In the Raft protocol, the leader sends AppendEntries; the follower writes the log entries to persistent storage, but doesn't apply them to the state machine yet; the leader sends another AppendEntries with a higher commit index; and then the follower applies the changes to the state machine. In RethinkDB's case, the "state machine" is the B-tree we store on disk. One of the guarantees we provide is that if the server acknowledges a write to the client, then all future reads should see that write; and we perform reads by querying the B-tree. So we can't acknowledge writes until they've been actually written to the B-tree, and we can't start writing to the B-tree until after the write has been committed via Raft. So that's where the latency would come from if we were using Raft to manage individual documents. We considered a couple of ways to work around this. One would be to make reads check both the B-tree and the Raft log; but that makes the read logic much more complicated. Another would be to start writing to the B-tree as soon as we put the write in the Raft log. The problem is that Raft expects to be able to roll back parts of its log at any time, and our storage engine's MVCC capabilities aren't good enough for that. The hybrid approach allows us to have good latency without major rewrites of the existing storage engine.
The other reason is that RethinkDB allows tables to be split into shards, and the number of shards can be changed while the databases is running and accepting queries. We considered having one Raft instance per shard, but we would have needed to modify the Raft algorithm to allow splitting and merging Raft instances while they are running and accepting queries. (This is approximately what CockroachDB is doing [1].) But the Raft algorithm is really tricky to get right even when you're not trying to modify it, and we wanted to stick as closely as possible to the official algorithm to minimize bugs. The approach we ended up going with allows us to have the performance and convenience of live resharding without having to modify the Raft algorithm.
In response to your question about consistency guarantees: RethinkDB gives users several options for trading off consistency and latency. The default is to acknowledge writes only once they're safely on disk on a majority of replicas, but to perform reads only on the primary replica (leader). This doesn't give perfect consistency; if the leader fails over, reads that hit the database around the time of the failover might see outdated data, or they might read writes that were rolled back as part of the failover. Unfortunately, the only way to get stronger consistency guarantees is to wait for a majority of replicas to acknowledge the read, which makes performance much worse. We offer a safe-but-slow mode for reads, but it's not the default because the performance is so bad. We also offer fast-but-unsafe modes for writes, for users that want better latency and are OK with losing the last few writes in the event of a failover. See the documentation [2] for more information.