The Beginner Generative AI Handbook – Book review

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The book "The Beginner Generative AI Handbook" by Jordan Blake, published in 2025, explains the principles and tools that can help those starting their journey in the world of generative AI.

Today, when we all know how to operate and utilize these tools, why do we need to read a beginner's book?

So, first of all, it also explains what's behind the scenes. And that's important.

Second, it organizes existing understanding and supplements knowledge.

Main topics:

• Introduction

• Uses

  • Conversation management

  • Design, creation, and art

  • Business innovation

  • Career development

• Responsible AI

• Equal opportunities and diversity

• The future of AI: trends and innovations

This review is not a substitute for the book, although it serves as a guide for those who haven't read it yet and as a helpful reminder for those who have already done so.

Hope this will be helpful to you!

Introduction

Basic Terms:

1. Artificial Intelligence: Automated solutions with the ability to mimic human decision-making and problem-solving.

2. Supervised Learning: Machine learning solutions based on data accompanied by labels for metadata purposes. [Unsupervised learning... learning that operates on data without the need for labels].

3. Algorithms: Work instructions, or according to Blake, "the secret recipe" that enables offering solutions based on data. Simple algorithms are decision trees (if-then); more complex algorithms include regression analysis, neural networks, and more.

4. Data: The basic material, without which the machine has no value. Requires preprocessing (refinement) to generate a value.

5. Neural Network: A collection of data layers that transfer information between them.

6. Machine Learning: A sub-discipline of artificial intelligence that focuses on systems that learn from data.

7. Deep Learning: A sub-discipline of machine learning based on multi-layered neural networks to analyze complex patterns.

8. Generative AI: Artificial intelligence solutions aimed at creating content, whether text, images, musical notes, or any other format.

Typical Models of Generative AI

1. GANs - Generative Adversarial Networks: The GAN operates as a pair of machines where one generates infinite solutions while the other filters and selects the few good ones among them. The filtering also serves as a learning tool for the first machine, facilitating continuous improvement of its outputs. An example use could be creating art images.

2. VAEs - Variational Autoencoders: The VAE operates to understand content, process it, and recreate it in a simpler and more condensed way in terms of content scope. Suitable for various uses ranging from file compression to music remixing and much more in between.

3. Autoregressive Models - Operate on data step by step, and at each step suggest the next step. A typical use would be story writing, where each set of words written serves as input for selecting the sentence continuation and the beginning of the next sentence.

Summary - Machine learning in general and generative AI in particular are implemented through models. These operate on refined data and their combination with algorithms in a smart way. Through the implementation and combination of algorithms, while selecting and refining data optimally, the model is created. The development process for accuracy and refinement is called "training" in machine learning (ML).

Uses

Generative AI can serve a very wide range of needs. Blake chooses several main parent uses, which he details. These not only make work more efficient, but also improve outputs. The ideal is collaborative teamwork (human-machine). Nevertheless, they catalyze the democratization of many fields that were until today the domain of the few.

Conversation Management General conversation management is the basic use of generative AI.

It includes:

• Writing an initial prompt

• Examining the response, reflection, and improving the writing

• Personal customization: adding role, target audience, context, purpose, and style

• Conversation scenario

• Problem solving (dealing with misunderstandings, errors, and hallucinations)

• Creative aspects to dialogue (for example, requesting the addition of humor)

• Managing complex requests (multi-layered requests)

Key tools: OpenAI ChatGPT, Google Gemini

Design, Creation, and Art:

Generative AI can facilitate the creation of collaborative art with individuals from diverse fields. The role of AI includes both streamlining task execution and creation itself, as well as enhancing creativity.

• Music. For example: remix, accompaniment, arrangement, mixing musical styles, and more.

• Fiction. For example: suggesting plot ideas, writing a skeleton, writing passages, improving writing, and more.

Key tools: Jasper AI, Grammarly.

• Design thinking: creative ideas, promoting processes according to different models, user experience planning, and more.

Key tools: Adobe Sensei, Canva.

Business Innovation

Business innovation is more than just implementing tasks in the subjects detailed above. Beyond aspects of responsible AI (full chapter below), attention must be paid to:

1. Breaking down organizational information silos, since information is the foundation on which AI can flourish.

2. Human maturity and reducing concerns (ML)

3. Graduated strategy for implementation

Typical areas where business activity can become more efficient or improve:

• Work management. For example: finding meeting times, allocating tasks according to workload, and more.

• Customer service. For example: A Chatbot that provides answers to questions 24/7, improving customer experience, sentiment analysis of customer communication, and more.

• Operations. For example: inventory management, preventive maintenance planning, sustainability, and more.

• Health. For example, improving diagnostic accuracy and anomaly detection.

• Growth and business development. For example, promoting innovation while consulting and utilizing AI for implementation, market analysis, and forecasting, as well as competitor analysis.

Career Development

Career development in the renewed job market requires knowledge of artificial intelligence capabilities. This doesn't mean everyone will work as model developers, algorithm developers, or programmers; however, one must be familiar with the basic concepts of databases, algorithms, and models, learn about tools and their usage, and deepen their understanding of the ethical use of AI. It's recommended to regularly invest time in lifelong learning during life and work, and adopt adaptability capabilities, while understanding that everything is constantly changing. One should not fear being replaced by AI machines, because success will come from the synergy of the combination. One must understand that AI is simultaneously both a threat and an opportunity, and recognizing this opportunity is key to adopting a growth mindset and achieving success in implementation. And how does one learn? Through courses, through reading, through conferences, and through participating in networks of those engaged in the subject.

Responsible AI

The responsibility for responsible AI belongs to all of us: regulators (government), industry leaders, organizations, and each individual.

The responsibility includes addressing several components:

• Biases - stem mainly from data on which models rely that are biased in their content, since they are not sufficiently representative.

• Individual data privacy stems from the existence of extensive information and data about all of us on the network, as well as the sharing of limited data in cloud environments where AI operates.

• Fairness and accountability - fairness refers to avoiding discriminatory use or preference. In contrast, accountability refers to the need for development and use that takes responsibility for errors and works to correct them.

Ways of action:

• Awareness and knowledge (ML)

• Promoting ethical policy for artificial intelligence issues. In developing such a policy, it's advisable to include as diverse a range of voices as possible to reduce bias and increase fairness.

• Ongoing monitoring and control, to search for problems, overcome them, and find future ways to prevent them.

Equal Opportunities and Diversity

When discussing diversity, the intention is not just to collect diverse data. Blake, beyond references throughout the book, dedicates a specific chapter to the importance of equal opportunities and ensuring diversity:

1. Among workers whose specialization is related to artificial intelligence. Blake refers to the importance of equality in aspects of:

   1. Gender (only 22% women today)

   2. Culture

2. In making applications accessible to people with different needs.

3. In listening and considering diverse voices in everything related to decision-making and the use of AI.

Equal opportunities are not only intended to benefit those whose share today is relatively low, but also for the benefit of society as a whole. Artificial intelligence development and implementation benefit significantly from diversity, as it is a catalyst for innovation.

It is the duty of those who already control the market to help others integrate, whether through guidance, mentoring, or any other means.

The Future of AI: Trends and Innovations

Artificial intelligence technologies are constantly developing at a dizzying pace.

Technological development directions worth following:

• Edge Computing: Computing that approaches the endpoints of devices, sensors, and IoT in general.

• Natural Language Processing (NLP): Natural language processing. Constitutes not only a basis for interfaces between AI and people, but also for models and leveraging understanding capability from information.

• Quantum Computing: A multidisciplinary discipline including quantum physics, mathematics, and computer science, enabling the development of computers based on quantum phenomena such as superposition and entanglement. These computers are capable of performing specific calculations in parallel and with much greater efficiency than classical computers. They are particularly suitable for processing enormous amounts of information and complex computational problems.

AI will impact work, household management, media, entertainment, and society as a whole.

Blake repeatedly emphasizes that the purpose of artificial intelligence is not to replace people, but to enhance their capabilities and decision-making abilities. To succeed in navigating the new world, we must be flexible and adaptable.

Learn. Connect. Try to be creative and innovative in solving problems and capitalizing on opportunities. Our future will be based on artificial intelligence, and we can take it to wonderful places.

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Want to learn more about applied generative AI?

Here are some articles you might find interesting:

• Personalization in the Age of Artificial Intelligence

• Conversational Gen AI as a Tool in Service Centers

• Artificial Intelligence Creates, Learns, and Acts: On Agentic AI