diff --git a/docs/source/motivation.rst b/docs/source/motivation.rst
index a5ebf67..4e2e3fb 100644
--- a/docs/source/motivation.rst
+++ b/docs/source/motivation.rst
@@ -85,8 +85,8 @@ model. Common to all of these solutions is the need to spend developer time and
 sacrifice simplicity.
 
 The fact is: text generation can be endlessley accomodated, but you'll still have to
-work around its arbitrary outputs. Fundamentally, sampling is not a clean solution to a
-classification problem.
+work around its arbitrary outputs. Fundamentally, unconstrained sampling is not a clean
+solution to a classification problem.
 
 
 Solution
diff --git a/docs/source/other_llm_structuring_tools.rst b/docs/source/other_llm_structuring_tools.rst
index 8dfb88a..a3bdb99 100644
--- a/docs/source/other_llm_structuring_tools.rst
+++ b/docs/source/other_llm_structuring_tools.rst
@@ -3,16 +3,17 @@ Other LLM structuring tools
 
 There are `other LLM structuring tools
 <https://www.reddit.com/r/LocalLLaMA/comments/17a4zlf/reliable_ways_to_get_structured_output_from_llms/>`_
-which support "just pick one" functionality. You should strongly consider using them.
-`guidance <https://github.com/guidance-ai/guidance>`_, for example, provides a
-``select`` function which almost always returns a valid choice.
+which support "just pick one" functionality. You should strongly consider using them, as
+they scale independently with the number of choices. `guidance
+<https://github.com/guidance-ai/guidance>`_, for example, provides a ``select`` function
+which almost always returns a valid choice.
 
 One potential weakness of algorithms like this is that they don't always look at the
 entire choice: they exit early when the generated choice becomes unambiguous. This
-property makes the algorithm highly scalable wrt the number of choices and tokens. But
-I'm curious to see if there are tasks where looking at all of the choice's tokens—like
-CAPPr does—squeezes more out. Taking the tiny task from the previous page (where CAPPr
-succeeds):
+property makes the algorithm highly scalable wrt the number of tokens in each choice.
+But I'm curious to see if there are tasks where looking at all of the choice's
+tokens—like CAPPr does—squeezes more out. Taking the tiny task from the previous page
+(where CAPPr succeeds):
 
 .. code:: python
 
diff --git a/src/cappr/__init__.py b/src/cappr/__init__.py
index 57e0eb9..a9b2250 100644
--- a/src/cappr/__init__.py
+++ b/src/cappr/__init__.py
@@ -3,7 +3,7 @@
 
 https://cappr.readthedocs.io/
 """
-__version__ = "0.8.5"
+__version__ = "0.8.6"
 
 from . import utils
 from ._example import Example