What is Generative AI?

Generative AI is a subset of artificial intelligence. It is focused on producing new data that mirrors the patterns in the training data. This includes generating human-like text, realistic images, original music, and even video. These models do not just categorize, they create. Unlike traditional AI models that solve classification or regression problems, generative models learn the underlying distribution of input data and generate entirely new outputs.

AI vs ML vs DL vs Generative AI ( “Generative AI vs Traditional AI”)

• Artificial Intelligence (AI): Any machine behavior that mimics cognitive functions like reasoning, learning, or problem-solving.

• Machine Learning (ML): A subset of AI that uses algorithms trained on data to make predictions or decisions.

• Deep Learning (DL): A further subset of ML using neural networks with multiple layers, enabling the handling of massive datasets and complex patterns.

• Generative AI: A domain within DL where models generate new content rather than simply analyzing it. Key technologies include GANs, VAEs, and Transformers.

How Are LLMs Trained?

Training large language models (LLMs) like ChatGPT or LLaMA 3 involves:

1. Data Collection: Billions of words scraped from websites, books, articles, and forums.

2. Cleaning & Preprocessing: Removing low-quality data, deduplicating text, filtering toxicity.

3. Tokenization:

   from transformers import GPT2Tokenizer
   tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
   tokens = tokenizer.tokenize("LLMs are changing the world!")
   print(tokens)
   

4. Model Architecture: Based on the Transformer design, with self-attention layers.

5. Pre-training: Models learn language structure by predicting the next token.

6. Fine-tuning: Focuses on specific tasks using labeled data.

7. RLHF (Reinforcement Learning from Human Feedback): Refines outputs using feedback from human evaluators.

Training Loss Graph Visualization (Optional)

import matplotlib.pyplot as plt
loss = [5.1, 4.5, 3.9, 3.2, 2.8, 2.3, 1.9]
epochs = list(range(1, 8))
plt.plot(epochs, loss, marker='o')
plt.xlabel('Epochs')
plt.ylabel('Training Loss')
plt.title('Training Curve of LLM')
plt.grid(True)
plt.show()

Evolution of LLM Models

1. GPT Series (OpenAI):

   • GPT-1 (2018): Basic transformer with 117M parameters.

   • GPT-2 (2019): 1.5B parameters; capable of coherent long-form text.

   • GPT-3 (2020): 175B parameters; zero-shot and few-shot learning.

   • GPT-4 (2023): Multimodal understanding (text + images).

2. BERT and RoBERTa (Google, Facebook): Focused on understanding context bidirectionally.

3. LLaMA (Meta):

   • LLaMA 1: Efficient, open-weight model.

   • LLaMA 2: Competitive with GPT-3.

   • LLaMA 3 (2024): Fine-tuned on high-quality datasets, available with open weights.

4. Other Popular LLMs (SEO Keywords: “Top LLM models in 2024”):

   • Claude 3 (Anthropic): Designed for alignment and safety.

   • PaLM 2 (Google): Powering Bard.

   • Mistral & Falcon: Lightweight, open-source models.

Comparative Table of Top LLMs (Updated for 2025)

Build Your Own LLM in Python

Text Generation

from transformers import pipeline
text_gen = pipeline("text-generation", model="gpt2")
output = text_gen("The future of AI is", max_length=50)
print(output[0]['generated_text'])

Text Summarization

summarizer = pipeline("summarization")
article = "Generative AI is a groundbreaking field that..."
summary = summarizer(article)
print(summary[0]['summary_text'])

Named Entity Recognition

ner = pipeline("ner", grouped_entities=True)
sentence = "Elon Musk founded SpaceX in California."
entities = ner(sentence)
print(entities)

Sentiment Analysis

classifier = pipeline("sentiment-analysis")
review = "I love using generative AI tools like ChatGPT!"
print(classifier(review)Conclusion

Conclusion

Generative AI and LLMs like ChatGPT, LLaMA 3, and Claude 3 are changing the way we interact with technology. With strong open-source models and tools like Hugging Face, anyone can build applications in this space. Whether you’re curious about “how LLMs are trained” or want to “build your own LLM in Python,” now is the time to dive into this transformative field.