Attention visualization displays attention weights to understand what the model focuses on during prediction. What attention shows: Which input tokens/positions influence each output position, relationship patterns across sequence, layer-by-layer information routing. Visualization types: Heatmaps (query-key attention matrices), head views (compare attention heads), token-level highlighting, attention flow diagrams. Tools: BertViz (interactive visualization), Ecco, Weights & Biases attention plotting, custom matplotlib heatmaps. Interpretation caveats: Attention ≠ importance: High attention doesn't mean causal influence on output. Not faithful: Attention may not reflect underlying reasoning process. Many heads: Patterns vary across heads - which to examine? Use cases: Debugging specific predictions, finding syntactic patterns (heads attending to previous token, subject-verb, etc.), qualitative analysis, presentations. Better alternatives: Attribution methods, probing, activation patching provide more causal evidence. Best practices: Use as exploratory tool, don't over-interpret, combine with other interpretability methods, focus on consistent patterns. Starting point for understanding but not definitive explanation.