For \begin{align} \overline{T_k}=\frac{T_1+\dots+T_k}{k} \end{align} being the sample average of the first $k$ samples, with $T_i$ being exponentially distributed, the CLT states, that for $k$ large enough the random variables $\sqrt{k}\left(\overline{T_k}-\mu \right)$ converge to a normal distribution $\mathcal{N}\left(0,\sigma^2 \right)$ in distribution, i.e \begin{align} \sqrt{k}\left(\overline{T_k}- \mu\right) \to \mathcal{N}\left( 0,\sigma^2\right) \, . \end{align}
How could one find an upper bound for the error? Is there a way of expressing the error with respect to $k$?