🔹 GenAI for Developers

• Generative AI (GenAI) = AI that can create new content (text, code, images, audio, video).
• For developers, GenAI is not just theory — it’s about integrating AI models into apps.
• Think of it like this:
  • 🔧 Traditional dev = you write all the logic yourself.
  • 🤖 GenAI dev = you orchestrate AI models (LLMs, diffusion models, etc.) with backend, DBs, APIs.
• Main dev tasks:
  1. Calling models (OpenAI API, Hugging Face, local LLMs).
  2. Storing/retrieving context (vector DBs like Pinecone, Redis, FAISS).
  3. Building pipelines/agents (LangChain, LlamaIndex).
  4. Deploying AI services (Docker, AWS, scaling like microservices).

💡 In short: GenAI dev = connecting AI brains with real-world apps.

🔹 What is GPT?

• GPT = Generative Pretrained Transformer
• Developed by OpenAI.
• It’s an LLM (Large Language Model) that:
  1. Generative → can produce new text/code.
  2. Pretrained → trained on massive internet data.
  3. Transformer → special deep learning architecture good at understanding sequences (text).
• GPT models (like GPT-4, GPT-5) can:
  • Chat
  • Write code
  • Summarize
  • Translate
  • Answer reasoning tasks

💡 You can think of GPT as a “text prediction engine” — it predicts the next best word/token based on input, but at scale it feels intelligent.

🔹 Token in AI (short & simple)

• A token = smallest unit of text that an AI model reads/understands.
• It can be a word, part of a word, or even punctuation.
• Example (GPT tokenization):
  • "developer" → might be split as "de", "velop", "er" (3 tokens).
  • "I am Yash." → could be ["I", " am", " Yash", "."] (4 tokens).

💡 So, when you hear “GPT processes 8k tokens” → it means it can handle 8,000 chunks of text (input + output combined) in one go.