Basic AI Concepts

City Web Talk January 29, 2025

Basic AI Concepts

1. What is a Model?

An AI model is like a super-smart brain that can read, write, and answer questions.

  • Just like a chef follows a recipe, AI follows a model to generate answers.
  • Example: GPT-4 is a model that understands text and responds like a human.

💡 Analogy:
Think of AI models like different car engines:

  • Some are fast and creative (GPT-4, Claude)
  • Some are precise and reliable (BERT, Llama-2)
  • Some are small but efficient (Gemini Nano, GPT-3.5)

2. What is a Token Limit?

Every AI model has a maximum number of tokens it can handle in one conversation.

  • If a model has 4,000 tokens, that includes your question + AI’s answer.
  • Longer conversations use up more tokens and may cause AI to forget earlier parts.

💡 Analogy:
Tokens are like a whiteboard space. Once it’s full, you need to erase old text to add new.

3. What is Context Length?

  • AI remembers your previous messages only within its token limit.
  • If you have a long conversation, earlier details might get forgotten.
  • Some models, like GPT-4 Turbo, have longer memory (128k tokens), meaning they can remember more.

💡 Analogy:
Think of AI’s memory like a chalkboard—it gets wiped once it’s full!

4. What is Fine-Tuning?

Fine-tuning is like customizing the AI’s knowledge for a specific task.

  • Example: If you train AI on only medical data, it becomes an expert in medicine.
  • Businesses fine-tune AI for customer support, finance, legal work, etc.

💡 Analogy:
Fine-tuning is like teaching a chef only to cook Italian food—they become an expert in it!

5. What is an API in AI?

API (Application Programming Interface) allows AI to connect with apps or websites.

  • Example: When you talk to ChatGPT on a website, it uses an API to send & receive responses.
  • Companies use APIs to add AI to their apps, chatbots, and automation tools.

💡 Analogy:
An API is like a waiter in a restaurant—it takes your order (your request) to the kitchen (AI) and brings back your food (the response).

6. What is Latency in AI?

Latency means how fast AI responds.

  • Low latency (fast) = Quick, instant replies.
  • High latency (slow) = Delayed responses.

💡 Analogy:
Imagine ordering pizza—fast delivery is low latency, and slow delivery is high latency.

7. What is Embedding in AI?

Embeddings help AI “remember” things better by turning words into numbers.

  • It helps AI find related words or concepts quickly.
  • Example: If you ask AI “Tell me about Apple”, it knows whether you mean the fruit 🍏 or the company 🍎 based on context!

💡 Analogy:
Embeddings are like tags on Instagram—they group similar things together!

8. What is a Prompt Engineer?

A prompt engineer is someone who writes better AI prompts to get high-quality responses.

  • Good prompts = Better AI answers
  • Example:
    • Bad Prompt: “Tell me about space.”
    • Good Prompt: “Explain space travel for kids in 5 simple points.”

💡 Analogy:
A prompt engineer is like a DJ who selects the perfect song to make people dance (get the best AI response).

9. What is Reinforcement Learning (RLHF)?

AI improves based on human feedback.

  • RLHF (Reinforcement Learning from Human Feedback) helps AI learn what’s right and wrong.
  • Example: If AI gives a bad answer, humans correct it so it improves next time.

💡 Analogy:
It’s like teaching a child manners—if they say something rude, you correct them, and they learn.

10. What is an Open-Source AI Model?

Open-source models are free for anyone to use and improve.

  • Examples: Llama 2, Falcon, Mistral, Stable Diffusion
  • Companies & developers can modify them without restrictions.

💡 Analogy:
Open-source AI is like a free recipe—anyone can use, change, and improve it!

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