Top Generative AI Terms You Should Always Know- 1. LLM (Large Language Model) What it enables: The engine behind modern AI applications - Business Impact: Powers everything from code generation to content creation - Implementation: Models like GPT-4, Claude, and Gemini 2. Transformers What it enables: The architecture making modern AI possible - Business Impact: Enables processing of complex, contextual information - Implementation: Foundation of models like BERT and GPT 3. Prompt Engineering What it enables: Getting consistent, high-quality outputs from AI - Business Impact: Determines the success of AI implementation - Implementation Strategy: - System prompts for consistent behavior - Chain prompts for complex tasks - Guard rails for safety 4. Fine-tuning What it enables: Customization for specific use cases - Business Impact: Creates specialized AI solutions - Implementation Strategy: - Domain adaptation - Task-specific optimization - Behavior alignment 5. Embeddings What it enables: Making AI understand similarity and context - Business Impact: Powers semantic search and recommendations - Implementation: Vector databases and similarity searches 6. RAG (Retrieval Augmented Generation) What it enables: Combining AI with reliable knowledge bases - Business Impact: Reduces hallucinations, increases accuracy - Implementation: - Knowledge base integration - Real-time data access - Fact verification 7. Tokens What it enables: Resource management and cost control - Business Impact: Directly affects operational costs - Implementation: - Input optimization - Output management - Cost forecasting 8. Hallucination What it enables: Understanding AI limitations - Business Impact: Critical for risk management - Implementation: - Fact-checking mechanisms - Confidence scoring - Source verification 9. Zero-shot Learning What it enables: Flexibility in AI applications - Business Impact: Reduces training requirements - Implementation: Task adaptation without specific training 10. Chain-of-Thought What it enables: Complex problem-solving capabilities - Business Impact: Improves accuracy and explainability - Implementation: Step-by-step reasoning processes 11. Context Window What it enables: Processing capacity for tasks - Business Impact: Determines task complexity handling - Implementation: - Document chunking strategies - Memory management - Conversation design 12. Temperature What it enables: Output control and consistency - Business Impact: Balances creativity and accuracy - Implementation Settings: - 0.0: Factual, consistent responses - 0.7-1.0: Creative, varied outputs As GenAI evolves, new terms and concepts emerge regularly. Key areas to watch: - Multimodal AI capabilities - Model compression techniques - Constitutional AI frameworks - Agent-based architectures Understanding these terms is just the starting point. The value comes from knowing how to apply them to real business problems. | 134 comments on LinkedIn