Our problem is not about auto-completion (we're not dealing with that much data to need sophisticated algorithms for that). What we're doing with our NN is ordering the set of results (matches) we already have. In other words, we're assigning a relevance number in [0, 1] to each result, based on the query string and training based on past user choices (clicking a result). In order to maintain some consistency and robustness, we need our NN to yield similar results for similar word fragments.
So if the NN has previously learned meaningful result priorities for "cargo", they should ideally also work out for "carg" (and vice versa) because of the live listing nature of our tool.
So if the NN has previously learned meaningful result priorities for "cargo", they should ideally also work out for "carg" (and vice versa) because of the live listing nature of our tool.