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LLM Prompt Engineering for Developers

2.1 - What is Natural Language Processing?

Natural language refers to the language that humans use to communicate with each other. It encompasses spoken and written language, as well as sign language. Natural language is distinct from formal language, which is used in mathematics and computer programming.

Generative AI systems, specifically ChatGPT, are capable of understanding and producing both natural and formal languages. In both cases, interactive AI assistants like ChatGPT use natural language to communicate with humans. Their output could be a natural language response or a mix of natural and formal languages.

To process, understand, and generate natural language, a whole field of AI has emerged: Natural Language Processing (NLP). NLP, by definition, is the field of artificial intelligence that focuses on the understanding and generation of human language by computers. It is employed in a wide range of applications, including voice assistants, machine translation, chatbots, and more. In other words, when we talk about NLP, we refer to the ability of computers to understand and generate natural language.

NLP has experienced rapid growth in recent years, largely due to advancements in language models such as GPT and BERT. These models are some of the most powerful NLP models to date. But what is a language model?

2.3 - Statistical Models (N-Grams)

Statistical models, like n-gram models, serve as foundational language models commonly used for text classification and language modeling. They can also be adapted for text generation, although more advanced models are typically better suited for complex text-to-text tasks. Within statistical models, word sequence probabilities are derived from training data, enabling the model to estimate the likelihood of the next word in a sequence.

N-gram models specifically consider the preceding n-1 words when estimating the probability of the next word. For instance, a bigram model takes into account only the preceding single word, while a trigram model examines the two preceding words. This characteristic endows n-gram models with quick training and utilization capabilities, but they exhibit limitations in capturing long-range dependencies.

ℹ️ In a trigram model, each current word is paired with the two preceding words, forming sequences of three words. For instance, in the sentence “A man of knowledge restrains his words,” the observed trigrams would include “A man of,” “man of knowledge,” “of knowledge restrains,” “knowledge restrains his,” and “restrains his words.” These sequential 3-word patterns are then employed by the model to estimate the probabilities of subsequent words.

Rather than clustering words, n-gram models leverage local word order and context derived from the training data. By focusing on these short-term sequences, n-gram models can make predictions about forthcoming words without modeling global semantics. Although they are efficient and straightforward, their local context makes them less suitable for generating lengthy texts.

Statistical models, particularly n-grams, are quite different from the more recent neural language models. The concept of “prompt engineering” as it’s understood today is more closely associated with the latter. However, there are ways in which the design of input or the preprocessing of data for n-gram models can be thought of as a precursor to prompt engineering.

2.6 - Neural Network-Based Models

Neural network-based models are models that learn and process information in a way inspired by the human brain. They are designed to recognize patterns and make predictions based on large amounts of data. These models consist of interconnected artificial neurons that work together to solve tasks such as image recognition, natural language processing, voice recognition, and more!

Neural network-based models are used in various applications, including self-driving cars, virtual assistants, and recommendation systems. They enable computers to learn from examples and improve their performance over time.

2.6.1 - Feedforward Neural Networks

Feedforward neural networks are the simplest type of neural network. They consist of an input layer, one or more hidden layers, and an output layer. The input layer receives the input data, which is then passed through the hidden layers to the output layer.

Each layer consists of a set of neurons that perform a specific task. The neurons in the input layer receive the input data and pass it to the neurons in the hidden layers. The neurons in the hidden layers perform calculations on the input data and pass the results to the neurons in the output layer. The neurons in the output layer perform calculations on the results and produce the final output.

This type of neural network can be used for tasks such as image recognition, speech recognition, and other simple classification tasks. However, it is not suitable for more complex tasks.

2.6.2 - Recurrent Neural Networks (RNNs)

Recurrent Neural Networks (RNNs) were created to overcome the limitations of traditional feedforward neural networks in handling sequential data. Unlike feedforward networks, which process inputs independently, RNNs have the ability to retain information from previous steps in the sequence. This is what makes them well-suited for handling sequential data like text and speech.

They are used in various applications such as translation, sentiment analysis, and text-to-speech processing.

ℹ️ Sequential data is data that is ordered in a particular way, and each element in the sequence has a specific meaning. For example, a sentence is a sequence of words, and each word has a specific meaning.

2.6.3 - Long Short-Term Memory (LSTM)

Long Short-Term Memory (LSTM) is a special type of neural network that excels at understanding and remembering information in sequences of data. It was created to solve a problem that regular neural networks have with remembering things over long periods.

Regular neural networks can sometimes forget important information when dealing with sequences. But LSTM is designed to remember important details and pass them along through many steps in the sequence.

This makes LSTM useful for tasks where understanding the order and context of the data is important, such as language translation, speech recognition, and predicting the next word in a sentence.

ℹ️ LSTM is like a smart memory that helps the neural network remember things in the right order and context.

They can be used in text generation tasks. While you can provide an LSTM with an initial sequence (akin to a “prompt”) to generate or classify subsequent sequences, the nuanced manipulation of this initial sequence to guide the LSTM’s output is not typically referred to as “prompt engineering.”

2.6.4 - Gated Recurrent Units (Grus)

Gated Recurrent Units (GRUs) are a type of neural network architecture designed to process sequences of data, much like Long Short-Term Memory (LSTM) networks. One of the primary motivations behind the development of GRUs was to address some of the complexities and computational demands of LSTMs, while still effectively capturing long-term dependencies in sequential data. As a result, GRUs have a more streamlined structure than LSTMs, which often allows them to train faster and require fewer computational resources. A key feature of GRUs is the use of “gates.”

ℹ️ Think of gates as checkpoints that regulate the flow of information within the network. They determine what data should be retained, updated, or discarded as the sequence is processed. This mechanism ensures that the network focuses on relevant details and can recall important information from earlier in the sequence.

GRUs have proven valuable in a variety of tasks that require understanding sequences, such as language processing, speech recognition, and more. Their ability to recognize patterns and relationships in sequential data makes them a popular choice for many applications in the realm of deep learning.

Text generation is one such application, but the manipulation of the initial sequence to guide the GRU’s output is not typically referred to as “prompt engineering.”

2.7 - Transformer Models

Imagine you’re reading a book, word by word. You understand each word, but you don’t understand the meaning of the whole sentence. You need to read the whole sentence to understand it. Similarly, you need to read the whole paragraph to understand the meaning of the whole paragraph. And you need to read the whole book to understand the meaning of the whole book. Naturally, this is how humans read and understand a book. The basic idea here is that you need to read the whole text to understand it, and it all starts with understanding each word and how it relates to the other words around it.

The transformer operates in a similar way. Instead of reading word by word, it can examine multiple words at once and understand their relationships. This is known as “attention”.

A few years ago, some researchers at Google devised a new method for reading text. They named it the “transformer”, and it was described in a paper titled ‘Attention Is All You Need’ by Ashish Vaswani and others from the Google Brain team.

Before this, there were other methods for reading and understanding text, such as something known as LSTM. However, this new transformer concept was faster and superior!

Here’s an example: Consider the sentence “The cat sat on the ___.” Even if you conceal the last word, you might guess it’s “mat” based on the preceding words. The transformer accomplishes this by examining all the words, understanding their relationships, and making an intelligent guess.

Now, this transformer concept has become extremely popular. People began using it not only for reading text but also for computer vision, biological sequence analysis, and more.

Due to the effectiveness of transformers, companies started constructing large models using them, which we refer to as “Large Language Models”. Two well-known ones are BERT and GPT.

2.7.1 - Bidirectional Encoder Representations from Transformers (BERT)

BERT is an advanced language model that comprehends words based on their entire context, taking into account both preceding and following words in a sentence. This bidirectional understanding enables BERT to grasp the specific meaning of words based on their context.

Developed using a neural network architecture known as the Transformer, BERT has been trained on vast amounts of text. During its training, some words in the text are intentionally hidden, and BERT attempts to predict them based on their context, aiding it in learning word relationships and meanings.

Developed by researchers at Google, BERT can be fine-tuned for various tasks. It’s commonly adapted for sentiment analysis, question answering, and identifying named entities such as people, places, or organizations in text.

Even though it’s possible to fine-tune it to be a language model, BERT is not typically used for text generation tasks mainly because of its bidirectional nature. Traditional LMs usually generate text in a unidirectional manner, predicting the next word based on previous words. BERT’s bidirectional nature makes it excellent for tasks like understanding and classifying existing text but less suited for generating coherent and fluent sequences of new text.

BERT is a high-capacity model designed to understand sophisticated text.

2.7.2 - Generative pre-trained transformer (GPT)

GPT is a language model that can generate text based on a given prompt. It is trained on a large amount of text and can generate text that is similar to the text it was trained on.

GPT-3 and GPT-4 are the latest versions of GPT. They are powerful language models with many parameters. They are built on the same architecture as GPT-2 but with some changes. GPT-3 and GPT-4 use a mix of dense and sparse attention patterns in their layers, which helps the model process information efficiently.

Unlike BERT, GPT is a high-capacity model designed to generate sophisticated text.

Prompt engineering is highly relevant to GPT-based models, as it can be used to guide the model’s output and produce more relevant and coherent text. When we talk about prompt engineering, we typically refer to the manipulation of the initial sequence to guide the GPT’s output. This involves carefully crafting the input to elicit a specific type of response or to steer the model’s behavior in a desired direction. The better the prompt, the more accurate and contextually relevant the output from GPT tends to be.

As GPT models, especially GPT-3 and GPT-4, have grown in size and capability, the importance of effective prompt engineering has also increased. This is because these models have a vast amount of knowledge and potential responses, so guiding them effectively can be the difference between a generic answer and a highly specific, relevant one. Moreover, with the right prompts, GPT models can perform tasks beyond mere text generation, such as answering questions, providing summaries, translating languages, and even some forms of reasoning.

Description

"LLM Prompt Engineering For Developers" begins by laying the groundwork with essential principles of natural language processing (NLP), setting the stage for more complex topics. It methodically guides readers through the initial steps of understanding how large language models work, providing a solid foundation that prepares them for the more intricate aspects of prompt engineering. As you proceed, the book transitions into advanced strategies and techniques that reveal how to effectively interact with and utilize these powerful models. From crafting precise prompts that enhance model responses to exploring innovative methods like few-shot and zero-shot learning, this resource is designed to unlock the full potential of language model technology. This book not only teaches the technical skills needed to excel in the field but also addresses the broader implications of AI technology. It encourages thoughtful consideration of ethical issues and the impact of AI on society. By the end of this book, readers will master the technical aspects of prompt engineering & appreciate the importance of responsible AI development, making them well-rounded professionals ready to focus on the advancement of this cutting-edge technology.

What you will learn

Understand the principles of NLP and their application in LLMs. Set up and configure environments for developing with LLMs. Implement few-shot and zero-shot learning techniques. Enhance LLM outputs through AutoCoT and self-consistency methods. Apply transfer learning to adapt LLMs to new domains. Develop practical skills in testing & scoring prompt effectiveness.

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