Prompt Engineering
Creating and editing content - Md. Tariqul Islam
What is prompt engineering?
Generative artificial intelligence (AI) systems are designed to generate specific outputs based on the quality of provided prompts. Prompt engineering helps generative AI models better comprehend and respond to a wide range of queries, from the simple to the highly technical.
Prompt engineering is the process of refining prompts that a person can input into a generative artificial intelligence (AI) service to create text or images. Anyone can do this using natural language in generators like ChatGPT or DALL-E. It's also a technique that AI engineers use when refining large language models (LLMs) with specific or recommended prompts.
For example, if you're using ChatGPT to brainstorm a professional summary while refining your resume, you might write a command like, “Write a sample professional summary for a marketing analyst.” You might prompt ChatGPT’s response with feedback like “too formal” or “shorten to less than 100 words.” You can do the same for text-to-image models like DALL-E.
Details: IBM
Prompting Progress: We'll All Speak AI In The Future Workforce
Robin Li, the co-founder and CEO of Chinese AI giant, Baidu, declared in January that “in ten years, half of the world's jobs will be in prompt engineering, and those who cannot write prompts will be obsolete.”
I want to get my Job Done With less Input and More Output
Our Prompt is Like the Confusing WiFi Password 😄😄😄
Prompt engineering examples
Here are a few examples of prompt engineering to give you a better understanding of what it is and how you might engineer a prompt with a text and image model.
For text models like ChatGPT:
What’s the difference between a professional summary and an executive summary?
Write a professional summary for a marketing analyst looking for a marketing manager job.
Now trim it down to less than 60 words.
Rewrite it with a less formal tone.
For image models like DALL-E:
A painting of a cat.
A painting of a cat chasing a mouse in Impressionist style.
Now use only warm tones in the painting.
The 6 Prompt Components
Task
Context
Exemplars
Persona
Format
Tone
Context Task (Task is main)
I am 70kg male. Give me a 3-month training program.
Putting more Information in Context:
Im a 70kg male looking to put on 5 kilograms of muscle mass over the next 3 months.
I only have time to go to the gym twice a week, and for 1 hour each session.
Six strategies for getting better results
Write clear instructions
These models can’t read your mind. If outputs are too long, ask for brief replies. If outputs are too simple, ask for expert-level writing. If you dislike the format, demonstrate the format you’d like to see. The less the model has to guess at what you want, the more likely you’ll get it.
Tactics:
Provide reference text
Language models can confidently invent fake answers, especially when asked about esoteric topics or for citations and URLs. In the same way that a sheet of notes can help a student do better on a test, providing reference text to these models can help in answering with fewer fabrications.
Tactics:
Split complex tasks into simpler subtasks
Just as it is good practice in software engineering to decompose a complex system into a set of modular components, the same is true of tasks submitted to a language model. Complex tasks tend to have higher error rates than simpler tasks. Furthermore, complex tasks can often be re-defined as a workflow of simpler tasks in which the outputs of earlier tasks are used to construct the inputs to later tasks.
Tactics:
Use intent classification to identify the most relevant instructions for a user query
Summarize long documents piecewise and construct a full summary recursively
Give the model time to "think"
If asked to multiply 17 by 28, you might not know it instantly, but can still work it out with time. Similarly, models make more reasoning errors when trying to answer right away, rather than taking time to work out an answer. Asking for a "chain of thought" before an answer can help the model reason its way toward correct answers more reliably.
Tactics:
Instruct the model to work out its own solution before rushing to a conclusion
Use inner monologue or a sequence of queries to hide the model's reasoning process
Use external tools
Compensate for the weaknesses of the model by feeding it the outputs of other tools. For example, a text retrieval system (sometimes called RAG or retrieval augmented generation) can tell the model about relevant documents. A code execution engine like OpenAI's Code Interpreter can help the model do math and run code. If a task can be done more reliably or efficiently by a tool rather than by a language model, offload it to get the best of both.
Tactics:
Use embeddings-based search to implement efficient knowledge retrieval
Use code execution to perform more accurate calculations or call external APIs
Test changes systematically
Improving performance is easier if you can measure it. In some cases a modification to a prompt will achieve better performance on a few isolated examples but lead to worse overall performance on a more representative set of examples. Therefore to be sure that a change is net positive to performance it may be necessary to define a comprehensive test suite (also known an as an "eval").
Tactic:
Tactics
Each of the strategies listed above can be instantiated with specific tactics. These tactics are meant to provide ideas for things to try. They are by no means fully comprehensive, and you should feel free to try creative ideas not represented here.
Strategy: Write clear instructions
Tactic: Include details in your query to get more relevant answers
In order to get a highly relevant response, make sure that requests provide any important details or context. Otherwise you are leaving it up to the model to guess what you mean.
How do I add up a row of dollar amounts in Excel? I want to do this automatically for a whole sheet of rows with all the totals ending up on the right in a column called "Total".
Who’s president?
Who was the president of Mexico in 2021, and how frequently are elections held?
Write code to calculate the Fibonacci sequence.
Write a TypeScript function to efficiently calculate the Fibonacci sequence. Comment the code liberally to explain what each piece does and why it's written that way.
Summarize the meeting notes.
Summarize the meeting notes in a single paragraph. Then write a markdown list of the speakers and each of their key points. Finally, list the next steps or action items suggested by the speakers, if any.
Tactic: Ask the model to adopt a persona
The system message can be used to specify the persona used by the model in its replies.
When I ask for help to write something, you will reply with a document that contains at least one joke or playful comment in every paragraph.
Write a thank you note to my steel bolt vendor for getting the delivery in on time and in short notice. This made it possible for us to deliver an important order.
Tactic: Use delimiters to clearly indicate distinct parts of the input
Delimiters like triple quotation marks, XML tags, section titles, etc. can help demarcate sections of text to be treated differently.
Summarize the text delimited by triple quotes with a haiku.
You will be provided with a pair of articles (delimited with XML tags) about the same topic. First summarize the arguments of each article. Then indicate which of them makes a better argument and explain why.
You will be provided with a thesis abstract and a suggested title for it. The thesis title should give the reader a good idea of the topic of the thesis but should also be eye-catching. If the title does not meet these criteria, suggest 5 alternatives.
For straightforward tasks such as these, using delimiters might not make a difference in the output quality. However, the more complex a task is the more important it is to disambiguate task details. Don’t make the model work to understand exactly what you are asking of them.
Tactic: Specify the steps required to complete a task
Some tasks are best specified as a sequence of steps. Writing the steps out explicitly can make it easier for the model to follow them.
Providing general instructions that apply to all examples is generally more efficient than demonstrating all permutations of a task by example, but in some cases providing examples may be easier. For example, if you intend for the model to copy a particular style of responding to user queries which is difficult to describe explicitly. This is known as "few-shot" prompting.
The river that carves the deepest valley flows from a modest spring; the grandest symphony originates from a single note; the most intricate tapestry begins with a solitary thread.
Tactic: Specify the desired length of the output
You can ask the model to produce outputs that are of a given target length. The targeted output length can be specified in terms of the count of words, sentences, paragraphs, bullet points, etc. Note however that instructing the model to generate a specific number of words does not work with high precision. The model can more reliably generate outputs with a specific number of paragraphs or bullet points.
Strategy: Provide reference text
Tactic: Instruct the model to answer using a reference text
If we can provide a model with trusted information that is relevant to the current query, then we can instruct the model to use the provided information to compose its answer.
Given that all models have limited context windows, we need some way to dynamically lookup information that is relevant to the question being asked. Embeddings can be used to implement efficient knowledge retrieval. See the tactic "Use embeddings-based search to implement efficient knowledge retrieval" for more details on how to implement this.
Tactic: Instruct the model to answer with citations from a reference text
If the input has been supplemented with relevant knowledge, it's straightforward to request that the model add citations to its answers by referencing passages from provided documents. Note that citations in the output can then be verified programmatically by string matching within the provided documents.
You will be provided with a document delimited by triple quotes and a question. Your task is to answer the question using only the provided document and to cite the passage(s) of the document used to answer the question. If the document does not contain the information needed to answer this question then simply write: "Insufficient information." If an answer to the question is provided, it must be annotated with a citation. Use the following format for to cite relevant passages ({"citation": …}).
Strategy: Split complex tasks into simpler subtasks
Tactic: Use intent classification to identify the most relevant instructions for a user query
For tasks in which lots of independent sets of instructions are needed to handle different cases, it can be beneficial to first classify the type of query and to use that classification to determine which instructions are needed. This can be achieved by defining fixed categories and hardcoding instructions that are relevant for handling tasks in a given category. This process can also be applied recursively to decompose a task into a sequence of stages. The advantage of this approach is that each query will contain only those instructions that are required to perform the next stage of a task which can result in lower error rates compared to using a single query to perform the whole task. This can also result in lower costs since larger prompts cost more to run (see pricing information).
Suppose for example that for a customer service application, queries could be usefully classified as follows.