Feed forward networks inside transformers

Today we are covering feed forward networks inside transformers. The goal is to build an interview ready explanation. Intuition first, mechanics second, and practical trade-offs at the end. Intuition attention mixes information across tokens. The feed forward network then processes each token independently, expanding and compressing its representation with nonlinear layers. Core mechanics. FFNs are applied position-wise to each token. They usually expand hidden dimension, apply activation, then project back. They add nonlinear feature transformation after attention. Mixing the same FFN weights are shared across positions in many LLMs. FFNs contain a large fraction of parameters. The compact mental model is FFN X= W2 activation WX plus B plus B2. In an interview, define each part in plain language before discussing implementation. Common traps. Do not confuse FFNs with attention. FFNs do not mix tokens directly. The expansion ratio effects parameter count and compute activation choice matters. Jello, CLU, or gated variance. FFNs can dominate memory and latency in large models. Concrete example. After attention gathers context for the token bank, the FFN can help transform that context into features closer to river bank or financial bank depending on the sentence. Walk through what changes at each step and why the operation helps. Interview checklist. Say attention mixes tokens. FFN transforms each token. Write the two layer structure. Mention expansion dimension name common activations. Connect FFNs to parameter count. Quick recap for feed forward networks inside transformers. Start with the intuition, define the mechanism, mention the trade-off, and close with a concrete example. That structure turns a memorized answer into a practical engineering answer.