An AI Agent is a system that can understand goals, reason about tasks, use tools, and take actions with varying levels of autonomy. Unlike a basic chatbot, an agent can do more than simply generate text.
AI hallucination occurs when an AI model generates information that appears convincing but is incorrect, unsupported, or fabricated. Understanding its causes and mitigation strategies is essential for building reliable AI systems.
Zero-shot and few-shot prompting are two important techniques for guiding LLMs. Understanding when to use each approach is essential for building effective Generative AI applications.
Top-k and Top-p are sampling techniques that control which candidate tokens an LLM can choose from during generation. Understanding them is important for controlling creativity, diversity, and consistency in AI applications.
Temperature is a parameter that controls the randomness of an LLM's output. Understanding temperature is important when designing AI systems that require either predictable or creative responses.
LLMs don't process text exactly like humans do. They process text as tokens. Understanding tokenization is essential for understanding context windows, API costs, model limits, and LLM performance.
A context window defines how much information an LLM can process in a single interaction. Understanding context windows is essential for designing reliable chatbots, RAG systems, and production AI applications.
Similarity Search helps AI systems find information that is semantically similar to a query. Learn how embeddings, vector databases, and Cosine Similarity work together in modern AI applications.
A Vector Database stores and searches numerical embeddings based on semantic similarity. Learn how vector databases power RAG systems and modern AI applications.
Embeddings convert text, images, or other data into numerical vectors that capture semantic meaning. Learn how embeddings power semantic search, RAG, recommendations, and modern AI applications.
AI hallucination occurs when an AI model generates information that sounds convincing but is incorrect, unsupported, or fabricated. Learn why hallucinations happen and how production AI systems can reduce them.
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.
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