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10 Prompt engineering techniques to improve your results

10 Prompt engineering techniques to improve your results

Meta previously known as Facebook and headed by Mark Zuckerberg a leading tech company, has published a detailed guide on prompt engineering. This guide is designed to help users, from developers to AI enthusiasts, get the most out of their interactions with advanced language models, such as Meta’s own LLaMA 2, ChatGPT, Bard and others. By applying the strategies outlined in the guide, users can significantly improve the quality and relevance of the results and responses they receive from these AI systems.

The guide introduces seven key prompting techniques that can greatly improve the performance of language models. The first technique, known as Explicit Instructions, focuses on the importance of providing clear and detailed prompts. This allows users to direct the model to produce outputs that adhere to specific guidelines, such as using only the most recent sources for information. This level of specificity ensures that the information provided by the AI is both accurate and up-to-date.

Another technique, Zero-Shot Prompting, challenges the model to respond to queries without any prior examples. This tests the model’s inherent ability to understand and respond to prompts, providing a quick measure of its capabilities. Conversely, Few-Shot Prompting gives the model a few examples to illustrate the type of output desired. This context helps the model to better understand the task at hand and deliver more precise and relevant responses.

Meta AI prompt engineering guide overview

Role Prompting is a technique where the model is assigned a specific role, such as a tutor or a journalist. This helps to guide the model’s responses within a certain context, leading to more consistent and appropriate outputs. Chain of Thought Prompting, on the other hand, encourages the model to outline its reasoning process. This can be particularly useful for tackling complex reasoning tasks, as it breaks down the problem into simpler steps that the model can more easily navigate.

Here are some other articles you may find of interest on the subject of writing prompts for AI models.

The Self-Consistency technique involves asking the model to come up with multiple answers and then select the most coherent or accurate one. This can enhance the reliability of the model’s responses. Lastly, Retrieval-Augmented Generation (RAG) prompts the model to incorporate external information into its responses, which is crucial for tasks that require current and factual data.

Meta’s guide is a significant step forward in making sophisticated language models more accessible and effective for a broad audience. By utilizing these prompt engineering techniques, users can expect interactions with AI systems that are more advanced, precise, and aware of the context. This guide is not just a manual; it’s a bridge between human users and the complex algorithms that drive language models, ensuring that communication is as seamless and productive as possible.

The implications of Meta’s guide extend beyond just improving user experience. It also opens up new possibilities for how AI can be used in various fields. For instance, in education, AI models can be tailored to act as personalized tutors, providing students with explanations and information that cater to their individual learning styles. In journalism, AI can assist reporters by quickly gathering and synthesizing information from a range of sources, enabling them to craft well-informed stories.

Moreover, the guide’s emphasis on techniques like Chain of Thought and Self-Consistency Prompting can lead to AI models that are not only more responsive but also more transparent in their decision-making processes. This transparency is crucial for building trust between users and AI systems, particularly in areas where accountability and accuracy are paramount.

As AI continues to integrate into our daily lives, the ability to fine-tune interactions with these systems becomes more critical. Meta’s guide to prompt engineering is a valuable resource for anyone looking to harness the power of AI. It provides a clear framework for improving communication with language models, ensuring that users can get the most out of these technologies.

An overview of Meta AI’s Prompt Engineering Guide

The release of this guide is timely, as the use of AI in various sectors is growing at an unprecedented rate. By sharing their expertise in prompt engineering, Meta is not only enhancing the user experience but also contributing to the broader field of AI research and development. As language models become more advanced, the insights from this guide will likely play a crucial role in shaping the future of human-AI interaction.

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1. Explicit Instructions

Explicit instructions are about providing precise and clear directives to the model. This technique goes beyond merely asking a question or requesting information; it involves defining the scope, context, and sometimes the structure of the desired response. For example, if you need an explanation of quantum physics tailored for a non-scientific audience, instead of asking “What is quantum physics?” you might say, “Explain quantum physics in simple terms, using no more than two sentences without technical jargon.” Here, you’re not only specifying the complexity level but also limiting the length and forbidding technical terms.


  • Reduced Ambiguity: By clearly stating what you want, you minimize the chances of receiving irrelevant or overly complex answers.
  • Enhanced Precision: The model’s responses are more likely to directly address the query, incorporating the specified limitations or directives.
  • Improved Efficiency: Time is saved both for the user, who receives a more targeted response, and potentially for the model, which can generate an answer without exploring irrelevant details.

2. Stylization

Stylization in prompts enables the model to adopt a specific voice or tone, which can be particularly useful in educational settings, content creation, or simply to make information more relatable. For instance, asking the model to explain a concept “like you’re a friendly scientist speaking to high school students” or “as if you’re a detective uncovering the mysteries of the universe” can make the explanation more engaging and memorable. This approach not only brings a creative aspect to the interaction but also tailors the content’s complexity and tone to the audience’s needs.


  • Increased Engagement: A stylized response can capture and hold the audience’s attention more effectively than a straightforward explanation.
  • Accessibility: Complex information becomes more digestible when presented in a familiar or entertaining context.
  • Versatility: This approach allows for a wide range of applications, from educational materials to creative writing, marketing content, and beyond.

3. Formatting

Formatting involves structuring the model’s output in a specific way, which can be crucial for clarity, especially when dealing with data, instructions, or multi-part information. For example, asking for a response in bullet points can make a list of instructions or features easier to follow, while requesting a JSON object might be essential for integrating the model’s response into a software application. By specifying the desired format, users can ensure that the model’s output is immediately usable or requires minimal post-processing.


  • Clarity and Organization: Information is easier to scan and understand when it’s well-organized, whether in lists, tables, or structured data formats.
  • Ease of Integration: For technical applications, receiving data in a specific format (like JSON) can significantly reduce the effort required to use this information in programming contexts.
  • Customization for End Use: Whether the information is intended for a report, a database, or to be displayed on a website, formatting requests help ensure that the model’s output aligns with the end goal.

4. Restrictions

Restrictions in prompting guide the model to adhere to certain parameters when generating responses. This can be particularly useful when the user needs information that is contemporary, specific, or complies with certain standards. For instance, if a user is interested in the latest research on artificial intelligence, they might specify that the model should base its response on articles or papers published within the last two years. Similarly, restrictions can be used to avoid sensitive topics or ensure that the content is suitable for all audiences.


  • Relevance and Timeliness: Ensuring that the information provided by the model is up-to-date and relevant to current contexts or standards.
  • Content Appropriateness: Helping to maintain the suitability of the content for specific audiences or purposes by avoiding unwanted or sensitive topics.
  • Focused Information: Narrowing down the scope of the model’s responses to fit specific research or professional needs, enhancing the utility of the information provided.

5. Zero- and Few-Shot Learning

Zero-shot and few-shot learning are techniques that allow models to perform tasks without or with minimal prior specific examples, respectively. Zero-shot learning enables the model to understand and execute a task it hasn’t been explicitly trained on, based on its general understanding and capabilities. Few-shot learning, on the other hand, provides the model with a small number of examples to guide its responses, improving its ability to generate accurate and relevant outputs in a new context.


  • Adaptability: These techniques enhance the model’s ability to adapt to new tasks and formats without the need for extensive retraining or specific data.
  • Efficiency: They allow for rapid deployment of the model in diverse applications, making it a versatile tool for a wide range of tasks.
  • Improved Accuracy and Consistency: By providing examples in few-shot learning, users can guide the model towards more accurate and consistent outputs that align with their expectations.

6. Role Prompting

Role prompting involves assigning a specific persona or expertise level to the model for the duration of the interaction. This could range from being a friendly advisor on financial matters to a technical expert in machine learning. By defining a role, the user sets expectations for the type of language, level of detail, and the perspective the model should use in its responses. This is particularly effective for obtaining nuanced and specialized information that requires a deep understanding of a subject area.


  • Enhanced Depth and Authority: Responses are more likely to reflect a deeper level of understanding and authority on the subject, tailored to the assigned role.
  • Contextualization: Role prompting helps the model contextualize its responses according to the assumed persona, making its outputs more relevant and specific to the user’s needs.
  • User Experience: It creates a more engaging and personalized interaction, as the model adopts a consistent and appropriate tone and perspective throughout the conversation.

7. Chain-of-Thought (CoT) Prompting

Chain-of-Thought prompting is a technique that encourages the model to unpack its reasoning process in a sequential, step-by-step manner. This method is particularly useful for complex problem-solving or when the logic behind an answer is as important as the answer itself. For example, in solving a math problem or explaining the causes of a historical event, CoT prompting can lead the model to present each logical step that leads to the final conclusion. This not only aids in understanding the model’s thought process but also helps in identifying and correcting errors in reasoning.


  • Improved Problem Solving: By breaking down complex tasks into simpler, sequential steps, the model can more effectively navigate through the reasoning required to reach a solution.
  • Increased Transparency: CoT prompting offers users insight into how the model arrives at its conclusions, making it easier to trust and verify the accuracy of the response.
  • Educational Value: This approach has significant educational applications, as it mirrors teaching methods that encourage students to show their work, thereby enhancing understanding and retention.

8. Self-Consistency

Self-consistency involves generating multiple responses to the same prompt and then selecting the most common or coherent answer among them. This method leverages the probabilistic nature of LLMs to mitigate the likelihood of errors. By comparing several attempts at answering a question, the model can identify and converge on the most reliable response. This technique is especially useful for questions where precision and reliability are critical, such as in factual verification, complex reasoning, or when providing advice or recommendations.


  • Enhanced Accuracy: Aggregating multiple responses to select the most frequent answer helps to ensure that the information provided is more likely to be correct.
  • Reduction of Anomalies: This method helps to filter out outlier responses or errors that might occur due to the probabilistic generation process.
  • Improved Reliability: For applications where accuracy is paramount, self-consistency can be a vital tool to increase the trustworthiness of the model’s outputs.

9. Retrieval-Augmented Generation (RAG)

Retrieval-Augmented Generation combines the generative capabilities of LLMs with the retrieval of external data to enhance the accuracy and relevance of responses. This technique is particularly valuable for questions requiring specific, up-to-date, or detailed factual information that may not be within the model’s pre-existing knowledge base. By accessing and incorporating external sources, the model can provide responses that are not only based on its training data but also reflect the most current information available or specific details from authoritative sources.


  • Access to Up-to-Date Information: RAG enables models to supplement their knowledge with the latest information from external sources, overcoming the limitations of their training data.
  • Increased Factual Accuracy: By retrieving data from reliable external databases or sources, the model can provide more accurate and specific answers.
  • Enhanced Versatility: This approach broadens the range of questions the model can address effectively, including those that require specialized knowledge or recent data.

10. Program-Aided Language Models (PAL)

Program-Aided Language Models (PAL) enhance the capabilities of traditional Large Language Models (LLMs) by integrating the ability to generate, understand, and utilize programming code within their responses. This approach leverages the model’s proficiency in natural language understanding and combines it with code generation to solve problems that require computational processes, such as arithmetic operations, data analysis, and algorithmic problem solving. Essentially, when faced with a task that involves complex calculations or data manipulation, the PAL technique instructs the model to formulate the problem in code, execute it (if the environment permits), and interpret the results back into human-readable form.

How It Works:

  • Prompting for Code Generation: Users can ask the model to solve a problem that involves computation by explicitly requesting it to generate the corresponding code. For example, a prompt might ask the model to write a Python script to analyze a dataset or perform a mathematical calculation.
  • Execution of Code: In environments where code execution is supported, the model can run the generated code to obtain results. This step is crucial for ensuring the accuracy of computational tasks.
  • Interpreting Results: The model then interprets the output of the executed code, translating it into a concise, understandable response for the user.


  • Expanded Problem-Solving Capabilities: PAL significantly broadens the scope of tasks LLMs can assist with, making them valuable tools for technical and scientific problem-solving that involves computation.
  • Precision and Reliability: By leveraging programming languages known for their precision in computation, PAL can provide exact numerical or data-driven answers, reducing the margin of error associated with purely text-based reasoning.
  • Automation of Routine Tasks: PAL can automate certain types of analytical and data-processing tasks, saving time and reducing the potential for human error.
  • Educational Applications: This technique can also serve an educational purpose by demonstrating how to approach problem-solving through programming, offering code examples and explanations for complex problems.

Applications: PAL is particularly useful in fields such as data science, engineering, and finance, where tasks often involve data analysis, statistical calculations, and algorithmic logic. It can assist in automating repetitive tasks, providing quick answers to computational questions, and even generating code snippets that users can learn from or integrate into larger projects.

Meta AI’s full prompt engineering guide can be found over on the GitHub website and offers more than just a set of instructions; Whether you’re a seasoned developer or simply curious about the potential of AI, this guide offers valuable insights into how to communicate effectively with some of the most sophisticated AI systems available today. With these techniques at their disposal, users can look forward to a new era of AI interactions that are not only efficient but also deeply aligned with their specific needs and goals.

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