Coding with ChatGPT and Other LLMs: Navigate LLMs for effective coding, debugging, and AI-driven development

Coding with ChatGPT and Other LLMs

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Contributors

About the author

Dr. Vincent Austin Hall is a computer science lecturer at Birmingham Newman University and CEO of Build Intellect Ltd, an AI consultancy. Build Intellect works closely with ABT News LTD, based in Reading, England. He holds a physics degree from the University of Leeds, an MSc in biology, chemistry, maths, and coding from Warwick, and a PhD in machine learning and chemistry, also from Warwick, where he developed licensed software for pharma applications. With experience in tech firms and academia, he’s worked on ML projects in the automotive and medtech sectors. He supervises dissertations at the University of Exeter, consults on AI strategies, coaches students and professionals, and shares insights through blogs and YouTube content.

I would like to thank my supportive and patient family: my excellent and wise partner Anna, our brilliant, different, and loving son Peter and our brilliant, inventive, and hilarious daughter Lara, for allowing me time to work on this book over many weekends and evenings and understanding that good things take long, hard work, and many iterations.

Thank you to Packt Publishing: Editor Joseph Sunil for making only good suggestions and improving my work; Book Project Manager, Aparna Nair for keeping the project progressing well and making sure everything got done; Publishing Product Manager, Nitin Nainani for managing and further direction; Priyanshi J for bringing me on board and suggesting this book in the first place; as well as the technical reviewers for helping Joseph and me to keep the book quality high.

Thanks to my business partner, Chief Chigbo Uzokwelu, CEO of ABT News Ltd, for lots of support in friendship and business: legal, sales, business communications, proof reading, and marketing.

Thanks to the reader for reading and learning, sharing what you've learned and helping others to upskill and create the best code, careers and solutions for Earth (and future populated worlds).

About the reviewers

Parth Santpurkar is a senior software engineer with over a decade of industry experience based out of the San Francisco Bay area. He’s a senior IEEE member and his expertise and interests range from software engineering and distributed systems to machine learning and artificial intelligence.

Sougata Pal is a passionate technology specialist performing the role of an enterprise architect in software architecture design and application scalability management, team building, and management. With over 15 years of experience, they have worked with different start-ups and large-scale enterprises to develop their business application infrastructure, enhancing their reach to customers. They have contributed to different open source projects on GitHub to empower the open source community. For the last couple of years, they have playing around with federated learning and cybersecurity algorithms to enhance the performance of cybersecurity processes by introducing concepts of federated learning.

Table of Contents

Preface

Part 1: Introduction to LLMs and Their Applications

1

What is ChatGPT and What are LLMs?

Introduction to LLMs

Origins of LLMs

Early LLMs

GPT lineage

BERT

LaMDA

LLaMA‘s family tree

Exploring modern LLMs

GPT-4

LLaMA-2

Gemini (formerly Bard)

Amazon Olympus

How Transformers work

How an LLM processes a piece of text

ChatGPT uses reinforcement learning from human feedback

LLMs are expensive

A note on the mathematics of LLMs

Applications of LLMs

Summary

Bibliography

2

Unleashing the Power of LLMs for Coding: A Paradigm Shift

Technical requirements

Unveiling the advantages of coding with LLMs

The short version

The longer version

Planning your LLM-powered coding

1. Understanding your purpose – unveiling the why

2. Identifying your audience – tailoring the experience

3. Defining the environment – where your code calls home

4. Mapping user interaction – charting the navigation flow

5. Identifying data sources – feeding the machine learning beast

6. What data format?

7. How will you plumb in the data?

8. Visualizing the interface

Getting into LLM-powered coding

Back to the HTML code for Prompt 5

Back to the Flask code for Prompt 5

Making it work for you

Summary

3

Code Refactoring, Debugging, and Optimization: A Practical Guide

Technical requirements

Dealing with error codes – debugging

Prompt 4 debugging

Prompt 5 debugging – HTML

Prompt 5 debugging – Python/Flask

Where’s the code?

Refactoring code

Refactoring code with Claude 3

Documenting code

Let’s get ChatGPT and to explain some code

Testing code

How do you test code?

Virtual software companies

Agents

Relevance to virtual software companies?

ChatDev

Summary

Part 2: Be Wary of the Dark Side of LLM-Powered Coding

4

Demystifying Generated Code for Readability

Technical requirements

Generating more readable code

Introduction to data compression methods

Code to compress data, written in Python 3.10

Let’s look at some well-written code

What makes code hard or easy to read?

Why is reading code hard?

Dos and don’ts of readable code – how to make readable code

Summarizing code for understanding

Generating documentation

Documentation for crypto_price_and_indicators.py

Summary

Bibliography

5

Addressing Bias and Ethical Concerns in LLM-Generated Code

Technical requirements

Understanding bias in LLM-generated code

Where does bias in LLMs come from?

Examining ethical dilemmas – challenges in LLM-enhanced working

Meta AI, or Meta Llama 3

ChatGPT on international security measures

Racist Gemini 1.5

Detecting bias – tools and strategies

Biases you might find in code and how to improve them

Analyzing the training data

Examining the code

Preventing biased code – coding with ethical considerations

Get good data

Ethical guidelines

Create transparent and explainable code

Code reviews

Your inevitable success

Examples of getting the balance right

Summary

Bibliography

6

Navigating the Legal Landscape of LLM-Generated Code

Technical requirements

Unraveling copyright and intellectual property considerations

The EU – needs the human touch

The UK – human creativity and arrangements necessary for the creation

The USA – no ownership of AI-generated works

The People’s Republic of China – whoever made the greater contribution

Taiwan – human creative expression

India and Canada – human author’s skill and judgment

Australia – to the person making the necessary arrangements

Japan – copyright requires human authorship

South Korea

Brazil – human authorship required

Indonesia – human authorship needed

Evolving legal landscape

Precedent

Addressing liability and responsibility for LLM-generated code

Licensing

Attribution and credit

Quality and reliability

Ethical considerations

Product liability

Use case restrictions

Security concerns

Transparency and explainability

Third-party dependencies

Use good communication to avoid legal action

Code of ethics when using AI

Accountability and redress mechanisms

Examining legal frameworks governing the use of LLMs in coding

UN resolution on AI

EU – the European Parliament adopts the AI Act

California AI kill switch bill proposed

AI Acts of other countries

Other regulations

Possible future of the regulation of AI-generated code

Key points moving forward

Questions that should still be answered

Keep up to date

Summary

Bibliography

7

Security Considerations and Measures

Technical requirements

Understanding the security risks of LLMs

Data privacy and confidentiality

Security risks in LLM-generated code

Implementing security measures for LLM-powered coding

Input sanitization and validation

Secure integration patterns

Monitoring and logging

Version control and traceability

Encryption and data protection

Regular security assessments

Incident response planning

Bonus – training

Who can help here?

Best practices for secure LLM-powered coding

Making the future more secure

Emerging threats

Shifting focus

Summary

Bibliography

Part 3: Explainability, Shareability, and the Future of LLM-Powered Coding

8

Limitations of Coding with LLMs

Technical requirements

Inherent limitations of LLMs

Core limitations

Other limitations to LLMs

Evaluating LLM performance

Overcoming inherent limitations

Challenges in integrating LLMs into coding workflows

Relevant workflow example

Security risks

IP concerns

Dependency management

Explainability

Future research directions to address limitations

Continuous learning

Novel architectures

Computational efficiency

Specialized training

Summary

Bibliography

9

Cultivating Collaboration in LLM-Enhanced Coding

Technical requirements

Why share LLM-generated code?

Benefits of sharing code

Real-world examples

Best practices for code sharing

Documentation

Consistent coding standards

Version control

Code security best practices

Proper attribution

Test the code thoroughly

Continuous improvement

Knowledge management – capturing and sharing expertise

Creating knowledge repositories

Conducting regular knowledge-sharing sessions

Peer mentorship – sharing the wisdom

Making the best use of collaborative platforms

Code review tools

Project management software

Communication channels – keeping the conversation flowing

Summary

Bibliography

10

Expanding the LLM Toolkit for Coders: Beyond LLMs

Technical requirements

Code completion and generation tools

Eclipse’s Content Assist

PyCharm’s code completion

NetBeans’ code completion

VS Code’s IntelliSense

SCA and code review tools

SonarQube

ESLint

PMD

Checkstyle for Java

Fortify Static Code Analyzer

CodeSonar

Coverity

FindBugs/SpotBugs

Bandit

HoundCI

Testing and debugging tools

Jest

Postman

Cypress

Selenium

Mocha

Charles Proxy

Summary

Bibliography

Part 4: Maximizing Your Potential with LLMs: Beyond the Basics

11

Helping Others and Maximizing Your Career with LLMs

Why Mentor Others in LLM-powered coding?

Mentoring in the time of LLMs

The Ripple Effect of Mentorship

Elevating Standards in the Field

Personal Growth Through Mentorship

Supporting a Culture of Continuous Learning

Section Summary

Other Ways to Share Your Expertise and Work

Blogging and Writing Articles

Online Courses

Open-Source Projects

Running Workshops

Social Media and Online Communities

Section Summary

Attend, Build, Network

Speaking Engagements and Workshops

Joining Professional Organizations

Network with Peers and Experts

Building Genuine Relationships

Seeking Mentorship and Offering Support

Section Summary

New Approaches from LLMs

Embracing Collaborative Coding

Latest Developments in LLMs

Section Summary

Summary

Bibliography

12

The Future of LLMs in Software Development

Technical requirements

Emerging trends in LLM technologies

Multimodal LLMs

Human-AI collaboration

Multi-agent systems

Generative business intelligence (Gen BI)

Your wish is my command

Future impacts

Democratization of coding and more

Feedback loop

Harmful AI?

Coming challenges and opportunities

Legal

Politics and government

No jobs for humans?

Scale to the stars, literally

Human directed

Summary

Like my ideas or what to change them?

Bibliography

Index

Other Books You May Enjoy

Preface

In this age of the AI Revolution, you cannot achieve goals entirely with human power.

Automation is thousands of times faster and accelerating extremely quickly! Software ate the world and created AI. Now AI is eating the world and recreating it better. The best way to create is a fusion of human and machine powers.

In Coding with ChatGPT and Other LLMs, you will learn how coding is best achieved today. You can learn how to find and effectively use the most advanced tools for code generation, architecting, description and testing while staying out of legal hassles, advancing your career faster and helping others around you to improve too. After reading this book, its prompts and its code, you should understand likely futures for this kind of technology. You’ll also be able to generate your own ideas about how to improve the world, and have the power to do that.

Who this book is for

This book is for new coders and experienced coders, software engineers, software developers, scientists doing scientific computing. If you want a career in coding or software, this book is for you. The book helps with ethics, bias, security, or the future impacts of AI, this book is for you.

If you are a lawyer concerned with the legal issues of AI and code, you’d do well to read this book.

What this book covers

Chapter 1

, What is ChatGPT and What are LLMs?, introduces Large Language Models (LLMs) like ChatGPT and Claude. It explains how these models function and explores their applications through real-world examples.

Chapter 2

, Unleashing the Power of LLMs for Coding: A Paradigm Shift, explores how LLMs can revolutionize software development by generating code. It introduces effective prompt strategies, highlights common pitfalls to avoid, and emphasizes the importance of iterative refinement for optimal results

Chapter 3

, Code Refactoring, Debugging, and Optimization: A Practical Guide, delves into the essential tasks of refining code. It covers debugging to ensure functionality, refactoring to improve structure or adapt functionality, and optimizing for speed, memory usage, and code quality. The chapter demonstrates how LLMs can assist in these processes, providing practical strategies for effective AI-powered coding.

Chapter 4

, Demystifying Generated Code for Readability, emphasizes the importance of writing clear, understandable code. It highlights how code that makes sense to its author may not be easily grasped by others—or even by the author at a later time. This chapter demonstrates how LLMs can help improve code readability by enhancing documentation, clarifying functions and libraries, and fostering practices that make the codebase more accessible for collaborators and your future self.

Chapter 5

, Addressing Bias and Ethical Concerns in LLM-Generated Code, explores how biases can arise from the data used to train LLMs, implicit assumptions in prompts, or developer expectations. It provides strategies to identify hidden biases and correct them to ensure fair and responsible code generation.

Chapter 6

, Navigating the Legal Landscape of LLM-Generated Code, discusses potential legal challenges related to biases, code reuse, copyright issues, and varying regulations across jurisdictions. This chapter equips you with the knowledge needed to address legal risks and ensure compliance when using LLM-generated code.

Chapter 7

, Security Considerations and Measures, focuses on safeguarding your software from vulnerabilities. It highlights security risks that may emerge in LLM-generated code and provides best practices for identifying, mitigating, and preventing potential threats.

Chapter 8

, Limitations of Coding with LLMs, addresses the boundaries of what LLMs can achieve. It explores their challenges in grasping the subtleties of human language and their limitations in handling complex coding tasks. The chapter also examines the inconsistencies and unpredictabilities inherent in LLM-generated outputs, helping readers set realistic expectations.

Chapter 9

, Cultivating Collaboration in LLM-Enhanced Coding, promotes a culture of openness and collaboration in software development. It offers best practices for sharing code generated by LLMs and the knowledge that accompanies it, fostering transparency and teamwork. Readers will discover strategies to ensure the expertise encoded within LLM-generated solutions is effectively shared and utilized across development teams.

Chapter 10

, Expanding the LLM Toolkit for Coders: Beyond LLMs, explores how non-LLM AI tools can complement LLM-powered coding. It highlights tools for code writing, analysis, and testing, detailing their capabilities and limitations. This chapter provides strategies for integrating these tools into a well-rounded coding toolkit to enhance productivity and maximize efficiency.

Chapter 11

, Helping Others and Maximizing Your Career with LLMs, focuses on contributing to the LLM coding community through teaching, mentoring, and knowledge-sharing. It offers guidance on how to advance the field by sharing expertise and explores ways to leverage LLM-generated coding skills for career growth and new opportunities.

Chapter 12

, The Future of LLMs in Software Development, looks ahead to emerging trends and developments in LLM technology. It reflects on how these advancements will shape the future of software development and examines the broader impact of automated coding on society, including potential implications for future communities.

To get the most out of this book

Assumed knowledge: some basic coding skills, an interest in software and or AI.

Try to apply what you’ve learned here, and share your code and your recent learnings and experience with others and learn from them.

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Conventions used

There are a number of text conventions used throughout this book.

Code in text: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and X handles. Here is an example: "Next, we have Prompt 5 as a Flask app (app.py) with Python code."

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ax.plot(data)

ax.set_xlabel(Index)

ax.set_ylabel(Value)

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Bold: Indicates a new term, an important word, or words that you see onscreen. For instance, words in menus or dialog boxes appear in bold. Here is an example: You'd have to click the first button, Click me, to get the pop-up window again.

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Part 1: Introduction to LLMs and Their Applications

This section lays the groundwork for understanding Large Language Models (LLMs) and their transformative potential across various fields. It introduces LLMs like ChatGPT, explaining how they work. We will also explore different ways that LLMs are applied across industries, from customer service to content generation. We will also check out the unique capabilities of LLMs in software development.

This section covers the following chapters:

Chapter 1

, What is ChatGPT and what are LLMs?

Chapter 2

, Unleashing the Power of LLMs for Coding: A Paradigm Shift

Chapter 3

, Code Refactoring, Debugging, and Optimization: A Practical Guide

1

What is ChatGPT and What are LLMs?

The world has been strongly influenced by the recent advancements in AI, especially large language models (LLMs) such as ChatGPT and Gemini (formerly Bard). We’ve witnessed stories such as OpenAI reaching one million users in five days, huge tech company lay-offs, history-revising image scandals, more tech companies getting multi-trillion dollar valuations (Microsoft and NVIDIA), a call for funding of $5–7 trillion for the next stage of technology, and talks of revolutions in how everything is done!

Yes, these are all because of new AI technologies, especially LLM tech.

LLMs are large in multiple ways: not just large training sets and large training costs but also large impacts on the world!

This book is about harnessing that power effectively, for your benefit, if you are a coder.

Coding has changed, and we must all keep up or else our skills will become redundant or outdated. In this book are tools needed by coders to quickly generate code and do it well, to comment, debug, document, and stay ethical and on the right side of the law.

If you’re a programmer or coder, this is for you. Software, especially AI/machine learning, is changing everything at ever-accelerating rates, so you’ll have to learn this stuff quickly, and then use it to create and understand future technologies.

I don’t want to delay you any longer, so let’s get into the first chapter.

In this chapter, we’ll cover some basics of ChatGPT, Gemini, and other LLMs, where they come from, who develops them, and what the architectures entail. We’ll introduce some organizations that use LLMs and their services. We’ll also briefly touch on some mathematics that go into LLMs. Lastly, we’ll check out some of the competition and applications of LLMs in the field.

This chapter covers the following topics:

Introduction to LLMs

Origins of LLMs

Early LLMs

Exploring modern LLMs

How transformers work

Applications of LLMs

Introduction to LLMs

ChatGPT is an LLM. LLMs can be used to answer questions and generate emails, marketing materials, blogs, video scripts, code, and even books that look a lot like they’ve been written by humans. However, you probably want to know about the technology.

Let’s start with what an LLM is.

LLMs are deep learning models, specifically, transformer networks or just "transformers." Transformers certainly have transformed our culture!

An LLM is trained on huge amounts of text data, petabytes (thousands of terabytes) of data, and predicts the next word or words. Due to the way LLMs operate, they are not perfect at outputting text; they can give alternative facts, facts that are hallucinated.

ChatGPT is, as of the time of writing, the most popular and famous LLM, created and managed by OpenAI. OpenAI is a charity and a capped-profit organization based in San Francisco [OpenAI_LP, OpenAIStructure].

ChatGPT is now widely used for multiple purposes by a huge number of people around the world. Of course, there’s GPT-4 and now GPT-4 Turbo, which are paid, more powerful, and do more things, as well as taking more text in prompts.

It’s called ChatGPT: Chat because that’s what you do with it, it’s a chatbot, and GPT is the technology and stands for generative pre-trained transformer. We will get more into that in the GPT lineage subsection.

A transformer is a type of neural network architecture, and a transformer is the basis of the most successful LLMs today (2024). GPT is a Generative Pre-trained Transformer. Gemini is a transformer [ChatGPT, Gemini, Menon, HuggingFace]. OpenAI’s GPT-4 is a remarkable advancement in the field of AI. This model, which is the fourth iteration of the GPT series, has introduced a new feature: the ability to generate images alongside text. This is a significant leap from its predecessors, which were primarily text-based models.

OpenAI also has an image generation AI, DALL-E, and an AI that can connect images and text and does image recognition, called CLIP (OpenAI_CLIP). The image generation capability of DALL-E is achieved by training the transformer model on image data. This means that the model has been exposed to a vast array of images during its training phase, enabling it to understand and generate visual content [OpenAI_DALL.E].

Furthermore, since images can be sequenced to form videos, DALL.E can also be considered a video generator. This opens up a plethora of possibilities for content creation, ranging from static images to dynamic videos. It’s a testament to the versatility and power of transformer models, and a glimpse into the future of AI capabilities.

In essence, tools from OpenAI are not just text generators but a comprehensive suite of content generators, capable of producing a diverse range of outputs. It’s called being multi-modal. This makes these tools invaluable in numerous applications, from content creation and graphic design to research and development. The evolution from GPT-3 to GPT-4 signifies a major milestone in AI development, pushing the boundaries of what AI models can achieve.

Origins of LLMs

Earlier neural networks with their ability to read sentences and predict the next word could only read one