🔑 Essential GenAI Terms You Should Know! 🔑
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If you’re diving into the world of Generative AI, these 12 terms are your toolkit. Understanding them will help you navigate this rapidly evolving field with confidence. Let’s break it down.
1️⃣ LLM (Large Language Model)
- What it is: Advanced AI systems trained on massive datasets of text.
- Where it’s used: ChatGPT, Claude, Gemini, Copilot.
- Example: GitHub Copilot using LLMs to generate code suggestions.
2️⃣ Transformers
- What it is: A groundbreaking neural network architecture powering all modern language models.
- Where it’s used: Models like GPT, BERT, and T5.
- Example: Google BERT enhancing the understanding of search queries.
3️⃣ Prompt Engineering
- What it is: The craft of designing effective instructions for AI.
- Where it’s used: Business tools, creative content, specialized AI tasks.
- Example: Creating prompts for DALL-E to generate unique images.
4️⃣ Fine-tuning
- What it is: Customizing pre-trained AI models for niche tasks.
- Where it’s used: Industry applications, chatbots, specialized tools.
- Example: Training AI to assist with medical diagnostics.
5️⃣ Embeddings
- What it is: Numerical representations of data (text, images) used for analysis.
- Where it’s used: Search engines, recommendation systems, document comparisons.
- Example: Pinecone leveraging embeddings for vector-based searches.
6️⃣ RAG (Retrieval Augmented Generation)
- What it is: AI generation enhanced by external knowledge sources.
- Where it’s used: Enterprise chatbots, knowledge tools, customer support.
- Example: Chatbots referencing company documents for accurate answers.
7️⃣ Tokens
- What it is: Small units of text processed by AI.
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- Why it matters: Determines input limits, output scope, and model costs.
- Example: GPT-4 managing a 32k token context for long-form tasks.
8️⃣ Hallucination
- What it is: AI producing incorrect but plausible information.
- Impact on: Content accuracy, business credibility, decision-making.
- Example: AI fabricating historical dates in an answer.
9️⃣ Zero-shot Learning
- What it is: AI completing tasks it hasn’t been specifically trained for.
- Where it’s used: Classifying new categories, understanding novel contexts.
- Example: Sorting new product types without prior examples.
🔟 Chain-of-Thought
- What it is: Step-by-step reasoning in AI outputs.
- Where it’s used: Problem-solving, logic tasks, complex calculations.
- Example: Solving math problems one step at a time.
1️⃣1️⃣ Context Window
- What it is: The maximum amount of text an AI can process at once.
- Why it matters: Influences conversation depth, document length, and task complexity.
- Example: AI reviewing lengthy legal documents.
1️⃣2️⃣ Temperature
- What it is: Controls randomness in AI-generated responses.
- Settings:
- Low (e.g., 0.0): Focused and deterministic.
- High (e.g., 0.7-1.0): Creative and exploratory.
- Example: Generating imaginative marketing copy with a high temperature setting.
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