A Step-by-Step Guide to How to Get Started with Generative AI

Getting started with Generative AI can seem daunting, but it's easier than you think. You can start by understanding the basics, which involves recognizing that Generative AI is a type of machine learning that allows systems to generate new content.

The first step is to choose the right platform, and there are several options available, including Google Cloud AI Platform, Microsoft Azure Machine Learning, and Amazon SageMaker. These platforms offer a range of tools and services to help you get started.

To begin, you'll need to have a basic understanding of machine learning concepts, such as neural networks and deep learning. You can start by learning the basics of Python programming, which is a fundamental skill for working with Generative AI.

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Generative AI is a game-changer, and understanding its basics will help you get started. It's an AI-powered technology that uses extensive libraries of information to generate new things, like stories, pictures, videos, music, and software code.

Generative AI learns from large datasets to create human-like content, unlike traditional AI, which uses machine learning, predefined rules, and programmed logic to perform specific tasks. This means generative AI can create new data based on the provided datasets, adapt to context, and produce unique, creative content.

To put it simply, generative AI is all about creating new content, while traditional AI is about performing specific tasks. Here's a quick comparison:

This comparison highlights the key differences between traditional and generative AI, and understanding these basics will help you get started with generative AI.

Generative AI is an AI-powered technology that uses extensive libraries of information to generate new things, like stories, pictures, videos, music, and software code.

This technology leverages a very large corpus of data, including large language models like GPT-3, to generate new content.

Forrester describes it as “a set of technologies and techniques that leverage a very large corpus of data” to achieve this goal.

Generative AI can create a wide range of new content, from stories and pictures to videos and music.

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One key difference between traditional AI and generative AI is that traditional AI uses machine learning, predefined rules, and programmed logic to perform specific tasks, whereas generative AI learns from large datasets to create human-like content.

Generative AI models offer a wide range of possibilities, paving the way for innovative applications across various industries. By understanding the different types of generative AI, we can appreciate their unique capabilities and harness their potential to create groundbreaking solutions.

Some of the most common types of generative AI models include those that can generate new content such as stories, pictures, videos, music, and software code. These models use extensive libraries of information and large language models like GPT-3 to create new content.

Here are some of the most common types of generative AI models:

Generative AI can create new data based on the provided datasets, and it can also adapt to context and produce unique, creative content.

Generative AI starts with prompting, a natural language request sent to a model to elicit a response. A prompt can contain text, images, videos, audio, documents, and other modalities or even multiple modalities (multimodal).

Prompt design is a process of trial and error, but there are principles and strategies to nudge the model to behave in the desired way. Vertex AI Studio offers a prompt management tool to help manage prompts.

To tap into the wider abilities of AI, you need to familiarize yourself with the prompt, which converts your natural language questions into computations understood by the Large Language Model (LLM). Think of AI as having multiple assistants, each with different capabilities.

You can interact with LLMs using the language you've been using your entire life, and it makes sense that the LLM has been trained on billions of pieces of human knowledge and learned how we communicate. A remarkable aspect of the prompt is our ability to communicate with it like it was a human.

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A good prompt engineer is someone who can "explain stuff well", and you don't need an ML degree, computer science degree, or programming knowledge to be one. All you need to do is be able to explain things clearly.

When working with an LLM, you are starting from scratch, and the unique properties of the LLM have meant that context, knowledge, and form have all become orthogonal to each other. This provides an amazing amount of potential.

You can add context to the prompt by giving it examples of what you're looking for, which can take the following forms:

ChatGPT is an impressive AI tool developed by OpenAI, designed to generate high-quality, human-like text responses in the form of conversation. Its reliable and relatable nature has made it a go-to choice for businesses looking to automate and streamline their operations.

ChatGPT uses the GPT (Generative Pre-trained Transformer) architecture to understand and produce text, making it a prime example of how Generative AI can be applied to create conversational agents and other text-based applications.

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It's essentially an AI-powered chatbot that specializes in providing instant responses to user questions, generating content, and acting as a virtual assistant.

ChatGPT is best for creating written content, like articles, social media posts, emails, and software code. It can also produce brand assets like logos and marketing images.

Its natural language processing capabilities have made it a popular choice for businesses and individuals alike, allowing them to automate and streamline their operations while maintaining a high level of engagement with their customers.

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To get started with generative AI, you need to understand the basics of content generation. Generative AI models need to learn new tasks to perform useful tasks in real-world applications. This means they need to be customizable, which can be done through model tuning on platforms like Vertex AI.

To access external information, generative AI models need to be able to interact with the world outside of their training data. This is where features like grounding and function calling come in, allowing models to access information about specific topics or products.

To ensure safety and prevent misuse, generative AI models need to have safety filters to block potentially harmful content. Vertex AI has built-in safety features to promote responsible use of generative AI services.

Here are some key capabilities of generative AI models:

By understanding these capabilities, you can start to explore the possibilities of generative AI and how to use it to create useful content.

Generative AI models are trained on vast amounts of data to generate increasingly realistic content. These models can be accessed through a managed API, like Vertex AI, which offers a variety of foundation models.

You can explore Google models, as well as open models and models from Google partners, in Model Garden. This platform provides access to a range of models with different sizes, modalities, and costs.

Generative adversarial networks (GANs) are a type of generative model that work by training two neural networks on the same datasets. These networks, the generator and the discriminator, compete against each other to create increasingly realistic content.

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Foundation models are a type of generative AI model that can be used for response generation. These models are accessible through a managed API, and Vertex AI offers a variety of them.

You can explore Google models, as well as open models and models from Google partners, in Model Garden. This platform provides a range of options to choose from, each with its own unique characteristics.

The size and modality of these models can vary, and so can their cost. This means you can select the model that best fits your needs and budget.

Here are some key characteristics of Vertex AI's generative AI foundation models:

Model customization is a powerful tool that allows you to tailor the behavior of Google's foundation models to meet your specific needs. By customizing these models, you can simplify your prompts and reduce the cost and latency of your requests.

Model tuning is the process of customizing the default behavior of Google's foundation models. This process is essential for generating consistent results without using complex prompts.

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With model tuning, you can simplify your prompts and reduce the cost and latency of your requests. By doing so, you can also improve the overall performance of your model.

Here are some key benefits of model customization:

Vertex AI offers model evaluation tools to help you evaluate the performance of your tuned model. This ensures that your model is production-ready before deploying it to an endpoint.

GANs consist of two neural networks: a generator and a discriminator, which continuously compete to create highly realistic outputs.

The generator creates data samples, such as images or text, based on training data, while the discriminator evaluates the authenticity of these samples.

These networks improve over time, pushing each other to create better content, resulting in highly realistic outputs.

The discriminator analyzes the generator's output, determining if it's real or generated data, much like a Jedi inspects a lightsaber to see if it's genuine.

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The generator and discriminator play a game of cat and mouse, with the generator trying to fool the discriminator and the discriminator trying to identify fake data.

This competition drives the generator to create increasingly more realistic content over time, as it receives feedback from the discriminator.

Two networks, the generator and the discriminator, work together to generate increasingly more realistic content over time, through continuous competition and feedback.

Variational Autoencoders are a type of generative AI that can create new samples with similar characteristics to the original data.

VAEs use an encoder to identify essential features of the input data and compress it into a lower-dimensional space. This process is what allows VAEs to simplify and optimize the data points.

Generative AI images and text are often created using VAEs, which can pull data from a hidden storage area called a latent space and reconstruct it to resemble its original form.

VAEs are widely used for generating images, text, and music, making them a valuable tool in various applications.

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Transformer models have recently gained significant attention, primarily due to their success in natural language processing tasks.

These models rely on self-attention mechanisms, enabling them to capture complex relationships within the input data. This makes them incredibly powerful for generating high-quality text.

Transformer models, such as GPT-3, have numerous applications in chatbots, content generation, and translation. They can learn context and "transform" one type of input into a different type of output to generate human-like text and answer questions.

For example, Han Solo might type, "May the" and generative AI might suggest, "force be with you." This is made possible by the transformer model's ability to predict the next word in the typing sequence.

Here are some key benefits of using transformer models:

Flow-Based models are great at transforming complex data distributions into simple ones. They're often used for image generation.

These models take a set of inputs and transform them into a new, simpler distribution. Think of it like Anakin Skywalker arranging building blocks to create a pattern, where he must ensure the same number of blocks is always in balance.

Flow-Based models enable us to create new patterns or refine existing ones while maintaining the balance of the input data. This is crucial in image generation, where we want to create realistic and varied images.

The key idea behind Flow-Based models is to take a complex distribution and break it down into smaller, more manageable pieces. This makes it easier to work with and understand the data.

Recurrent neural networks are a type of generative AI model.

They're used to process and generate sequential data, which is data that has a specific order or sequence.

Training an RNN on data sequences generates new sequences that resemble learned data.

This means that RNNs can predict what comes next in a sequence based on what's occurred in previous sequences.

For example, Wicket the Ewok can catch a ball consistently because he's learned to anticipate the ball's path and predict where it will land based on all the previous throws.

RNNs are used in applications like Siri and Google Voice search.

They're able to generate new sequences that are similar to the data they've learned from, which is why they're so effective at predicting what comes next.

Recurrent neural networks are a powerful tool for working with sequential data.

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Large Language Models are a crucial part of generative AI, and understanding them can help you get started. You can customize the default behavior of Google's foundation models to consistently generate desired results without using complex prompts, a process called model tuning.

This customization process helps reduce the cost and latency of your requests by allowing you to simplify your prompts. Model tuning also enables you to evaluate the performance of your tuned model using Vertex AI's model evaluation tools.

If you're looking for more control over your language model, you can consider using an open-source LLM or training your own model from scratch. This might be a good option if you have a unique knowledge base or specific tasks that can't be met by commercial LLMs.

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A custom large language model is an option worth considering. You can either use an open source LLM or train your own model from scratch.

One reason to consider a custom LLM is if you have a knowledge base that's unlikely to be present in existing pre-trained language models. This could be a unique industry or a specific domain that's not well-represented in existing models.

You may also choose to host your own LLM for security reasons. This can restrict the risk that private information leaks out, as you're not passing data through a third-party API.

Inference costs of commercial LLMs can be a major concern for businesses. If you're finding that these costs are starting to not make business sense, a custom LLM might be a more cost-effective option.

Here are some scenarios where a custom LLM might be a good choice:

Fine-tuning an LLM for specific tasks is a great option when you need to adapt the model to tasks or domains beyond its pre-trained capabilities. This process allows you to teach the LLM new tasks, such as text summarization, virtual assistants, text generation within constraints, recommendation systems, and document classification.

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Fine-tuning can also help you better train the LLM to generate highly relevant text with fewer examples and fewer tokens needing to be passed into the prompt. This can result in significant savings, with Open AI reporting that the prompt length can be reduced by up to 90% while maintaining performance.

Training an existing LLM costs in the range of USD $1-5K, which is a much lower cost compared to the initial training set. With fine-tuning, you only need 100-300 high-quality examples, which can give you a linear increase in performance.

If the initial training set doesn’t provide the results you are looking for, doubling the size of the examples will give you a linear increase in performance. This is a great way to adjust and refine your model without breaking the bank.

Open AI has recently enhanced their GPT 3.5-turbo model to allow for fine-tuning by customers, which is a powerful feature that can help you achieve your goals. Later this year, they’ll release this feature for the GPT-4 model as well.

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To get started with generative AI, you'll want to explore popular tools and applications. ChatGPT, Bard, and Dall-E are making significant impacts for curious early adopters worldwide.

These tools can create realistic and coherent outputs across various applications. I've personally benefited from ChatGPT for coding and Python, which has been incredibly helpful for staying on track.

It's essential to note that company or proprietary data should not be used in any interaction with LLMs, as it can be used in future training runs. Having this as a hard rule removes a lot of risk and allows for exploration.

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ChatGPT is a form of Generative AI that can be used by teams to access tools and speed up coding and increase productivity.

I've benefited from ChatGPT immensely for this exact use case, especially when I was rusty with Python.

Bard is another interesting generative AI tool that focuses on helping users generate creative and engaging written content.

With its confident and smart approach, Bard can assist writers in overcoming writer’s block, brainstorming ideas, and even writing full-length articles, stories, or blog posts.

Google offers two generative AI models, PaLM, and Google Bard, with Bard being a conversational generative AI chatbot created by Google as a competitor to ChatGPT.

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API integration is a game-changer for building applications that use large language models (LLMs) like ChatGPT, Bard, and Claude 2. These APIs have rich documentation and low barriers to entry, making it easy to get started.

You can integrate your applications with third-party LLMs using these APIs, and only pay per usage. This means you can choose the API that best fits your needs and budget.

With API integration, you can also integrate custom or private data into the LLM prompt via Semantic Search and Embeddings, powered by a vector database. This enables you to get more relevant results, even if the query doesn't contain the exact words that are present in the documents.

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Semantic search uses word embeddings to compare the meaning of a query to the meaning of the documents in their index. Embeddings are numerical representations of objects, such as words, sentences, or entire documents, in a multi-dimensional space.

By placing similar items closer together in vector space, embeddings enable us to evaluate the relationship between different entities. For example, concepts like "cat" and "dog" are quite close to each other, while "vehicle" and "car" are far removed from "organic life".

Generative AI can automate a wide range of tasks in marketing automation, from creating personalized email campaigns to optimizing product recommendations.

By analyzing data from multiple sources, generative AI algorithms can identify patterns and preferences, and create tailored content that is more likely to resonate with customers.

AI has revolutionized the world of ecommerce marketing by providing companies with the tools needed to create more effective campaigns.

Generative AI can help businesses optimize their advertising spend by identifying which channels and messages generate the best returns.

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AI-powered chatbots are now widely used by ecommerce businesses to provide instant and personalized support to customers.

These chatbots can handle a wide range of customer queries, from tracking orders to answering FAQs, without the need for human intervention.

AI-powered marketing automation tools can also help businesses improve their targeting capabilities by analyzing data on customer behavior, preferences, and demographics.

Generative AI can create personalized campaigns that are more likely to drive sales and increase customer engagement.

By analyzing user data, these algorithms can now create personalized campaigns that are more likely to resonate with customers and lead to higher conversion rates.

Using generative AI technology, businesses can take data from multiple touchpoints, including social media, email campaigns, and website interactions, to create a holistic picture of the customer journey.

Generative AI uses advanced data analysis tools to collect, analyze, and interpret data gathered from customer interactions and buying behaviors.

Algorithms are then developed to identify similar patterns and trends, enabling the creation of highly accurate and personalized consumer recommendations.

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The more data that is collected by the algorithms, the more refined the recommendations become, allowing businesses to create dynamic product recommendations and offers that speak directly to each customer.

Generative AI algorithms can analyze large amounts of data in real time, allowing businesses to quickly respond to changing consumer trends and market conditions.

Establishing a culture of responsible AI is crucial for enterprise success. This involves governance, guardrails, prototype delivery systems, change management, and prioritization of use cases.

To ensure responsible AI, auditing mechanisms are essential, especially when introducing external knowledge sources. Auditing will help businesses develop and deploy policies to protect against risks such as copyright infringement and proprietary data leakages.

Here are the six best practices for running generative AI successfully:

In the world of customer service, generative AI is a game-changer. With instant, automated answers to customer inquiries, your support team can manage high volumes of requests during peak times.

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Generative AI allows for more natural, personalized conversations with accurate information. This results in a better customer experience, higher customer satisfaction (CSAT) scores, and customer loyalty. Generative AI also provides multilingual support, recognizing and adapting to languages for 24/7 global customer service.

Using AI for customer service makes it easy for your support team to create an exceptional customer experience with more human-like interactions. By leveraging generative AI, you can provide personalized support and create engaging self-service content in your knowledge base.

A great way to see value with generative AI is using this technology to structure, summarize, and auto-populate tickets. This helps your support team resolve customer requests faster, allowing human agents to focus on the rewarding tasks that require their empathy and strategic thinking.

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New team members can get help with response phrasing, allowing them to type a few words and generative AI can predict the rest of the sentence, filling in the blanks with the right information.

This feature is especially helpful for agents who still need to learn the company's return policy, allowing them to quickly and accurately reply to customers.

Generated ticket summaries provide new agents with the most relevant information in the conversation, lessening their learning time and making it easier to get up to speed.

With these generative AI tools, businesses can reduce training time and get support agents up to speed more quickly.

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As you navigate the world of enterprise and generative AI, it's essential to follow best practices that ensure responsible and successful implementation. Establishing a culture of responsible AI is crucial, with a focus on governance, guardrails, prototype delivery systems, change management, and prioritization of use cases.

Auditing is also a must-have, especially when introducing external knowledge sources to improve context. This helps businesses develop and deploy policies to protect against risks such as copyright infringement and proprietary data leakages.

Centers of excellence play a vital role in organizing and cleaning data, which is often a major challenge in AI. By upskilling employees in generative AI, these centers can learn to adjust prompts and finetune outputs to address inaccurate and biased results.

To balance employee enthusiasm with responsible AI practices, it's essential to democratize ideas while limiting production. This means allowing employees to experiment with generative AI without the ability to operationalize it, then using a center of excellence as a change management hub to design, integrate, and scale prototypes into enterprise-grade solutions.

Dynamic data handling is also a significant challenge, especially with generative AI creating vast amounts of dynamic data. Enterprise leaders must work with agility to streamline data sources, talent, and technology.

Finally, business executives need to be involved in the exploration of generative AI, as they have the closest connection to end customers and can drive innovation and ambition. By encouraging business leaders to take an active role, you can unlock the full potential of generative AI in your enterprise.

Here are the six best practices for enterprise summarized: