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AI Basics - Generative AI

[Last Updated: Jan 6, 2026]

What Is AI Today

AI today is a software tool that understands natural language, generates content and code, and finds patterns in data. Developers use it for tasks like coding, testing, documentation, and analysis.

AI Is Not Human Intelligence

AI does not think, reason, or understand. It applies mathematical models to data and produces statistically likely outputs. There is no consciousness, intent, or common sense behind the results.

AI Types Developers Should Know

1. Machine Learning (ML)

ML systems learn from data instead of relying on fixed rules. 

// Rule-based
if (temperature > 100) return "Fever";

// ML-based
return model.predict(temperature, symptoms);

2. Deep Learning

Deep Learning is ML that uses neural networks with many layers to learn complex patterns automatically.

Example: Given many labeled cat images, the network learns visual patterns and can recognize new cats without explicit rules. 

// Rule-based features
if (hasPointyEars && hasWhiskers) return "Cat";

// Deep Learning
DeepNeuralNetwork model = buildLayers(...);
model.train(images);
return model.predict(newImage);

How Training Works

The model makes predictions, compares them with correct answers, and adjusts itself when wrong. Over many iterations, it improves, similar to refining code based on test failures and feedback.

Training: Show it labeled examples → AI guesses → compares to labels → adjusts when wrong → repeat.

3. Generative AI & LLMs

Generative AI creates new text, code, or images. Large Language Models (LLMs) predict the next most likely word based on patterns learned from massive datasets.

Example: Prompt: “Write a Java method to parse JSON” → the model generates code by matching patterns it has seen before.

Developer analogy: Autocomplete trained on a vast amount of public code and text.

How Developers Use AI

  • Code generation and autocompletion
  • Debugging and test generation
  • Documentation and explanations
  • Chatbots and internal tools
  • Data analysis and pattern detection

How AI Is Built (High Level)

  • Trained on large datasets
  • Uses statistical models and neural networks
  • Runs on specialized hardware (GPUs/TPUs)
  • Improved through evaluation and feedback

Key Limitations

  • Can sound confident but be wrong
  • Quality depends on training data
  • No real understanding or intent
  • Weak outside familiar patterns

Summary

AI is a powerful productivity tool for developers. It excels at pattern-based tasks and generation, but it does not replace human judgment. Treat it as an assistant—not a decision-maker.

See Also