Generative AI refers to models that can produce new content — text, images, code, audio — rather than simply classifying or predicting from existing data. The last few years have seen these models move from research labs to everyday tools.

How Large Language Models Work

LLMs like GPT-4 and Gemini are trained on massive text corpora using a technique called self-supervised learning. The model learns to predict the next token in a sequence. After pretraining, models are fine-tuned using Reinforcement Learning from Human Feedback (RLHF) to make their outputs more helpful and aligned.

Prompt Engineering

The quality of your prompt significantly impacts output quality. A few principles:

  • Be specific — vague prompts produce vague outputs
  • Provide context — include relevant background information
  • Give examples — few-shot prompting dramatically improves consistency
  • Iterate — treat prompting as a feedback loop, not a one-shot command

Building with APIs

Most LLMs expose HTTP APIs. A minimal example using the OpenAI-compatible interface:

from openai import OpenAI

client = OpenAI(api_key="your-key")

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Explain backpropagation in one paragraph."}
    ]
)

print(response.choices[0].message.content)

What to Build First

Start small and useful:

  1. A CLI tool that summarizes long documents
  2. A script that generates commit messages from diffs
  3. A simple chatbot with memory using conversation history

The best way to understand generative AI is to build something real with it.