diff --git a/spec/draft/index.rst b/spec/draft/index.rst
index 9b1c1a0eb..5df9b31cb 100644
--- a/spec/draft/index.rst
+++ b/spec/draft/index.rst
@@ -30,6 +30,13 @@ Contents
    verification_test_suite
    benchmark_suite
 
+.. toctree::
+   :caption: Guides and Tutorials
+   :maxdepth: 1
+
+   migration_guide
+   tutorial_basic
+
 .. toctree::
    :caption: Other
    :maxdepth: 1
diff --git a/spec/draft/migration_guide.md b/spec/draft/migration_guide.md
new file mode 100644
index 000000000..9babf2f35
--- /dev/null
+++ b/spec/draft/migration_guide.md
@@ -0,0 +1,236 @@
+(migration-guide)=
+
+# Migration Guide
+
+This page is meant to help migrate your codebase to an Array API compliant
+implementation. The guide is divided into two parts and, depending on your
+exact use-case, you should look thoroughly into at least one of them.
+
+The first part is dedicated for {ref}`array-producers`. If your library
+mimics, for example, NumPy's or Dask's functionality, then you can find in
+the first part additional instructions and guidance on how to ensure
+downstream users can easily pick your solution as an array provider for
+their system/algorithm.
+
+The second part delves into details for Array API compatibility for
+{ref}`array-consumers`. This pertains to any software that performs
+multidimensional array manipulation in Python, such as may be found in
+scikit-learn, SciPy, or statsmodels. If your software relies on a certain
+array producing library, such as NumPy or JAX, then you can use the second
+part to learn how to make it library agnostic and interchange array
+namespaces with significantly less friction.
+
+## Ecosystem
+
+Apart from the documented standard, the Array API ecosystem also provides
+a set of tools and packages to help you with the migration process:
+
+
+(array-api-compat)=
+
+### Array API Compat
+
+GitHub: [array-api-compat](https://github.com/data-apis/array-api-compat)
+
+User group: Array Consumers
+
+Although NumPy, Dask, CuPy, and PyTorch support the Array API Standard, there
+are still some corner cases where their behavior diverges from the standard.
+`array-api-compat` provides a compatibility layer to cover these cases.
+This is also accompanied by a few utility functions for easier introspection
+into array objects. As an array consumer, you can still rely on the original
+API while having access to the standard compatible one.
+
+
+(array-api-strict)=
+
+### Array API Strict
+
+GitHub: [array-api-strict](https://github.com/data-apis/array-api-strict)
+
+User group: Array Consumers, Array Producers (for testing)
+
+`array-api-strict` is a library that provides a strict and minimal
+implementation of the Array API Standard. For array producers, it is designed
+to be used as a reference implementation for testing and development purposes.
+You can compare your API calls with `array-api-strict` counterparts and
+ensure that your library is fully compliant with the standard and can
+serve as a reliable reference for other developers in the ecosystem.
+For consumers, you can use `array-api-strict` during the development as an
+array provider to ensure your code uses APIs compliant with the standard.
+
+
+(array-api-tests)=
+
+### Array API Test
+
+GitHub: [array-api-tests](https://github.com/data-apis/array-api-tests)
+
+User group: Array Producers
+
+`array-api-tests` is a collection of tests that can be used to verify the
+compliance of your library with the Array API Standard. It includes tests
+for array producers, covering a wide range of functionalities and use cases.
+By running these tests, you can ensure that your library adheres to the
+standard and can be used with compatible array consumer libraries.
+
+
+(array-api-extra)=
+
+### Array API Extra
+
+GitHub: [array-api-extra](https://github.com/data-apis/array-api-extra)
+
+User group: Array Consumers
+
+`array-api-extra` is a collection of additional utilities and tools that are
+missing from the Array API Standard but can be useful for compliant array
+consumers. It includes additional array manipulation and statistical functions.
+It is already used by SciPy and scikit-learn.
+
+The sections below mention when and how to use them.
+
+
+(array-producers)=
+
+## Array Producers
+
+For array producers, the central task during the development/migration process
+is ensuring that the user-facing API adheres to the Array API Standard.
+
+The complete API of the standard is documented in the
+[API specification](https://data-apis.org/array-api/latest/API_specification/index.html).
+
+There, each function, constant, and object is described with details
+on parameters, return values, and special cases.
+
+### Testing against Array API
+
+There are two main ways to test your API for compliance: either using
+`array-api-tests` suite or testing your API manually against the
+`array-api-strict` reference implementation.
+
+#### Array API Test suite (Recommended)
+
+{ref}`array-api-tests` is a test suite which verifies that your API
+adheres to the standard. For each function or method, it confirms
+it's importable, verifies the signature, generates multiple test
+cases with the [hypothesis](https://hypothesis.readthedocs.io/en/latest/)
+package, and runs assertions on the outputs.
+
+The setup details are enclosed in the GitHub repository, so here we
+cover only the minimal workflow:
+
+1. Install your package (e.g., in editable mode).
+2. Clone `array-api-tests`, and set the `ARRAY_API_TESTS_MODULE` environment
+   variable to your package import name.
+3. Inside the `array-api-tests` directory run the command for running pytest: `pytest`. There are
+   multiple useful options delivered by the test suite. A few worth mentioning:
+   - `--max-examples=1000` - maximal number of test cases to generate when using
+     hypothesis. This allows you to balance between execution time of the test
+     suite and thoroughness of the testing. It's advised to use as many examples
+     as the time buget can fit. Each test case is a random combination of
+     possible inputs: the more cases, the higher chance of finding an
+     unsupported edge case.
+   - With the `--xfails-file` option, you can describe which tests are expected
+     to fail. It's impossible to get the whole API perfectly implemented on a
+     first try, so tracking what still fails gives you more control over the
+     state of your API.
+   - `-o xfail_strict=<bool>` is often used with the previous option. If a test
+     expected to fail actually passes (`XPASS`), then you can decide whether
+     to ignore that fact or raise it as an error.
+   - `--skips-file` for skipping tests. At times, some failing tests might stall
+     the execution time of the test suite. In that case, the most convenient
+     option is to skip these for the time being.
+
+We strongly advise you to embed this setup in your CI as well. This will allow
+you to continuously monitor Array API coverage, and make sure new changes don't break existing
+APIs. As a reference, see [NumPy's Array API Tests CI setup](https://github.com/numpy/numpy/blob/581d10f43b539a189a2d37856e5130464de9e5f6/.github/workflows/linux.yml#L296).
+
+
+#### Array API Strict
+
+A simpler, and more manual, way of testing Array API coverage is to
+run your API calls along with the {ref}`array-api-strict` Python implementation.
+
+This way, you can ensure that the outputs coming from your API match the minimal
+reference implementation. Bear in mind, however, that you need to write
+the tests cases yourself, so you need to also take into account any applicable edge
+cases.
+
+
+(array-consumers)=
+
+## Array Consumers
+
+For array consumers, the main premise is to keep in mind that your **array
+manipulation operations should not lock in for a particular array producing
+library**. For instance, if you use NumPy for arrays, then your code could
+contain:
+
+```python
+import numpy as np
+
+# ...
+b = np.full(shape, val, dtype=dtype) @ a
+c = np.mean(a, axis=0)
+return np.dot(c, b)
+```
+
+The first step should be as simple as assigning the `np` namespace to a dedicated
+namespace variable. The convention used in the ecosystem is to name it `xp`. Then,
+it is vital to ensure that each method and function call is something that the Array API
+supports. For example, `dot` is present in the NumPy's API, but the standard
+doesn't support it. For the sake of simplicity, let's assume both `c` and `b`
+are `ndim=2`; therefore, we select `tensordot` instead, as both NumPy and the
+standard define it:
+
+```python
+import numpy as np
+
+xp = np
+
+# ...
+b = xp.full(shape, val, dtype=dtype) @ a
+c = xp.mean(a, axis=0)
+return xp.tensordot(c, b, axes=1)
+```
+
+At this point, replacing one backend with another one should only require providing a different
+namespace, such as `xp = torch` (e.g., via an environment variable). This can be useful
+if you're writing a script or in your custom software. The other alternatives are:
+
+- If you are building a library where the backend is determined by input arrays,
+  and your function accepts array arguments, then a recommended way is to ask
+  your input arrays for a namespace to use: `xp = arr.__array_namespace__()`.
+  If the given library doesn't have it, then [`array_api_compat.array_namespace()`](https://data-apis.org/array-api-compat/helper-functions.html#array_api_compat.array_namespace)
+  should be used instead:
+  ```python
+  def func(array1, scalar1, scalar2):
+    xp = array1.__array_namespace__()  # or array_namespace(array1)
+    return xp.arange(scalar1, scalar2) @ array1
+  ```
+- For a function that accepts scalars and returns arrays, use namespace `xp` as
+  a parameter in the signature. Enforcing objects to have the same type as the
+  provided backend can then be achieved with `arg1 = xp.asarray(arg1)` for each input:
+  ```python
+  def func(s1, s2, xp):
+    return xp.arange(s1, s2)
+  ```
+
+If you're relying on NumPy, CuPy, PyTorch, Dask, or JAX then
+{ref}`array-api-compat` can come in handy for the transition. The compat layer
+allows you to still rely on your preferred array producing library, while
+making sure you're already using standard compatible API. Additionally, it
+offers a set of useful utility functions, such as:
+
+- [array_namespace()](https://data-apis.org/array-api-compat/helper-functions.html#array_api_compat.array_namespace)
+  for fetching the namespace based on input arrays.
+- [is_array_api_obj()](https://data-apis.org/array-api-compat/helper-functions.html#array_api_compat.is_array_api_obj)
+  for inspecting whether a given object is Array API compatible.
+- [device()](https://data-apis.org/array-api-compat/helper-functions.html#array_api_compat.device)
+  for retrieving the device on which an array resides.
+
+For now, the migration from a specific library (e.g., NumPy) to a standard
+compatible setup requires a manual intervention for each failing API call,
+but, in the future, we're hoping to provide tools for automating the migration process.
diff --git a/spec/draft/tutorial_basic.md b/spec/draft/tutorial_basic.md
new file mode 100644
index 000000000..ea8a3b453
--- /dev/null
+++ b/spec/draft/tutorial_basic.md
@@ -0,0 +1,158 @@
+(tutorial-basic)=
+
+# Array API Tutorial
+
+In this tutorial, we're going to demonstrate how to migrate to the Array API from the array consumer's
+point of view for a simple graph algorithm.
+
+The example presented here comes from the [`graphblas-algorithms`](https://github.com/python-graphblas/graphblas-algorithms).
+library. In particular, we'll be migrating [the HITS algorithm](https://github.com/python-graphblas/graphblas-algorithms/blob/35dbc90e808c6bf51b63d51d8a63f59238c02975/graphblas_algorithms/algorithms/link_analysis/hits_alg.py#L9), which is
+used for the link analysis for estimating prominence in sparse networks, to be Array API compliant.
+
+The inlined and slightly simplified (without "authority" feature)
+implementation looks similar to the following:
+
+```python
+def hits(G, max_iter=100, tol=1.0e-8, normalized=True):
+    N = len(G)
+    h = Vector(float, N, name="h")
+    a = Vector(float, N, name="a")
+    h << 1.0 / N
+    # Power iteration: make up to max_iter iterations
+    A = G._A
+    hprev = Vector(float, N, name="h_prev")
+    for _i in range(max_iter):
+        hprev, h = h, hprev
+        a << hprev @ A
+        h << A @ a
+        h *= 1.0 / h.reduce(monoid.max).get(0)
+        if is_converged(hprev, h, tol):
+            break
+    else:
+        raise ConvergenceFailure(max_iter)
+    if normalized:
+        h *= 1.0 / h.reduce().get(0)
+        a *= 1.0 / a.reduce().get(0)
+    return h, a
+
+def is_converged(xprev, x, tol):
+    xprev << binary.minus(xprev | x)
+    xprev << unary.abs(xprev)
+    return xprev.reduce().get(0) < xprev.size * tol
+```
+
+We can see that the API is specific to the GraphBLAS array object.
+There is a `Vector` constructor, overloaded `<<` for assigning new values,
+and `reduce`/`get` for reductions. We need to replace them, and, by convention,
+we will use `xp` namespace for calling respective functions.
+
+First, we want to make sure we construct arrays in an agnostic way:
+
+```python
+h = xp.full(N, 1.0 / N)
+A = xp.asarray(G.A)
+```
+
+Then, instead of `reduce` calls, we will use appropriate reduction
+functions from the Array API:
+
+```python
+h = h / xp.max(h)
+# ...
+h = h / xp.sum(xp.abs(h))
+a = a / xp.sum(xp.abs(a))
+# ...
+err = xp.sum(xp.abs(...))
+```
+
+We replace the custom binary operation with the Array API counterpart:
+
+```python
+...(x - xprev)
+```
+
+And finally, let's ensure that the result of the convergence
+condition is a scalar coming from our API:
+
+```python
+err < xp.asarray(N * tol)
+```
+
+The rewrite is complete now, we can assemble all constituent parts into
+a full implementation:
+
+```python
+def hits(G, max_iter=100, tol=1.0e-8, normalized=True):
+    N = len(G)
+    h = xp.full(N, 1.0 / N)
+    A = xp.asarray(G.A)
+    # Power iteration: make up to max_iter iterations
+    for _i in range(max_iter):
+        hprev = h
+        a = hprev @ A
+        h = A @ a
+        h = h / xp.max(h)
+        if is_converged(hprev, h, N, tol):
+            break
+    else:
+        raise Exception("Didn't converge")
+    if normalized:
+        h = h / xp.sum(xp.abs(h))
+        a = a / xp.sum(xp.abs(a))
+    return h, a
+
+def is_converged(xprev, x, N, tol):
+    err = xp.sum(xp.abs(x - xprev))
+    return err < xp.asarray(N * tol)
+```
+
+At this point, the actual execution depends only on `xp` namespace,
+and replacing that one variable will allow us to switch from, e.g., NumPy arrays on the CPU
+to JAX arrays for running on a GPU. This lets us be more flexible, and, for example,
+use lazy evaluation and JIT compile a loop body with JAX's JIT compilation:
+
+```python
+import jax
+import jax.numpy as jnp
+
+xp = jnp
+
+def hits(G, max_iter=100, tol=1.0e-8, normalized=True):
+    N = len(G)
+    h = xp.full((N, 1), 1.0 / N)
+    A = xp.asarray(G.A)
+    # Power iteration: make up to max_iter iterations
+    for _i in range(max_iter):
+        h, a, conv = loop_body(h, A, N, tol)
+        if conv:
+            break
+    else:
+        raise Exception("Didn't converge")
+    if normalized:
+        h = h / xp.sum(xp.abs(h))
+        a = a / xp.sum(xp.abs(a))
+    return h, a
+
+@jax.jit
+def loop_body(hprev, A, N, tol):
+    a = hprev.mT @ A
+    h = A @ a.mT
+    h = h / xp.max(h)
+    conv = is_converged(hprev, h, N, tol)
+    return h, a, conv
+
+def is_converged(xprev, x, N, tol):
+    err = xp.sum(xp.abs(x - xprev))
+    return err < xp.asarray(N * tol)
+
+if __name__ == "__main__":
+
+    class Graph():
+        def __init__(self):
+            self.A = xp.ones((10, 10))
+        def __len__(self):
+            return len(self.A)
+
+    G = Graph()
+    h, a = hits(G)
+```