Key Concepts in Generative AI: A Deep Dive (Part 2 – Examples and Use Cases)

In Part 1, we explored key concepts in Generative AI, such as transformers, fine-tuning, tokenization, retrieval-augmented generation (RAG), and vector search. In Part 2, we’ll demonstrate how these concepts work in practice, with examples and sample data to give you a hands-on understanding.

Example 1: Tokenization and Transformers in Text Generation

Tokenization is a fundamental step in generative AI for text. Let’s explore how a simple sentence is tokenized and processed by a transformer model, such as GPT.

Sample Input:

- Sentence: "Generative AI is transforming industries."

Tokenization:

Using Byte-Pair Encoding (BPE), we break the sentence into smaller tokens:

["Gener", "ative", " AI", " is", " transform", "ing", " industries", "."]        

These tokens are mapped to numeric IDs, which the model can process:

[5123, 8712, 289, 97, 7218, 482, 1471, 11]        

Transformer Model Example:

The transformer model processes these tokens using self-attention. At each layer, the model weighs the importance of different tokens relative to each other:

"AI" might pay more attention to "transforming industries" to understand context.

Once the transformer processes these relationships across multiple layers, it can generate a continuation of the text.

Output:

The model might generate:

"Generative AI is transforming industries by automating tasks, enhancing creativity, and driving innovation."

In this case, the transformer has extended the input sentence based on the learned patterns from vast amounts of text data.

Example 2: Fine-Tuning a Pre-Trained Model for Sentiment Analysis

Let’s say we want to fine-tune a pre-trained transformer model (like BERT) for a sentiment analysis task. Fine-tuning allows us to adapt a general-purpose language model to a specific task by training it on a smaller, task-specific dataset.

Dataset:

We have a dataset of movie reviews labeled as positive or negative:

1. "The movie was fantastic! I loved the performances." → Positive

2. "Terrible film. Poor direction and weak plot." → Negative

Fine-Tuning Process:

We take a pre-trained BERT or smilar model and fine-tune it on this sentiment analysis dataset. During fine-tuning, we update the model weights slightly so that it learns to classify movie reviews as positive or negative.

Fine-Tuned Model Output:

Given a new review, "The acting was good, but the story was boring.", the fine-tuned model outputs:

Sentiment: Negative (with 65% confidence)        

This illustrates how fine-tuning enables a pre-trained model to specialize in tasks like sentiment analysis by training on domain-specific labeled data.

Example 3: Retrieval-Augmented Generation (RAG) for Factual Text Generation

In some cases, generative models can hallucinate or produce incorrect information. RAG models address this by combining a generative model with a retrieval mechanism that pulls relevant information from an external knowledge base.

Scenario:

We want a chatbot to answer a question about the latest research in AI.

Input:

Question: "What are the latest advancements in generative AI?"

RAG Workflow:

1. Retrieval: The model searches a knowledge base (e.g., a collection of research papers) for relevant information. It retrieves the most relevant passages:

"Generative adversarial networks (GANs) have improved in stability and performance through new architectures like StyleGAN. Additionally, large-scale language models like GPT-4 continue to push the boundaries of text generation."

2. Generation: The generative model uses the retrieved passages to generate a coherent, fact-based response:

"Recent advancements in generative AI include the development of more stable GANs, such as StyleGAN, and the continued improvement of large-scale language models like GPT-4."

By grounding the response in factual data retrieved from a database, RAG reduces the chance of hallucinating incorrect information.

Example 4: Vector Search for Similar Text Retrieval

In this example, we’ll demonstrate how vector search can be used to find semantically similar text entries in a database.

Dataset:

We have a database of text documents. Here are two entries:

1. "Artificial intelligence is revolutionizing healthcare by enabling faster diagnoses."

2. "AI is transforming industries, from healthcare to finance."

Vector Representation:

Using a transformer-based model, we convert each document into a vector embedding. For example:

Doc 1 embedding: [0.15, 0.78, 0.65, ...]        

Doc 2 embedding: [0.17, 0.80, 0.63, ...]        

Query:

We query the system with the sentence:

"How is AI impacting healthcare?"

The system converts this query into a vector and uses cosine similarity to compare it with the vectors of documents in the database. The closest match is:

1. "Artificial intelligence is revolutionizing healthcare by enabling faster diagnoses."

Vector search enables the retrieval of semantically similar documents, even if the wording is different. This technique is widely used in semantic search engines and recommendation systems.

Example 5: Latent Space Exploration in Image Generation

Generative models like Variational Autoencoders (VAEs) learn to encode input data (e.g., images) into a latent space and then generate new data by sampling from this space.

Example:

Suppose we train a VAE on a dataset of handwritten digits (e.g., the MNIST dataset).

- Latent Space: Each digit is encoded as a point in a 2D latent space. Different points in this space correspond to different styles of digits (e.g., a curved "3" vs. a straight "3").

- Generation: By moving through the latent space, we can generate new digits that blend the characteristics of nearby points. For example, moving from one point representing a "2" to another point representing a "3" creates a hybrid digit.

Output:

Sampling from the latent space generates new images that resemble the original handwritten digits but are not exact copies:

Generated digits: [2, 5, 3, 9, 1]        

Latent space exploration allows for the creation of diverse outputs and is useful in image synthesis and style transfer.

Example 6: Embedding Spaces for Word Similarity

Embeddings represent data in a high-dimensional space, where semantically similar words or phrases are located close to each other.

Example:

We use word embeddings from a pre-trained transformer model (e.g., BERT) to find similar words in a text corpus.

Input:

Query word: "King"

Embedding Space:

The model finds the nearest words in the embedding space based on cosine similarity:

Nearest words: ["Queen", "Prince", "Monarch", "Emperor"]        

The relationships captured by the embedding space allow us to find words with similar meanings, even if they are not explicitly related in the text.

Example 7: Prompting for Text Generation

Generative AI models can be guided through prompts, where the input provides context or direction for the output.

Example:

Prompt: "Write a short poem about the ocean."

The model generates the following response:

The ocean sings in shades of blue,

A dance of waves in morning's hue.

Beneath the depths, a world unknown,

Where life and mystery have grown.

By adjusting the prompt, we can control the style, tone, or content of the generated text. This is useful for creative writing, dialogue generation, and chatbots.

In Part 2, we discussed some practical examples of key concepts in Generative AI such as tokenization, transformers, fine-tuning, retrieval-augmented generation (RAG), vector search, latent spaces, and embeddings. These concepts are central to the development of AI applications, ranging from text and image generation to personalized search and recommendation systems.

Understanding how these mechanisms work allows developers to create more effective AI solutions while gaining a clearer picture of how AI models process data and generate content. Whether building chatbots, search engines, or creative tools, a solid grasp of these concepts is important for making the most of generative AI's capabilities.

By Syed Faisal ur Rahman

CTO at Blockchain Laboratories and W3 SaaS Technologies Ltd.