Attribution traces which retrieved sources support each part of a generated answer, enabling verification. Motivation: Users need to verify AI claims, trust requires transparency, citations enable fact-checking. Implementation approaches: Post-hoc: Generate answer, then match statements to sources via NLI/similarity. Inline generation: Train/prompt model to cite sources as it generates [1], [2] style. Structured output: Model outputs (statement, source_ids) pairs. Citation quality: Precision (cited sources actually support claim), recall (all claims have citations), verifiability (human can check). Challenges: Generated text may paraphrase sources, combining information from multiple sources, hallucinated citations. Evaluation: ALCE benchmark, human evaluation of citation quality. Tools: LangChain source tracking, LlamaIndex citation engine. UI considerations: Display sources alongside text, link to original documents, highlight supporting passages. Best practices: Retrieve high-quality sources, verify citations before presenting, allow users to see source context. Attribution builds trust and enables human-AI collaboration for accuracy.