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====== AI Terms: RLHF and RAG ======

=== Reinforcement Learning from Human Feedback (RLHF) ===
RLHF is a machine learning technique used to fine-tune AI models using human feedback to improve their behavior and outputs. The key steps involved are:
  * **Response Generation**: The AI generates a set of responses to a given input.
  * **Human Feedback**: Humans rank or provide feedback on the quality of the responses.
  * **Training**: The AI is trained using reinforcement learning to favor responses that align with human preferences.

**Applications:** RLHF is used to align AI with human values, reduce harmful outputs, and ensure responses are more relevant, ethical, and understandable.

=== Retrieval-Augmented Generation (RAG) ===
RAG is an AI architecture that combines a language model with information retrieval to provide factually grounded responses. The process involves:
  * **Information Retrieval**: The AI pulls relevant data from external knowledge bases.
  * **Augmentation**: The retrieved information is fed into the language model to improve accuracy and relevance.
  * **Response Generation**: The AI generates a final output using both retrieved information and its existing knowledge.

**Applications:** RAG is commonly used in chatbots, virtual assistants, and question-answering systems to improve the factual accuracy and relevance of responses.

=== Related Terms ===
  * **Fine-Tuning**: The process of adapting a pre-trained model to a specific task using additional training data.
  * **Prompt Engineering**: Designing inputs to guide AI models toward producing desired outputs.
  * **Reinforcement Learning (RL)**: A broader machine learning approach where agents learn by interacting with an environment and receiving rewards or penalties.
  * **Knowledge Base**: A repository of information used for retrieval in systems like RAG.
  * **Human-in-the-Loop (HITL)**: A method where humans remain involved in the training or decision-making processes to ensure quality and relevance.

RLHF and RAG are essential techniques for improving AI behavior and accuracy, offering complementary solutions for creating more human-centric, reliable AI systems.

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