This course material introduces prompt engineering, focusing on practical application rather than rote memorization of prompts. It explains how large language models (LLMs) function, emphasizing the importance of understanding their underlying mechanisms—like tokens and context windows—to craft effective prompts. The course uses examples and exercises to illustrate how prompt design impacts LLM outputs, covering various techniques like using personas and custom instructions. It stresses the iterative nature of prompt engineering and the ongoing evolution of the field. Finally, the material explores the potential of LLMs and the ongoing debate surrounding artificial general intelligence (AGI).

Prompt Engineering Study Guide

Quiz

Instructions: Answer the following questions in 2-3 sentences each.

1. What is the main focus of the course, according to the instructor?
2. Why is prompt engineering a skill, not a career, in the instructor’s opinion?
3. How did the performance of large language models change as they got larger?
4. What is multimodality, and what are four things a leading LLM can do?
5. What is the purpose of the playground mentioned in the course?
6. What are tokens, and how are they used by large language models?
7. What is temperature in the context of language models, and how does it affect outputs?
8. Explain the “reversal curse” phenomenon in large language models.
9. What are the two stages of training for large language models?
10. How does the system message influence the model’s behavior?

Quiz Answer Key

1. The main focus of the course is working with large language models, teaching how to use this new technology effectively in various aspects of work and life. It is not focused on selling pre-made prompts but on understanding the models themselves.
2. The instructor believes that prompt engineering is a skill that enhances any job, not a standalone career. He argues that it’s a crucial skill for efficiency, not a profession in itself.
3. As models increased in size, performance at certain tasks did not increase linearly but instead skyrocketed, with new abilities emerging that weren’t present in smaller models. This was an unexpected and non-linear phenomenon.
4. Multimodality is the ability of LLMs to understand and generate not only text, but also other modalities like images, the internet, and code. LLMs can accept and generate text, accept images, browse the internet, and execute python code.
5. The playground is a tool that allows users to experiment with and test the different settings of large language models. It is a space where one can fine-tune and better understand the model’s outputs.
6. Tokens are the way that LLMs understand and speak; they are smaller pieces of words that the model analyzes. LLMs determine the sequence of tokens most statistically probable to follow your input, based on training data.
7. Temperature is a setting that controls the randomness of the output of large language models. Lower temperature makes the output more predictable and formalistic, while higher temperature introduces randomness and can lead to creativity or gibberish.
8. The reversal curse refers to the phenomenon where an LLM can know a fact but fail to provide it when asked in a slightly reversed way. For example, it may know that Tom Cruise’s mother is Mary Lee Pfeiffer but not that Mary Lee Pfeiffer is Tom Cruise’s mother.
9. The two stages are pre-training and fine-tuning. In pre-training, the model learns patterns from a massive text dataset. In fine-tuning, a base model is adjusted to be an assistant, typically through supervised learning.
10. The system message acts as a “North Star” for the model, it provides a set of instructions or context at the outset that directs how the model should behave and interact with users. It is the model’s guiding light.

Essay Questions

Instructions: Answer the following questions in essay format. There is no single correct answer for any of the questions.

1. Discuss the concept of emergent abilities in large language models. How do these abilities relate to the size of the model, and what implications do they have for the field of AI?
2. Explain the Transformer model, and discuss why it was such a significant breakthrough in natural language processing. How has it influenced the current state of AI technologies?
3. Critically analyze the role of the system message in prompt engineering. In what ways can it be used to both enhance and undermine the functionality of an LLM?
4. Explore the role of context in prompt engineering, discussing both its benefits and potential pitfalls. How can prompt engineers effectively manage context to obtain the most useful outputs?
5. Discuss the various strategies employed throughout the course to trick or “break” an LLM. What do these strategies reveal about the current limitations of AI technology?

Glossary of Key Terms

Artificial Intelligence (AI): A broad field of computer science focused on creating intelligent systems that can perform tasks that typically require human intelligence.

Base Model: The initial output of the pre-training process in large language model development. It is a model that can do language completion, but is not yet conversational.

Context: The information surrounding a prompt, including previous conversation turns, relevant details, and additional instructions that help a model understand the task.

Context Window: The maximum number of tokens that a large language model can consider at any given time in a conversation. Also known as token limit.

Custom Instructions: User-defined instructions in platforms like ChatGPT that affect every conversation with a model.

Deep Learning: A subfield of machine learning that uses artificial neural networks with multiple layers to analyze data.

Emergent Abilities: Unforeseen abilities that appear in large language models as they scale up in size, which are not explicitly coded but rather learned.

Fine-Tuning: The process of adapting a base model to specific tasks and use cases, usually through supervised learning.

Large Language Model (LLM): A type of AI model trained on vast amounts of text data, used to generate human-like text.

Machine Learning: A subset of AI that enables systems to learn from data without being explicitly programmed.

Mechanistic Interpretability: The field of study dedicated to figuring out what’s happening when tokens pass through all the various layers of the model.

Multimodality: The ability of a language model to process and generate information beyond text, such as images, code, and internet browsing.

Natural Language Processing (NLP): A branch of AI that enables computers to understand, interpret, and generate human language.

Parameters: The internal variables of a large language model that it learns during training, affecting its ability to make predictions.

Persona: The role or identity given to a language model, which influences its tone, style, and the way it responds.

Pre-Training: The initial phase of large language model training, where the model is exposed to massive amounts of text data to learn patterns.

Prompt Engineering: The practice of designing effective prompts that can elicit the desired responses from AI models, particularly large language models.

System Message: The initial instructions or guidelines provided to a large language model by the model creator, which establishes its behavior and role. Also known as meta-prompt or system prompt.

Temperature: A parameter in large language models that controls the randomness of the output. Higher temperature leads to more diverse outputs, while lower temperatures produce more predictable responses.

Tokens: The basic units of text processing for large language models. They are often sub-word units that represent words, parts of words, or spaces.

Transformer Model: A neural network architecture that uses the “attention” mechanism to process sequences of data, such as text, enabling large language models to consider context over long ranges.

Prompt Engineering: Mastering Large Language Models

Okay, here is a detailed briefing document summarizing the key themes and ideas from the provided text, incorporating quotes where appropriate:

Briefing Document: Prompt Engineering Course Review

Introduction:

This document summarizes the main concepts discussed in a course focused on working with Large Language Models (LLMs), often referred to as “prompt engineering.” The course emphasizes practical application and understanding the mechanics of LLMs, rather than rote memorization of specific prompts. It highlights the importance of viewing prompt engineering as a multi-disciplinary skill, rather than a career in itself, for most individuals.

Key Themes and Ideas:

1. Prompt Engineering is More Than Just Prompts:

• The course emphasizes that true “prompt engineering” is not about memorizing or using pre-made prompts. As the instructor Scott states, “it’s not about teaching you 50 promps to boost your productivity…you’re going to learn to work with these large language models.”
• Scott believes that “there are plenty of people out there trying to sell you prompt libraries I think those are useless. They’re single prompts that are not going to produce exactly what you need for your work.” Instead, the course aims to teach how LLMs work “under the hood” so users can create effective prompts for their specific use cases.

1. Prompt Engineering as a Multi-Disciplinary Skill:

• The course defines prompt engineering as “a multi-disciplinary branch of engineering focused on interacting with AI through the integration of fields such as software engineering, machine learning, cognitive science like psychology, business, philosophy, computer science.”
• It stresses that “whatever your area of expertise is…you are going to be able to take that perspective and add it to the field.” This is because the field is new and constantly evolving.

1. Understanding How LLMs Work is Crucial:

• The core idea of the course is that to effectively use LLMs, you need to understand how they function internally. This includes concepts like tokens, parameters, and the Transformer architecture.
• “you need to understand what’s going on behind the scenes so that you can frame your prompt in the right light.”
• The course emphasizes that LLMs are not simply coded programs that have pre-set responses but rather “trained on data and after that training certain abilities emerged.”
• Emergent abilities, new capabilities that appear as models scale in size, demonstrate that these are not simply predictable increases in performance. This “scaling up the model linearly should increase performance linearly, but that’s not what happened.”

1. LLMs are not perfect:

• The course emphasizes that, despite the impressiveness of LLMs, they are still prone to making mistakes due to a few reasons including user error and their design.
• “it’s because we’re not dealing with code or a computer program here in the traditional sense. We’re dealing with a new form of intelligence, something that was trained on a massive data set and that has certain characteristics and limitations.”
• The concept of “hallucinating”, where the LLM produces confident yet false statements, is also important to keep in mind.

1. Multimodality and Capabilities:

• LLMs can handle more than just text. They can process and generate images, browse the internet (to access current information), and execute code, particularly Python code.
• “it can accept and generate text, it can accept images, it can generate images, it can browse the internet…and it can execute python code.”
• The course walks through an example of an LLM creating and refining a simple game by using Python.

1. Tokens are the Foundation:

• LLMs understand and “speak” in tokens, which are sub-word units, not whole words. “one token is equal to about 0.75 words”.
• The model determines the most statistically probable sequence of tokens based on its training data, giving the impression of “guessing” the next word.
• A high temperature setting increases the randomness when picking tokens, leading to more casual and sometimes nonsensical outputs, while a low temperature setting produces more formal output.

1. The Importance of Context and its Limitations:

• Providing sufficient context in prompts improves accuracy.
• However, there is a limitation to the amount of context LLMs can handle at a given time (the token or context window).
• “every time you send a prompt your entire conversation history is bundled up and packed on to the prompt…chat GPT is essentially constantly reminding of your entire conversation.”
• Once the context window fills, older information starts to be forgotten and accuracy can be compromised. This happens without the user necessarily realizing it.
• Information provided at the beginning of a prompt has a larger impact and is remembered better than information provided at the end, in effect creating a “Primacy Effect”. Information in the middle is more readily forgotten. This process mimics how the human brain handles context.

1. The Power of Personas:

• Giving an LLM a specific persona or role (“you are an expert mathematician,” or even a character such as Bilbo Baggins) provides it with crucial context and improves the quality of responses. This allows the user to better interact with and leverage LLMs.
• Personas are often set via the system message or by custom instructions.

1. Custom Instructions

• Users can provide instructions that the LLM uses as its “North Star” much in the same way as a system message.
• These “custom instructions” are used for any new chat, however users may forget about these instructions which may cause problems.

1. LLMs and “Secrets”:

• LLMs are not designed to keep secrets and are susceptible to being tricked into revealing private information given the right prompt.
• The way these LLMs “think” with tokens also enables the spilling of tea by crafting prompts that circumvent normal parameters.

1. The LLM Landscape:

• The course breaks down the LLM landscape into base models, which are trained on data and then further fine-tuned to create chatbot interfaces or domain specific models. The Transformer architecture enables LLMs to pay attention to and incorporate a wider range of context.
• Different companies, such as OpenAI, Anthropic, and Meta, create various models, including open-source ones like Llama 2.

Practical Applications:

• The course focuses on practical applications of prompt engineering. It uses examples such as making a game and generating music using an AI.
• The skills learned in the course can be used to create chatbots, generate code, understand complex documents, and make other helpful outputs to assist in work, study, or just general life.

Conclusion:

This course aims to provide a deep understanding of LLMs and how to effectively interact with them through thoughtful prompt engineering. It prioritizes practical knowledge, emphasizing that it is a “skill” rather than a “career” for most individuals, and that this skill is important for everyone. It is constantly updated with the latest techniques for effective prompting. By understanding the underlying mechanisms and limitations of these models, users can leverage their immense potential in their work and lives.

Prompt Engineering and Large Language Models

Prompt Engineering and Large Language Models: An FAQ

1. What exactly is “prompt engineering” and why is it important?
2. While the term “prompt engineering” is commonly used, it’s essentially about learning how to effectively interact with large language models (LLMs) to utilize their capabilities in various work and life situations. Instead of focusing on memorizing specific prompts, it’s about understanding how LLMs work so you can create effective instructions tailored to your unique needs. It’s a multi-disciplinary skill, drawing from software engineering, machine learning, psychology, business, philosophy, and computer science, and it is crucial for harnessing the full potential of AI for efficiency and productivity. It is considered more of a skill that enhances various roles, rather than a job in and of itself.
3. Why is prompt engineering necessary if LLMs are so advanced?
4. LLMs aren’t just programmed with specific answers; they learn from vast datasets and develop emergent abilities. Prompt engineering is necessary because we’re not dealing with traditional code or programs. We’re working with a form of intelligence that has been trained to predict the most statistically probable sequence of tokens, given the prompt and its training data. By understanding how these models process information, you can learn to frame your prompts in a way that leverages their understanding, yielding more accurate results. Also, prompting techniques can elicit abilities from models that might not be present when prompted in more basic ways.
5. Are prompt libraries or pre-written prompts helpful for prompt engineering?
6. While pre-written prompts can introduce you to what’s possible with LLMs, they are generally not very useful for true prompt engineering. Each user’s needs are unique, so generic prompts are unlikely to provide the results you need for your specific work. You’re better off learning the underlying principles of how to interact with LLMs than memorizing a collection of single-use prompts. It’s about developing an intuitive understanding of how to phrase requests, which enables you to naturally create effective prompts for your situation.
7. What is multimodality in the context of LLMs and how can it be used?
8. Multimodality refers to an LLM’s ability to understand and generate text, images, and even code. This goes beyond simple text inputs and outputs. LLMs can take images as prompts and give text responses to them, browse the internet to access more current data, or even execute code to perform calculations. This means prompts can incorporate diverse inputs and generate diverse outputs, greatly expanding the potential ways that LLMs can be used.
9. What is the “playground” and why might someone use it?
10. The playground is an interface provided by OpenAI (and other companies) that allows you to experiment directly with different LLMs, as well as test advanced settings and features such as temperature (for randomness) and the probability of the next token. It’s an important tool for advanced users to understand how the underlying technology works and to test techniques such as different system messages before implementing them into their products or day-to-day work with AI. It’s relatively inexpensive to use the playground and is a good place to go for more in-depth experimentation with AI tools.
11. What are “tokens” and why are they important?
12. Tokens are the fundamental units that LLMs use to understand and generate language. They’re like words, but LLMs actually break words down into smaller pieces. One token is approximately equivalent to 0.75 words. LLMs do not see words the way humans do; instead they see tokens that have a numerical ID which is part of a complex lookup table. The LLM statistically predicts the most probable sequence of tokens to follow your input, which is why it is often described as a ‘word guessing machine’. A word can consist of multiple tokens. Understanding this helps you see how LLMs are processing information on a basic level. This basic understanding of tokens will help guide your prompts more effectively.
13. What is the significance of “system messages” or “meta prompts” in prompt engineering?
14. A system message is an initial, often hidden, instruction or context that’s provided to the LLM before it interacts with the user. It acts as a “North Star” for the model, guiding its behavior, tone, and style. The system message determines how the model responds to user input and how it will generally interpret all user prompts. Understanding system messages is vital, particularly if you are developing an application that incorporates an LLM. System messages can be modified to tailor the model to various tasks or use cases, but it’s important to be aware that a model will always be pulled back to its original system message. Also, adding specific instructions to the system message will help the model with complex instructions that you want the model to remember for each and every interaction.
15. What is context, and why is it important when prompting, and why does the rule of more context being better not always hold up?
16. Context refers to all the information or details that accompany a prompt, including past conversation history, instructions or details within the prompt itself, and even the system message. More context usually leads to better, more accurate responses. However, LLMs have a limited “token window” (or a context window) which sets a maximum amount of text or context they can manage at any one time. When you exceed this limit, older context tokens are removed. It is imperative that the most important information or context is placed at the beginning of the context window because models have a tendency to pay more attention to the first and last part of a context window, and less to the information in the middle. Additionally, too much context can actually decrease the accuracy of an LLM, because the model will sometimes pay less attention to relevant information, or become bogged down by less relevant information.

Prompt Engineering: A Comprehensive Guide

Prompt engineering is a critical skill that involves developing and optimizing prompts to efficiently use artificial intelligence for specific tasks [1, 2]. It is not typically a standalone career but a skill set needed to use AI effectively [1, 3]. The goal of prompt engineering is to use AI to become more efficient and effective in work and life [2, 3].

Key aspects of prompt engineering include:

• Understanding Large Language Models (LLMs): It is essential to understand how LLMs work under the hood to effectively utilize them when prompting [3]. These models are not simply code; they have emergent abilities that arise as they grow larger [4, 5]. They are sensitive to how prompts are framed, and even slight changes can lead to significantly different responses [2].
• Prompts as Instructions: Prompts are essentially the instructions and context provided to LLMs to accomplish tasks [2]. They are like seeds that grow into useful results [2].
• Elements of a Prompt: A basic prompt has two elements: the input (the instruction) and the output (the model’s response) [6].
• Not Just About Productivity: Prompt engineering is not just about using pre-made prompts to boost productivity. Instead, it is about learning to work with LLMs to utilize them for specific use cases [3, 7, 8].
• Multi-Disciplinary Field: Prompt engineering integrates fields such as software engineering, machine learning, cognitive science, business, philosophy, and computer science [9].
• Importance of Empirical Research: The field is undergoing a lot of research, and prompt engineering should be based on empirical research that shows what works and what doesn’t [10].
• Hands-On Experience: Prompt engineering involves hands-on demos, exercises, and projects, including coding and developing prompts [10]. It requires testing, trying things out, and iterating until the right output is achieved [11, 12].
• Natural Language: Prompt engineering is like programming in natural language. Like programming, specific words and sequences are needed to get the right result [6].
• Beyond Basic Prompts: It’s more than just asking a question; it’s about crafting prompts to meet specific needs, which requires understanding how LLMs work [6, 7, 13].

Applied Prompt Engineering involves using prompt engineering principles in the real world to improve work, career, or studies [13, 14]. It includes using models to complete complex, multi-step tasks [8].

Why Prompt Engineering is Important:

• Maximizing Potential: It is key to using LLMs productively and efficiently to achieve specific goals [8].
• Avoiding Errors and Biases: Proper prompt engineering helps to minimize errors and biases in the model’s output [8].
• Programming in Natural Language: Prompt engineering is an example of programming using natural language [15].
• Future Workplace Skill: Prompt engineering skills will be essential in the workplace, just like Microsoft Word and Excel skills are today [3, 10]. A person with the same skills and knowledge but who also knows how to use AI through prompt engineering will be more effective [16].

Tools for Prompt Engineering:

• Chat GPT: The user interface to interact with LLMs [16, 17].
• OpenAI Playground: An interface for interacting with the OpenAI API that allows for more control over the LLM settings [16, 18].
• Replit: An online integrated development environment (IDE) to run coding applications [19].

Key Concepts in Prompt Engineering:

• Tokens: The way LLMs understand and speak. Words are broken down into smaller pieces called tokens [20].
• Attention Mechanism: This allows the model to pay more attention to more context [21, 22].
• Transformer Architecture: An architecture that allows the model to pay attention to more context, enabling better long-range attention [22, 23].
• Parameters: The “lines” and “dots” that enable the model to recognize patterns. LLMs compress data through parameters and weights [24, 25].
• Base Model: A model resulting from the pre-training phase, which is not a chatbot but rather a model that completes words or tokens [25].
• Fine-Tuning: The process of taking the base model and giving it additional text information so it can generate more helpful and specific output [25, 26].
• System Message: A default prompt provided to the model by its creator that sets the stage for interactions by including instructions or specific context [27]. It is like a North Star, guiding the model’s behavior [27, 28].
• Context: The additional information provided to the LLM that helps it better understand the task and respond accurately [29].
• Token Limits: LLMs have token limits, which are the maximum amount of words they can remember at any given time. This also acts as a context window [30, 31].
• Recency Effect: The effect of information being more impactful when given towards the end [32, 33].
• Personas: Giving the model a persona or role can help it provide better, more accurate responses [34, 35]. Personas work because they provide additional context [35].

This summary should provide a clear overview of what prompt engineering is and its key components.

Large Language Models: An Overview

Large Language Models (LLMs) are a type of machine learning model focused on understanding and generating natural language text [1, 2]. They are characterized by being trained on vast amounts of text data and having numerous parameters [2]. LLMs are a subset of Natural Language Processing (NLP), which is a branch of Artificial Intelligence focused on enabling computers to understand text and spoken words the same way human beings do [1, 3].

Here’s a more detailed breakdown of key aspects of LLMs:

• Size and Training: The term “large” in LLMs refers to the fact that these models are trained on massive datasets, often consisting of text from the internet [2, 4]. These models also have a large number of parameters, which are the “lines” and “dots” that enable the model to recognize patterns [4, 5]. The more tokens and parameters, the more capable a model generally is [6].
• Parameters: Parameters are part of the model’s internal structure that determine how it processes information [5, 7]. They can be thought of as the “neurons” in the model’s neural network [7].
• Emergent Abilities: LLMs exhibit emergent abilities, meaning that as the models become larger, new capabilities arise that weren’t present in smaller models [8, 9]. These abilities aren’t explicitly programmed but emerge from the training process [8].
• Tokens: LLMs understand and process language using tokens, which are smaller pieces of words, rather than the words themselves [10]. Each token has a unique ID, and the model predicts the next token in a sequence [11].
• Training Process: The training of an LLM typically involves two main phases:
• Pre-training: The model is trained on a large corpus of text data to learn patterns and relationships within the text [7]. This results in a base model [12].
• Fine-tuning: The base model is further trained using a more specific dataset, often consisting of ideal questions and answers, to make it better at completing specific tasks or behaving like a helpful assistant [12, 13]. The fine tuning process adjusts the parameters and weights of the model, which also impacts the calculations within the model and creates emergent abilities [13].
• Transformer Architecture: LLMs utilize a transformer architecture, which allows the model to pay attention to a wider range of context, improving its ability to understand the relationships between words and phrases, including those separated by large distances [6, 14]. This architecture helps enable better long-range attention [14].
• Context Window: LLMs have a limited context window, meaning they can only remember a certain number of tokens (or words) at once [15]. The token limit acts as a context window [16]. The context window is constantly shifting, and when a new prompt is given, the older information can be shifted out of the window, meaning that the model may not have all of the prior conversation available at any given time [15, 16]. Performance is best when relevant information is at the beginning or end of the context window [17].
• Word Guessing: At their core, LLMs are essentially “word guessing machines”, determining the most statistically probable sequence of tokens to follow a given prompt, based on their training data [11, 18].
• Relationship to Chatbots: LLMs are often used as the underlying technology for chatbots. For example, the GPT models from OpenAI are used by the ChatGPT chatbot [2, 19]. A chatbot is essentially a user interface or “wrapper” that makes it easy for users to interact with a model [20]. The system message provides a default instruction to the model created by the creator of the model [21]. Custom instructions can also be added to change the model’s behavior [22].
• Task-Specific Models: Some models are fine-tuned for specific tasks. For example, GitHub Copilot uses the GPT model but has been further fine-tuned for code generation [19, 20].
• Limitations: LLMs can sometimes provide incorrect or biased information, and they can also struggle with math [23, 24]. These models can also hallucinate (make things up) [25, 26]. They may also learn that A=B but not that B=A, which is known as the “reversal curse” [27]. Also, the model may only remember information in the context window and can forget information from the beginning of a conversation [16].

In summary, LLMs are sophisticated models that process and generate language using statistical probabilities, trained on extensive datasets and incorporating architectures that allow for better context awareness, but are also limited by context windows, and other factors, and may produce errors or biased results..

AI Tools and Prompt Engineering

AI tools, particularly those powered by Large Language Models (LLMs), are becoming increasingly prevalent in various aspects of work and life [1-4]. These tools can be broadly categorized based on their underlying model and specific functions [5, 6].

Here’s a breakdown of key aspects regarding AI tools, drawing from the sources:

• LLMs as the Foundation: Many AI tools are built upon LLMs like GPT from OpenAI, Gemini from Google, Claude from Anthropic, and Llama from Meta [5-8]. These models provide the core ability to understand and generate natural language [5, 6].
• Chatbots as Interfaces:
• Chatbots like ChatGPT, Bing Chat, and Bard use LLMs as their base [5, 6]. They act as a user interface (a “wrapper”) that allows users to interact with the underlying LLM through natural language [5, 6].
• The user interface makes it easier to input prompts and receive outputs [6]. Without it, interaction with an LLM would require code [6].
• Chatbots also have a system message, which is a default prompt that is provided by the chatbot’s creator to set the stage for interactions and guides the model [9, 10].
• Custom instructions can also be added to chatbots to further change the model’s behavior [11].
• Task-Specific AI Tools:
• These tools are designed for specific applications, such as coding, writing, or other domain-specific tasks [6, 7].
• Examples include GitHub Copilot, Amazon CodeWhisperer (for coding), and Jasper AI and Copy AI (for writing) [6, 7].
• They often use a base model that has been fine-tuned for their specific purposes [6, 7]. For example, GitHub Copilot uses a modified version of OpenAI’s GPT model fine-tuned for code generation [7].
• Task-specific tools may also modify the system message or system prompt to further customize the model’s behavior [6, 12].
• Custom AI Tools: AI tools can also be customized to learn a specific subject, improve mental health, or complete a specific task [13].
• Multimodality: Some advanced AI tools, like ChatGPT, can handle multiple types of input and output [14]:
• Text : They can generate and understand text [14].
• Images: They can accept images and generate images [14-16].
• Internet: They can browse the internet to gather more current information [17].
• Code: They can execute code, specifically Python code [17].
• Prompt Engineering for AI Tools:
• Prompt engineering is the key to using AI tools effectively [13].
• It helps maximize the potential of AI tools, avoid errors and biases, and ensure the tools are used efficiently [13].
• The skill of prompt engineering involves crafting prompts that provide clear instructions to the AI tool, guiding it to produce the desired output [4, 13].
• It requires an understanding of how LLMs work, including concepts like tokens, context windows, and attention mechanisms [2, 12, 18, 19].
• Effective prompts involve more than simply asking a question; they involve understanding the task, the capabilities of the AI tool, and the science of prompt engineering [4].
• Using personas and a unique tone, style and voice with AI tools can make them more intuitive for humans to use, improve their accuracy, and help them to be on brand [20, 21].
• By setting up a tool with custom instructions, it’s possible to effectively give the tool a new “North Star” or behavior profile [11, 22].
• Importance of Training Data: The effectiveness of an AI tool depends on the data it has been trained on [23]. The training process involves both pre-training on a vast amount of text data and then fine-tuning on a specific dataset to enhance its capabilities [24, 25].

In summary, AI tools are diverse and powerful, with LLMs acting as their core technology. These tools range from general-purpose chatbots to task-specific applications. Prompt engineering is a critical skill for maximizing the effectiveness of these tools, allowing users to tailor their behavior and output through carefully crafted prompts [13]. Understanding how LLMs function, and having clear and specific instructions are key for success in using AI tools [4, 12].

Prompt Engineering: Principles and Best Practices

Prompt engineering involves the development and optimization of prompts to effectively use AI for specific tasks [1]. It is a skill that can be used by anyone and everyone, regardless of their job or technical background [2]. The goal of prompt engineering is to use AI to become more efficient and effective in work by understanding how Large Language Models (LLMs) function [2]. It is a multi-disciplinary branch of engineering focused on interacting with AI through the integration of fields such as software engineering, machine learning, cognitive science, business, philosophy, and computer science [3, 4].

Key principles of prompt engineering include:

• Understanding LLMs: It’s important to understand how LLMs work under the hood, including concepts like tokens, the transformer architecture, and the context window [2]. LLMs process language using tokens, which are smaller pieces of words [5]. They also use a transformer architecture, allowing them to pay attention to more context [6].
• Prompts as Instructions: A prompt is essentially the instructions and context given to LLMs to accomplish a task [1]. It’s like a seed that you plant in the LLM’s mind that grows into a result [1]. Prompts are like coding in natural language, requiring specific words and sequences to get the right result [3].
• Prompt Elements: A basic prompt consists of two elements, an input (the question or instruction) and an output (the LLM’s response) [3].
• Iterative Process: Prompt engineering is an iterative process of testing, trying things out, evaluating, and adjusting until the desired output is achieved [7].
• Standard Prompts: The most basic type of prompt is the standard prompt, which consists only of a question or instruction [8]. These are important because they are often the starting place for more complex prompts, and can be useful for gathering information from LLMs [9].
• Importance of Context: Providing the LLM with more information or context generally leads to a better and more accurate result [10]. Context includes instructions, background information, and any other relevant details. It helps the LLM understand the task and generate a more helpful response. More context means more words and tokens for the model to analyze, causing the attention mechanism to focus on relevant information and reducing the likelihood of errors [11]. However, providing too much context can also be detrimental, as LLMs have token limits [12, 13].
• Context Window: LLMs have a limited context window (also known as a token limit), which is the number of tokens (or words) the model can remember at once [12, 13]. Once that limit is reached, the model will forget information from the beginning of the conversation. Therefore, it is important to manage the context window to maintain the accuracy and coherence of the model’s output [12].
• Primacy and Recency Effects: Information placed at the beginning or end of a context window is more likely to be accurately recalled by the model, while information in the middle can get lost [14-16]. For this reason, place the most important context at the beginning of a prompt [16].
• Personas: Giving an LLM a persona or role can provide additional context to help it understand the task and provide a better response [17-19]. Personas help to prime the model to think in a certain way. Personas can be functional and fun [20, 21].
• Tone, Style, and Voice: A persona can also include a specific tone, style, and voice that are unique to the task, which can help produce more appropriate and nuanced outputs [21].
• Custom Instructions: Custom instructions are a way to give the model more specific information about what you want it to know or how you want it to respond [21]. This is similar to giving the model a sub system message.

In summary, prompt engineering is about understanding how LLMs work and applying that understanding to craft effective prompts that guide the model toward accurate, relevant, and helpful outputs. By paying attention to detail and incorporating best practices, users can achieve much more with LLMs and tailor them to meet their specific needs and preferences [22].

Mastering Prompt Engineering with LLMs

This course provides an in-depth look at prompt engineering and how to work with large language models (LLMs) [1]. The course emphasizes gaining practical, real-world skills to put you at the forefront of the AI world [1]. It aims to teach you how to use AI to become more efficient and effective in your work [2]. The course is taught by Scott Kerr, an AI enthusiast and practitioner [1].

Here’s an overview of the key components of the course:

• Focus on Practical Skills: The course focuses on teaching how to work with LLMs for specific use cases, rather than providing a library of pre-made prompts [2]. It emphasizes learning by doing, with numerous exercises and projects, including guided and unguided projects [1]. The projects include coding games and using autonomous agents, among other tasks [3].
• Understanding LLMs: A key part of the course involves diving deep into the mechanics of LLMs, understanding how they work under the hood, and using that knowledge when prompting them [2].
• This includes understanding how LLMs use tokens [4], how they use the transformer architecture [5], and the concept of a context window [6].
• The course also covers the training process of LLMs and the difference between base models and assistant models [7].
• Prompt Engineering Principles: The course teaches prompt engineering as a multi-disciplinary branch of engineering that requires integrating fields such as software engineering, machine learning, cognitive science, business, philosophy, and computer science [8]. The course provides a framework for creating complex prompts [9]
• Standard Prompts: The course starts with the most basic prompts, standard prompts, which are a single question or instruction [10].
• Importance of Context: The course teaches the importance of providing the LLM with more information or context, which includes providing relevant instructions and background information to get more accurate results [11].
• The course emphasizes placing key information at the beginning or end of the prompt for best results [12].
• Managing the Context Window: The course emphasizes the importance of managing the limited context window of the LLMs, to maintain accuracy and coherence [6].
• System Messages: The course discusses the importance of the system message, which acts as the “North Star” for the model, and it teaches users how to create their own system message for specific purposes [13].
• Personas: The course teaches the use of personas to give LLMs a specific role, tone, style and voice, to make them more useful for humans to use [14, 15].
• Applied Prompt Engineering: The course emphasizes using prompt engineering principles in real-world scenarios to make a difference in your work [16]. The course shows the difference in responses between a base model and an assistant model, using LM Studio, to emphasize the importance of applied prompt engineering [7].
• Multimodality: The course introduces the concept of multimodality and how models like Chat-GPT can understand and produce images as well as text, browse the internet, and execute python code [17-19].
• Tools and Set-Up: The course introduces different LLMs, including the GPT models by Open AI, which can be used through chat-GPT [20]. It also teaches how to use the Open AI playground to interact with the models [20, 21]. The course also emphasizes the importance of using the chat-GPT app to use on a daily basis [22].
• Emphasis on Empirical Research: The course is grounded in empirical research and peer-reviewed studies conducted by AI researchers [3].
• Up-to-Date Information: The course is designed to provide the most up-to-date information in a constantly changing field and is dedicated to continually evolving [23].
• Projects and Exercises: The course includes hands-on demos, exercises, and guided and unguided projects to develop practical skills [3]. These include coding games and using autonomous agents [1].
• Evaluation: The course introduces the concept of evaluating and testing prompts, because in order to be scientific, the accuracy and success of prompts needs to be measurable [24].

In summary, the course is structured to provide a blend of theoretical knowledge and practical application, aiming to equip you with the skills to effectively utilize LLMs in various contexts [1]. It emphasizes a deep understanding of how these models work and the best practices for prompt engineering, so that you can use them to your advantage.

By Amjad Izhar
Contact: amjad.izhar@gmail.com
https://amjadizhar.blog

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