diff --git a/docs/userDocs/source/user/UsingClad.rst b/docs/userDocs/source/user/UsingClad.rst
index ad3cfa51e..5191934f6 100644
--- a/docs/userDocs/source/user/UsingClad.rst
+++ b/docs/userDocs/source/user/UsingClad.rst
@@ -23,7 +23,7 @@ Clad consists of 4 primary automatic differentiation functions:
 
 - ``clad::differentiate`` -- Primary forward mode automatic differentiation
 - ``clad::gradient`` -- Primary reverse mode automatic differentiation
-- ``clad::hessian``
+- ``clad::hessian``  
 - ``clad::jacobian``
 
 Each of these functions will be explored in this guide.
@@ -217,9 +217,25 @@ Few important things to note about ``clad::hessian``:
   by the derived function. The hessian matrix array size should at least be as big as the size 
   required to store the hessian matrix. Passing an array less than the required size will result in undefined behaviour.
 
-.. todo::
+Consider the case of the input being an array we need to specify the array index 
+that needs to be differentiated even when we want to differentiate w.r.t entire array.
+
+.. code-block:: cpp
+  
+ #include "clad/Differentiator/Differentiator.h"
+
+ double fn(double x, double arr[2]) { return x * arr[0] * arr[1]; }
+
+ int main() {
+
+   auto fn_hessian = clad::hessian(fn, "x, arr[0:1]");
 
-   Add details for computing hessian of array ranges.
+   // We have 3 independent variables thus we require space of 9.
+   double mat_fn[9] = {0};
+   clad::array_ref<double> mat_fn_ref(mat_fn, 9);
+   double num[2] = {1, 2};
+   fn_hessian.execute(3, num, mat_fn_ref);
+ }
 
 Jacobian Computation
 ----------------------
diff --git a/docs/userDocs/source/user/tutorials.rst b/docs/userDocs/source/user/tutorials.rst
index f6ec9d21b..a7f934567 100644
--- a/docs/userDocs/source/user/tutorials.rst
+++ b/docs/userDocs/source/user/tutorials.rst
@@ -1,6 +1,156 @@
 Tutorials
 ----------
-
-.. todo::
    
-   Add tutorials
\ No newline at end of file
+Clad is an open source clang plugin which supports automatic differentiation of 
+mathematical functions in C++. Currently Clad supports four modes for automatic 
+differentiation namely forward, reverse, Hessian, Jacobian.
+
+**The Forward mode** 
+
+Clad supports forward mode automatic differentiation through the `clad::differentiate`
+API call.
+
+.. code-block:: cpp 
+
+ #include <iostream>
+ #include "clad/Differentiator/Differentiator.h"
+
+ double func(int x) { return x * x; }
+
+ int main() {
+   /*Calling clad::differentiate to get the forward mode derivative of
+   the given mathematical function*/
+   auto d_func = clad::differentiate(func, "x");
+   // execute the generated derivative function.
+   std::cout << d_func.execute(/*x =*/3) << std::endl;
+   // Dump the generated derivative code to std output.
+   d_func.dump();
+ }
+
+Here we are differentiating a function `func` which takes an input `x` and 
+returns a scaler value `x * x`.`.dump()` method is used to get a dump of generated 
+derivative function to the standard output.
+
+**The Reverse Mode** 
+ 
+Clad also supports reverse mode automatic differentiation, through the `clad::gradient` 
+API call.
+
+.. code-block:: cpp  
+
+ #include <iostream>
+ #include "clad/Differentiator/Differentiator.h"
+
+ double f(double x, double y, double z) { return x * y * z; }
+
+ int main() {
+   auto d_f = clad::gradient(f, "x, y");
+   double dx = 0, dy = 0;
+   d_f.execute(/*x=*/2, /*y=*/3, /*z=*/4, &dx, &dy);
+   std::cout << "dx : " << dx << "dy :" << dy << std::endl;
+ }
+
+In the above example we are differentiating w.r.t `x and y` we can also 
+differentiate w.r.t to single argument i.e. either `x` or `y` as `clad::gradient(f, "x")` 
+not writing any argument i.e. `clad::gradient(f)` will result in differentiation 
+of the function w.r.t to each input. 
+
+
+**The Hessian Mode**
+
+Clad can also produce an hessian matrix through the `clad::hessian` API call.
+It returns the hessian matrix as a flattened vector in row major format.
+
+.. code-block:: cpp
+
+ #include <iostream>
+ #include "clad/Differentiator/Differentiator.h"
+
+ double f(double x, double y, double z) { return x * y * z; }
+
+ // Function with array input
+
+ double f_arr(double x, double y, double z[2]) { return x * y * z[0] * z[1]; }
+
+ int main() {
+   // Workflow similar to clad::gradient for non-array input arguments.
+   auto f_hess = clad::hessian(f, "x, y");
+   double matrix_f[9] = {0};
+   clad::array_ref<double> matrix_f_ref(matrix_f, 9);
+   f_hess.execute(3, 4, 5, matrix_f_ref);
+   std::cout << "[" << matrix_f_ref[0] << ", " << matrix_f_ref[1]
+             << matrix_f_ref[2] << "\n"
+             << matrix_f_ref[3] << ", " << matrix_f_ref[4] << matrix_f_ref[5]
+             << "\n"
+             << matrix_f_ref[6] << ", " << matrix_f_ref[7] << matrix_f_ref[8]
+             << "]"
+             << "\n";
+ }
+
+When arrays are involved we need to specify the array index that needs to be 
+differentiated. For example if we want to differentiate w.r.t to the first two 
+elements of the array along with `x` and `y` we will write `clad::hessian(f_arr, z[0:1])` 
+for the above example rest of the steps for execution are similar to reverse mode.
+Here the `clad::array_ref` variable stores the hessian matrix.
+
+
+**The Jacobian Mode**
+
+Clad can produce Jacobian of a function using its reverse mode. It returns the 
+jacobian matrix as a flattened vector with elements arranged in row-major format.
+
+.. code-block:: cpp
+
+ #include <iostream>
+ #include "clad/Differentiator/Differentiator.h"
+
+ void f(double x, double y, double z, double* output) {
+   output[0] = x * y;
+   output[1] = y * y * x;
+   output[2] = 6 * x * y * z;
+ }
+
+ int main() {
+   auto f_jac = clad::jacobian(f);
+
+   double jac[9] = {0};
+   double output[3] = {0};
+   f_jac.execute(3, 4, 5, output, jac);
+   std::cout << jac[0] << " " << jac[1] << std::endl
+             << jac[2] << " " << jac[3] << std::endl
+             << jac[4] << " " << jac[5] << std::endl
+             << jac[6] << " " << jac[7] << std::endl
+             << jac[8] << std::endl;
+ }
+
+The jacobian matrix size should be equal to `no. of independent variables times 
+the number of outputs in the original function` in the above example it would be
+an array of size 3x3 = 9.
+
+**Error Estimation API**
+
+Clad is capable of annotating a given function with floating point error estimation
+code using reverse mode AD.
+
+.. code-block::  cpp
+
+ #include <iostream>
+ #include "clad/Differentiator/Differentiator.h"
+
+ double func(double x, double y) { return x * y; }
+
+ int main() {
+
+   auto dfunc_error = clad::estimate_error(func);
+   // Used to print generated code to standard output.
+   dfunc_error.dump();
+   double x, y, d_x, d_y, final_error = 0;
+   // Call execute
+   dfunc_error.execute(x, y, &d_x, &d_y, final_error);
+
+   std::cout << final_error;
+ }
+
+The function signature is similar to `clad::gradient` except we need to add an 
+extra argument of type `double&` which is used to store the total floating point
+error.