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.
Building an AI system is not enough—you need to measure how accurately, reliably, and safely it performs. Learn the key metrics and methods used to evaluate modern AI and LLM applications.
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.
RAG and Fine-Tuning solve different AI problems. RAG gives a model access to external knowledge, while Fine-Tuning adapts the model's behavior through additional training.
Fine-tuning adapts a pretrained AI model using additional training data, while prompt engineering guides the model through instructions without changing its parameters.
Prompt Engineering is the practice of designing clear and effective instructions to guide AI models toward accurate, relevant, and consistent outputs.
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.
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.
Showing 12 of 24
