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Prompt engineering designs inputs to guide AI model behavior, improving output relevance and quality.

Learn how tokenization in AI helps models process text by breaking it into tokens for better language understanding.

Fine-tuning adjusts pre-trained AI models to specific tasks, improving accuracy without full retraining.

Embedding in AI represents data like text or images as numerical vectors, enabling models to understand relationships.

LLM stands for Large Language Model, advanced AI systems trained on massive datasets to understand and generate text.

Vector databases store and search embeddings efficiently, crucial for semantic search and AI retrieval systems.