An introduction to LLM Prompt Engineering

Note: This article was written with the assistance of OpenAI’s GPT-4. All Prompts were engineered by the author. All final editing was by the author.

Abstract

Large language models, such as GPT-4, are revolutionising natural language processing. However, their effectiveness and accuracy depend on well-crafted prompts that provide clarity, specificity, and sufficient context. Users should experiment with phrasing, break down complex questions, and stay informed of best practices. They should be aware of potential biases, iterate and refine prompts, and evaluate the performance of LLMs to ensure accurate and ethical outputs. Incorporating feedback from end-users and domain experts can lead to more refined and valuable applications. By adopting a responsible and iterative approach to prompt engineering, users can unlock the full potential of large language models and pave the way for innovative AI-driven solutions.

Executive Summary

Large Language Models (LLMs) generate text based on learned patterns from training data and predict the most likely next token based on context. They are not just advanced search engines, as they offer creative and contextually aware responses. However, the accuracy of the generated content may sometimes be sacrificed for the sake of fluency and coherence. Good prompt engineering can help to guide LLMs towards producing accurate and relevant responses by providing clear and sufficient context.

LLMs such as GPT have a vast knowledge base and are capable of generating coherent and creative text, making them useful for various tasks. However, they have limitations such as being limited to their training data, potentially generating irrelevant or incorrect information, and being prone to biases. To design effective prompts, one needs to provide clear and unambiguous language, context and background information, break down complex questions, experiment with different phrasings, and monitor generated content for accuracy and biases. One should continuously iterate and improve prompts based on the model's performance and stay informed about the latest research and best practices in prompt engineering and NLP.

Crafting clear, specific, and unambiguous prompts, and including context and information, is important to obtain accurate and relevant responses from large language models. Specific prompts reduce ambiguity, mitigate the model's limitations, and exploit its capabilities more effectively. Including context helps the model understand the topic and disambiguate terms or concepts that may have multiple meanings. Relevant information anchors the model's response to the specific context provided, leading to more focused and detailed answers. Providing context and information encourages the model to generate more coherent and logically consistent responses.

Experimenting with different phrasings and being cautious of potential biases can improve the effectiveness of prompts for obtaining accurate, relevant, and unbiased information from a LLM. Users can try different perspectives, rephrasing questions, and providing explicit instructions to identify the most effective way to obtain the desired information. It is important to iterate and refine prompts based on the model's responses, be cautious of potential biases, and avoid leading questions or biased language. Encouraging balanced responses and validating information can also help ensure accuracy. Users should also keep themselves informed about the known limitations and biases of large language models to recognize and mitigate any issues that may arise in the generated responses.

Utilising step-by-step or conversation-style prompts can improve the performance of a language model by breaking down complex questions or tasks into smaller, simpler components, and engaging the model in a back-and-forth dialogue. Users can refine their prompts to improve the model's understanding and obtain more accurate and detailed responses. These prompts help the model to process information more effectively, generate coherent responses, address misunderstandings or errors, and provide more detailed and accurate information. Using step-by-step and conversation-style prompts supports incremental problem-solving and allows the user to tackle complex issues one step at a time.

Regularly evaluating the performance of prompts and fine-tuning context and domain-specific data can improve the performance of LLMs for specific tasks or industries, ensuring accurate, relevant, and valuable outputs that meet user needs. Users should analyse generated responses and define metrics for evaluating response quality. Establishing feedback processes from end-users or domain experts can help track the model's performance over time and identify potential issues, changes in output quality, or opportunities for improvement.

To promote responsible usage and ensure ethical interactions with LLMs, users should be aware of ethical considerations such as potential biases, privacy concerns, and risks of generating harmful or misleading content. Users should regularly evaluate the model's responses for potential biases or stereotypes, avoid using sensitive information in prompts, and test the model for harmful or misleading content. Additionally, it's important to ensure the use of AI models is accountable and transparent, and to design prompts that promote positive and responsible use of the model.

Prompt Engineering?

An LLM (Large Language Model) prompt is an input text or query given to a language model, such as GPT-4, to guide the model in generating a desired output or response. The prompt serves as a starting point or context for the model to produce coherent, relevant, and accurate text completions based on its learned patterns and associations. 

Prompt engineering for Large Language Models (LLMs) involves designing effective inputs to guide the model towards producing more accurate and useful outputs.

It is important, when thinking of LLMs, to understand that these are not just more advanced search engines. A  search engine retrieves text or information from specific web pages or documents on the internet, based on user queries. It does not generate text but rather directs users to existing content. An LLM generates completions based on learned patterns from its training data, offering creative and contextually aware responses. 

LLMs generate text by predicting the most likely next token based on the given context. Sometimes, the model may generate a sequence of tokens that seems coherent but is not accurate or relevant to the prompt. LLMs are optimised to generate fluent and coherent text, which may sometimes take precedence over the accuracy of the generated content. This can result in outputs that are well-written but factually incorrect or unrelated to the prompt.

We can help here with good prompt engineering. If a prompt is vague, ambiguous, or lacks sufficient context, the model may struggle to generate a relevant response and might end up "hallucinating" plausible-sounding but incorrect information. Great prompts will give the model the best chance at generating great responses.

Model capabilities and limitations

LLMs like the GPT solutions are powerful language models with several strengths and weaknesses. Understanding these characteristics can help you design effective prompts that maximise strengths and minimise limitations of the model that you are working with.

One of the greatest strengths of these LLMs is that they have a vast knowledge base. They are trained on a huge amount of diverse text, giving them a vast knowledge base on various subjects. This training allows LLMs to demonstrate strong capabilities in understanding the context and nuances of language. This includes grammar, idiomatic expressions, and semantic relationships. 

Because the output is generative, and because the training data is diverse, LLMs can generate what looks like creative and coherent text, which makes them suitable for tasks like content generation, storytelling, and brainstorming ideas. This vast training data also makes them adaptable to a wide range of tasks.

They do have some weaknesses though. For example, the LLMs knowledge is limited to its training data. GPT-4 has a cut-off date in September 2021. This means it knows nothing about recent events, updates, or developments.

LLMs may also struggle with ambiguous or poorly-defined prompts and may provide irrelevant or incorrect information as a result. The better we structure our prompts, the less likely this is to be an issue.

LLMs can over-optimise for coherence and can therefore be excessively verbose. They may prioritise coherence over correctness, leading to plausible-sounding but incorrect or nonsensical answers. So called “hallucination”. They can also sometimes provide inconsistent responses or change their "opinion" when asked the same question multiple times or with slightly different phrasings.

Finally, although the developers try hard to work against it, the LLM can inadvertently generate biased, offensive, or politically-charged content. This is due to biases present in its training data.

So, to design prompts that exploit the model's strengths and mitigate its limitations, we need to learn to use specific, clear, and unambiguous language to reduce the chance of obtaining irrelevant or incorrect answers. We should provide context and background information to help the model understand the topic and desired output better. We need to think about breaking complex questions into smaller, simpler parts, or make use of a conversation-style approach to guide the model towards more accurate and detailed answers.

It is important to experiment with different phrasings or instructions to find the most effective prompt for the desired output. It is equally important to monitor and filter the generated content to ensure it is free from biases, inaccuracies, or inappropriate language. Remember, that if you put something out into the World that has been generated by an LLM, then you are taking responsibility for those outputs.

There is no failure, only feedback, so continuously iterate and improve prompts based on the model's performance and stay informed about the latest research and best practices in prompt engineering and NLP.

Prompt specificity and context

Crafting clear, specific, and unambiguous prompts, and including context and information, helps guide the model towards generating accurate and relevant responses while maximising its capabilities and mitigating its limitations.

Prompt specificity, being clear, specific, and unambiguous, plays a crucial role in obtaining accurate and relevant responses from large language models. Crafting these clear, specific, and unambiguous prompts is important.

A specific and unambiguous prompt helps guide the model by explicitly stating the desired output. This reduces the chances of the model generating irrelevant or off-topic responses and increases the likelihood of obtaining accurate and relevant answers.

We know that LLMs try to predict the most likely continuation of a given input (remember this is not Search!). When the prompt is vague or ambiguous, the model may have to make a best guess about your intention. There is a good probability that this could lead to incorrect or less relevant responses. A specific prompt reduces the need for guesswork and enables the model to focus on generating the desired output.

As discussed earlier, by providing a clear and specific prompt, you can better mitigate the model's limitations and increase the chances of receiving an accurate response. LLMs have a vast knowledge base and can handle a wide range of tasks. A specific and unambiguous prompt enables you to exploit the model's capabilities more effectively and get the most out of its potential.

We can also add specificity and reduce ambiguity by Including context and information in the prompt. Providing context helps the model better understand the topic, especially in cases where the subject matter is complex or requires domain-specific knowledge. This can lead to more accurate and relevant responses.

If there is any risk of ambiguity, then including context can help disambiguate terms or concepts that may have multiple meanings or interpretations. This ensures that the model understands your intended meaning and provides a more appropriate response.

Including relevant information in the prompt helps anchor the model's response to the specific context you provide. This can lead to more focused and detailed answers that are better suited to your needs.

By providing context and information, you can encourage the model to generate more coherent and logically consistent responses that build upon the context given in the prompt.

Experimenting with phrasing

By experimenting with different phrasings and being cautious of potential biases, users can improve the effectiveness of their prompts and obtain more accurate, relevant, and unbiased information from a LLM.

Instruction phrasing refers to the way a prompt or question is framed and articulated when interacting with a language model. The choice of words, structure, and tone can significantly impact the model's response. Experimenting with different phrasings can help users identify the most effective way to obtain accurate, relevant, and useful information from the model.

There are a number of approaches when experimenting with different phrasings. For example, trying different perspectives. If you frame the prompt from various angles or perspectives you can explore how the model responds. This can help identify the most effective way to obtain the desired information.

Another option is to rephrase questions. By altering the structure, tone, or focus of the question you can see how it influences the response. This can provide insights into the phrasing that generates the most accurate and helpful answers.

You can also experiment with providing more explicit instructions, specifying the format or desired outcome of the response. For example, you could ask the model to provide a step-by-step explanation or a pros-and-cons list.

Also, you can test phrasings that instruct the model to provide concise, detailed, or comprehensive responses, depending on your needs.

As with any experiment, it is critical to iterate and refine. Analyze the model's responses to different phrasings, learn from the results, and refine your prompts accordingly.

It is important to be cautious of potential biases in the model and avoid leading questions. Always strive to be objective and phrase your prompts in a neutral and objective manner, avoiding biased language or assumptions that might influence the model's response. Avoid using questions that contain assumptions, exaggerations, or biases, as these can lead to misleading or biased responses.

It is possible to encourage balanced responses. By Instructing the model to consider multiple perspectives, counter-arguments, or sources to encourage a more balanced and well-rounded response.

I would also recommend that you validate information on a regular basis. If you can, cross-check the information provided by the model with other reliable sources to ensure its accuracy. It is always good to keep yourself informed about the known limitations and biases of large language models, so you can recognize and mitigate any issues that may arise in the generated responses.

The conversation-style approach

By utilising step-by-step or conversation-style prompts, users can improve the model's understanding, provide more detailed responses, and guide the model towards more accurate and relevant outcomes.

Step-by-step or conversation-style prompts involve breaking down complex questions or tasks into smaller, simpler components, or engaging the model in a back-and-forth dialogue. Users can make use of these approaches to enhance the model's performance and obtain more accurate and detailed responses.

One way to do this is to break down complex questions. Divide a complex question into several smaller, more manageable parts. This allows the model to focus on each aspect of the question separately, improving the chances of obtaining accurate answers.

If it is a multi-step task or problem, provide the model with a sequence of instructions or questions that gradually build on each other. This approach helps guide the model through the task in a more structured manner.

You should frame your prompts in a conversational style, with a series of questions and answers that build on each other. This can help maintain context throughout the interaction and guide the model towards more coherent and accurate responses.

Conversational style also means being able to request clarification or elaboration. If the model provides an unclear or incomplete response, ask for clarification or elaboration to obtain more detailed information.

As with experiments, it is important to iterate and refine based on the model's responses. Refine your prompts or questions to improve the model's understanding and performance.

This step-by-step or conversation-style prompts has several advantages. For example, breaking complex questions into simpler steps or using a conversation-style approach allows the model to process information more effectively, leading to a better understanding of the context and requirements.

Engaging in a dialogue or step-by-step process helps maintain context throughout the interaction. This enables the model to generate more coherent and logically consistent responses that are better aligned with the user's needs.

Breaking down complex tasks or using a conversation-style approach can help identify and address misunderstandings or errors early in the process, reducing the likelihood of inaccurate or irrelevant responses. Focusing on smaller, simpler components of a question or task allows the model to provide more detailed and accurate information, resulting in higher-quality responses.

Finally, step-by-step and conversation-style prompts support incremental problem-solving, making it easier to tackle complex or multifaceted issues by addressing them one step at a time.

Prompt evaluation and fine tuning

By regularly evaluating the performance of their prompts and fine-tuning the contexts and domain-specific data, users can improve the model's performance for specific tasks or industries, ensuring more accurate, relevant, and valuable outputs that meet their unique needs and requirements.

Evaluation and fine-tuning are essential aspects of working with LLMs to ensure optimal performance and alignment with specific tasks or industries. 

Users should regularly evaluate the performance of their prompts by analysing the generated responses for accuracy, relevance, coherence, and detail. This helps identify areas where improvements can be made and ensures that the model's outputs continue to meet their needs and expectations.

It is good to define metrics for evaluating the quality of the generated responses, such as correctness, completeness, and readability. This allows for a systematic and consistent assessment of the model's performance.

As a matter of course, a process should be established to obtain feedback from end-users or domain experts to better understand the effectiveness of the prompts and the value of the generated outputs. Combine this feedback with the established metrics to track the model's performance over time and identify potential issues, changes in the quality of the outputs, or opportunities for further improvement.

Ethical considerations 

By being aware of ethical considerations and designing prompts that minimise risks, users can promote responsible usage and ensure that their interactions with LLMs are ethical, safe, and aligned with their values and objectives.

Users should be aware of several ethical considerations when working with LLMs, as these models can have potential biases, privacy concerns, and risks of generating harmful or misleading content. 

Bias is an important ethical consideration. Because language models are trained on vast amounts of data from the internet, which may contain biases or stereotypes, the model may unintentionally perpetuate these biases in its generated outputs. It is therefore critical to regularly evaluate the model's responses to identify any potential biases or stereotypes, and refine prompts to mitigate these issues. Following the advice here to craft prompts that are clear, specific, and neutral can minimise the chances of biased responses.

Since language models are trained on public data, they may inadvertently memorise or reproduce sensitive information, such as personal details or confidential data. You yourself should avoid the risk of information leakage by never using performance identifiable information (PII) or confidential or sensitive data in your prompts. Regularly check the model's outputs to ensure that no sensitive information is being disclosed inadvertently.

Language models may generate content that is offensive, harmful, or misleading, which can have negative consequences for users and society. Test the model with various prompts to understand its behaviour and identify potential risks of generating harmful or misleading content. Use techniques like content filters, moderation, or custom fine-tuning to minimise the risk of generating harmful or misleading outputs.

Ensuring that the use of AI models is accountable and transparent is essential to maintain trust and ethical standards. You should inform users when they are interacting with an AI model and provide relevant information about its capabilities and limitations. Keep records of the AI model's decisions and actions to maintain accountability and facilitate the identification and correction of any issues. 

Finally, be sure to design prompts that promote positive, constructive, and responsible use of the model and its capabilities.

Conclusion

In conclusion, large language models, like GPT-4, have revolutionised the field of natural language processing and opened up new possibilities for AI-generated text. However, their effectiveness and accuracy depend heavily on well-crafted prompts that provide sufficient context, clarity, and specificity. 

By designing effective prompts, breaking down complex questions, experimenting with phrasings, and staying informed about the latest research and best practices, users can harness the power of these models more effectively and responsibly. 

It is essential to continuously iterate and refine prompts, be aware of potential biases, and evaluate the performance of LLMs to ensure accurate, relevant, and ethical outputs. Furthermore, incorporating feedback from end-users and domain experts can lead to more refined and valuable applications of LLMs across various tasks and industries. 

By adopting a responsible and iterative approach to prompt engineering, users can unlock the full potential of large language models and pave the way for innovative and impactful AI-driven solutions.