The 60-second answer
RNNs process sequence state recurrently, giving natural order and linear-time recurrence but limited parallelism and difficult long-range credit assignment. Transformers use attention plus positional information, enabling highly parallel training and direct long-range interactions at the cost of attention memory/compute with sequence length.
Build the answer in this order
RNNs process sequence state recurrently, giving natural order and linear-time recurrence but limited parallelism and difficult long-range credit assignment.
Transformers use attention plus positional information, enabling highly parallel training and direct long-range interactions at the cost of attention memory/compute with sequence length.
LSTMs/GRUs mitigate vanishing gradients but retain sequential dependency; Transformers dominate many large-scale language tasks because they scale efficiently on accelerators.
Choose from sequence length, streaming/latency, data/compute scale, and task—not architecture fashion alone.
A useful interview mental model
This is the shape of a strong answer—not a script to memorize.
Senior-level signal
- Senior answers mention state-space/linear-attention alternatives and distinguish training throughput from autoregressive serving latency.
- Discuss cases where a small recurrent model is still cheaper or better suited to continuous streaming.
What the interviewer is really testing
Likely follow-up questions
Common weak-answer patterns
- Reciting a definition without mechanism or assumptions.
- Claiming one technique is always better without a data regime.
- Stopping before failure modes, validation, or deployment implications.