Update tutorials/docs-10-using-turing-autodiff/index.qmd

Co-authored-by: Guillaume Dalle <[email protected]>

Author: willtebbutt

tutorials/docs-10-using-turing-autodiff/index.qmd

@@ -20,7 +20,8 @@ As of Turing version v0.30, the global configuration flag for the AD backend has
 Users can pass the `adtype` keyword argument to the sampler constructor to select the desired AD backend, with the default being `AutoForwardDiff(; chunksize=0)`.
 
 For `ForwardDiff`, pass `adtype=AutoForwardDiff(; chunksize)` to the sampler constructor. A `chunksize` of 0 permits the chunk size to be automatically determined. For more information regarding the selection of `chunksize`, please refer to [related section of `ForwardDiff`'s documentation](https://juliadiff.org/ForwardDiff.jl/dev/user/advanced/#Configuring-Chunk-Size).
-For `ReverseDiff`, pass `adtype=AutoReverseDiff()` to the sampler constructor. An additional argument can be provided to `AutoReverseDiff` to specify whether to to cache the tape only once and reuse it later use (`false` by default, which means no caching). This can substantially improve performance, but risks silently incorrect results if not used with care.
+For `ReverseDiff`, pass `adtype=AutoReverseDiff()` to the sampler constructor. An additional keyword argument called `compile` can be provided to `AutoReverseDiff`. It specifies whether to pre-record the tape only once and reuse it later (`compile` is set to `false` by default, which means no pre-recording). This can substantially improve performance, but risks silently incorrect results if not used with care.
+
 
 
 Cached tapes should only be used if you are absolutely certain that the sequence of operations performed in your code does not change between different executions of your model.