Explain Transformers and QKV matrices

Explain the Transformer architecture with emphasis on self-attention. Define query (Q), key (K), and value (V) matrices: how they are produced from input embeddings and what information each carries. What specifically does the V matrix represent and how is it used after attention weights are computed? Describe at a high level how similarity scores yield attention weights and outputs. Compare Transformers to RNNs/LSTMs and explain how Transformers address sequential dependency and long-range context limitations. Briefly outline multi-head attention and positional encoding and when they matter at inference time.