Few-Shot Learning

Stepwise reasoning patterns that can improve multi-step tasks; often handled implicitly or summarized for safety/priv...

Architecture that retrieves relevant documents (e.g., from a vector DB) and conditions generation on them to reduce h...

Retrieval based on embedding similarity rather than keyword overlap, capturing paraphrases and related concepts.

Breaking documents into pieces for retrieval; chunk size/overlap strongly affect RAG quality.

Converting text into discrete units (tokens) for modeling; subword tokenizers balance vocabulary size and coverage.

Training objective where the model predicts the next token given previous tokens (causal modeling).

Generates sequences one token at a time, conditioning on past tokens.

Predicts masked tokens in a sequence, enabling bidirectional context; often used for embeddings rather than generation.