diff --git a/docs/jax.lax.rst b/docs/jax.lax.rst
index 5f51cdb3b..9db79f591 100644
--- a/docs/jax.lax.rst
+++ b/docs/jax.lax.rst
@@ -58,6 +58,7 @@ Operators
     clz
     collapse
     complex
+    composite
     concatenate
     conj
     conv
diff --git a/jax/_src/lax/lax.py b/jax/_src/lax/lax.py
index 99760099d..d4131e69b 100644
--- a/jax/_src/lax/lax.py
+++ b/jax/_src/lax/lax.py
@@ -1489,14 +1489,14 @@ def composite(
 ):
   """Composite with semantics defined by the decomposition function.
 
-  A composite is a higher-order JAX function that encapsulates an operation mad
+  A composite is a higher-order JAX function that encapsulates an operation made
   up (composed) of other JAX functions. The semantics of the op are implemented
   by the ``decomposition`` function. In other words, the defined composite
   function can be replaced with its decomposed implementation without changing
   the semantics of the encapsulated operation.
 
   The compiler can recognize specific composite operations by their ``name``,
-  ``version``, ``kawargs``, and dtypes to emit more efficient code, potentially
+  ``version``, ``kwargs``, and dtypes to emit more efficient code, potentially
   leveraging hardware-specific instructions or optimizations. If the compiler
   doesn't recognize the composite, it falls back to compiling the
   ``decomposition`` function.
@@ -1505,11 +1505,11 @@ def composite(
   be implemented as ``sin(x) / cos(x)``. A hardware-aware compiler could
   recognize the "tangent" composite and emit a single ``tangent`` instruction
   instead of three separate instructions (``sin``, ``divide``, and ``cos``).
-  With compilers for hardwares without dedicated tangent support, it would fall
-  back to compiling the decomposition.
+  For hardware without dedicated tangent support, it would fall back to
+  compiling the decomposition.
 
-  This is useful for preserving high level abstraction that would otherwise be
-  lost while lowering which allows for easier pattern-matching in low-level IR.
+  This is useful for preserving high-level abstractions that would otherwise be
+  lost while lowering, which allows for easier pattern-matching in low-level IR.
 
   Args:
     decomposition: function that implements the semantics of the composite op.
@@ -1517,19 +1517,20 @@ def composite(
     version: optional int to indicate semantic changes to the composite.
 
   Returns:
-    out: callable composite function. Note that positional arguments to this
-      function should be interpreted as inputs and keyword arguments should be
-      interpreted as attributes of the op. Any keyword arguments that are passed
-      with ``None`` as a value will be omitted from the
-      ``composite_attributes``.
+    Callable: Returns a composite function. Note that positional arguments to
+    this function should be interpreted as inputs and keyword arguments should
+    be interpreted as attributes of the op. Any keyword arguments that are
+    passed with ``None`` as a value will be omitted from the
+    ``composite_attributes``.
 
   Examples:
     Tangent kernel:
+
     >>> def my_tangent_composite(x):
     ...   return lax.composite(
-    ...     lambda x: lax.sin(x) / lax.cos(x), name='my.tangent'
+    ...     lambda x: lax.sin(x) / lax.cos(x), name="my.tangent"
     ...   )(x)
-    ...
+    >>>
     >>> pi = jnp.pi
     >>> x = jnp.array([0.0, pi / 4, 3 * pi / 4, pi])
     >>> with jnp.printoptions(precision=3, suppress=True):
@@ -1538,9 +1539,10 @@ def composite(
     [ 0.  1. -1.  0.]
     [ 0.  1. -1.  0.]
 
-    The recommended way to create composites is via a decorator. Use `/` and `*`
-    in the function signature to be explicit about positional and keyword
-    arguments respectively:
+    The recommended way to create composites is via a decorator. Use ``/`` and
+    ``*`` in the function signature to be explicit about positional and keyword
+    arguments, respectively:
+
     >>> @partial(lax.composite, name="my.softmax")
     ... def my_softmax_composite(x, /, *, axis):
     ...   return jax.nn.softmax(x, axis)