RAG and Fine-Tuning solve different AI problems. Learn how they work, when to use each approach, and why RAG is often better for applications that need frequently changing or private knowledge.
AI Agents can reason, use tools, make decisions, and take actions to complete tasks. Learn how AI Agents differ from traditional chatbots and how they work in real-world applications.
An AI model, an AI API, and an AI application are different layers of an AI system. Understanding how they work together is essential for building real-world AI applications.
Zero-Shot, One-Shot, and Few-Shot Prompting are techniques for guiding LLMs with different numbers of examples. Learn how they work and when to use each approach.
A context window defines how much information an LLM can process at one time. Learn what tokens and context windows are, why they matter, and how they affect AI application performance.
Vector databases store and search numerical embeddings efficiently, making them essential for RAG, semantic search, recommendation systems, and modern AI applications.
AI Agents go beyond simple chatbots by understanding goals, planning tasks, using tools, and taking actions. Learn how AI Agents work and how they differ from traditional chatbots.
Generative AI can create text, images, audio, video, and code from simple instructions. But how does it actually generate this content? This guide explains the basic process of Generative AI in simple language with practical examples.