diff --git a/AI_Agents_Guide/Constrained_Decoding/README.md b/AI_Agents_Guide/Constrained_Decoding/README.md
index 0fbf37d0..28e07417 100644
--- a/AI_Agents_Guide/Constrained_Decoding/README.md
+++ b/AI_Agents_Guide/Constrained_Decoding/README.md
@@ -162,7 +162,7 @@ assistant
 ### Example 2
 
 Optionally, we can also restrict an output to a specific schema. For example,
-in [`client.py`](./artifacts/client.py) we use a `pydentic` library to define the
+in [`client.py`](./artifacts/client.py) we use a `pydantic` library to define the
 following answer format:
 
 ```python
@@ -279,7 +279,7 @@ self.executor = trtllm.Executor(model_path=...,
 ...
 ```
 
-Additionally, if you want to enable logits pos-processor for every request
+Additionally, if you want to enable logits post-processor for every request
 individually, you can do so via an additional `input` parameter.
 For example, in this tutorial we will add `logits_post_processor_name` in
 `inflight_batcher_llm/tensorrt_llm/config.pbtxt`:
diff --git a/AI_Agents_Guide/Constrained_Decoding/artifacts/utils.py b/AI_Agents_Guide/Constrained_Decoding/artifacts/utils.py
index fb476d49..70ef4237 100644
--- a/AI_Agents_Guide/Constrained_Decoding/artifacts/utils.py
+++ b/AI_Agents_Guide/Constrained_Decoding/artifacts/utils.py
@@ -25,9 +25,8 @@
 # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 
 import json
-import typing
 from collections import defaultdict
-from typing import DefaultDict, Dict
+from typing import DefaultDict, Dict, List
 
 import torch
 from lmformatenforcer import JsonSchemaParser, TokenEnforcer
@@ -102,7 +101,7 @@ def __call__(
         self,
         req_id: int,
         logits: torch.Tensor,
-        ids: typing.List[typing.List[int]],
+        ids: List[List[int]],
         stream_ptr: int,
     ):
         # Create a mask with negative infinity to block all tokens initially.
@@ -140,12 +139,10 @@ def __call__(
         self,
         req_id: int,
         logits: torch.Tensor,
-        ids: typing.List[typing.List[int]],
+        ids: List[List[int]],
         stream_ptr: int,
     ):
-        # Initialize the FSM state dictionary if the input_ids are empty,
-        # as this means that the input_ids are the first tokens of the sequence.
-        seq_id = hash(tuple(ids[0]))
+        seq_id = None
         # If the prefix token IDs have changed we assume that we are dealing
         # with a new sample and reset the FSM state
         if (