the customization of AI outputs to meet specific user needs and preferences. By tailoring prompts to particular tasks or user requirements, engineers can ensure that the AI system delivers outputs that are accurate and tailored to the unique context of the application.
The effectiveness of prompt engineering heavily relies on the prompts’ quality. Poorly designed prompts can lead to irrelevant or inaccurate outputs, necessitating expertise, and iterative refinement to achieve optimal results. This reliance on prompt quality can be a significant barrier for less experienced users.
Prompt engineering often requires domain-specific knowledge to craft effective prompts. This limitation can hinder the generalizability of prompts across different domains, as prompts that work well in one context may not be effective in another. For instance, prompts designed for healthcare applications may not be suitable for financial analysis.
There is a risk of introducing or perpetuating biases through prompt engineering. If the prompts are not carefully designed to be neutral and inclusive, they can lead to biased outputs, which can have serious ethical implications. Research has shown that prompt engineering can mitigate and exacerbate biases in AI systems, depending on how it is applied.
The process of prompt engineering can be complex and time-consuming, often requiring multiple iterations to refine prompts for optimal performance. This iterative process can be resource-intensive, demanding significant time and effort from engineers to achieve the desired outcomes.
While prompt engineering provides enhanced control over AI outputs, it does not offer complete control. The inherent unpredictability of generative AI models means that even well-crafted prompts can sometimes produce unexpected or undesired results. This limitation stresses the need for continuous monitoring and adjustment of prompts to maintain output quality.
Aporia’s Guardrails provide a robust solution to these limitations, restoring control to users within minutes:
This approach enables teams to surmount the limitations of traditional prompt engineering. It facilitates the production of high-quality, controlled AI outputs without necessitating constant manual adjustments or extensive domain expertise.
Generative AI is expected to significantly impact the workforce by changing job dynamics and creating new opportunities while potentially displacing certain roles. Studies indicate that approximately 32.8% of occupations could be fully impacted by AI technologies like ChatGPT, with another 36.5% experiencing partial effects.
Generative AI can enhance productivity by automating routine tasks, enabling workers to focus on more complex and creative activities. However, it also poses challenges, such as the need for reskilling and the potential for job displacement in sectors heavily reliant on repetitive tasks.
Generative AI is expected to continue reshaping the workforce, with significant shifts in the occupational mix and the creation of new job roles related to AI model management and customization. Organizations must adopt a proactive approach to harness the potential of Gen AI, focusing on human-centric progress and ensuring that technology serves as a tool for human empowerment.
The overall effect on the labor market will depend on how quickly industries adapt and integrate these technologies.
An AI Prompt Engineer designs and refines prompts to guide the outputs of large language models (LLMs).
A prompt engineer creates and optimizes prompts to ensure LLMs generate accurate, relevant, and contextually appropriate responses.
Skills needed include a deep understanding of LLMs, domain-specific knowledge, strong linguistic abilities, and iterative testing and refinement techniques.
Prompt engineering does not necessarily require coding, but familiarity with programming can enhance the ability to integrate and automate prompt-based solutions.
Prompt engineering enhances AI performance by providing clear, specific instructions that guide the model to generate more accurate and relevant outputs.
Yes, prompt engineering can help mitigate biases by carefully crafting prompts to be neutral and inclusive, thus reducing the risk of biased outputs.
Common challenges include ensuring prompt quality, addressing domain specificity, managing potential biases, and dealing with the complexity and iterative nature of the process.
Prompt engineering is applied across various domains, such as NLP, image recognition, and predictive analysis, enhancing the relevance and accuracy of AI outputs in each field.
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