diff --git a/docs/source/features/sampling.md b/docs/source/features/sampling.md
index 5d6db8626b..43c8ed4d43 100644
--- a/docs/source/features/sampling.md
+++ b/docs/source/features/sampling.md
@@ -1,19 +1,50 @@
 # Sampling
-The PyTorch backend supports most of the sampling features that are supported on the C++ backend, such as temperature, top-k and top-p sampling, beam search, stop words, bad words, penalty, context and generation logits, log probability and logits processors
+
+The Pytorch backend supports a wide variety of features, listed below:
+
+| Forward Pass       | Sampling Strategies              | Sampling Features              |
+|--------------------|----------------------------------|--------------------------------|
+| No drafting        |  Greedy                          | Guided Decoding                |
+| Draft target model |  TopP                            | Plugging Logits Post-Processor |
+| Eagle 3            |  TopK                            | Temperature                    |
+| Ngram              |  TopK + TopP                     | MinP                           |
+|                    |  Beam Search                     | Embedding / Logits Bias        |
+|                    |  Best of / n (composable)        | Stop criteria                  |
+|                    |  Rejection sampling (composable) | Return Logits                  |
+|                    |                                  | Return LogProbs                |
+|                    |                                  | TopK LogProbs                  |
 
 ## General usage
 
-To use the feature:
+There are two sampling backends available.
 
-1. Enable the `enable_trtllm_sampler` option in the `LLM` class
-2. Pass a [`SamplingParams`](source:tensorrt_llm/sampling_params.py#L125) object with the desired options to the `generate()` function
+* Torch Sampler
+* TRTLLM Sampler
 
-The following example prepares two identical prompts which will give different results due to the sampling parameters chosen:
+Torch Sampler currently supports a superset of features of TRTLLM Sampler, and is intended as the long term solution. One can specify which sampler to use explicitly with:
+
+```python
+from tensorrt_llm import LLM
+
+# Chooses TorchSampler explicitly
+llm = LLM(model='nvidia/Llama-3.1-8B-Instruct-FP8',
+          sampler_type="TorchSampler")
+
+# Chooses TRTLLMSampler explicitly
+llm = LLM(model='nvidia/Llama-3.1-8B-Instruct-FP8',
+          sampler_type="TRTLLMSampler")
+```
+
+By default, the sampling backend is chosen to be `auto`. This will use:
+
+* TRTLLM Sampler when using Beam Search.
+* Torch Sampler otherwise.
+
+Here is an example to run a model with basic usage of sampling parameters. This example prepares two identical prompts which will give different results due to the sampling parameters chosen:
 
 ```python
 from tensorrt_llm import LLM, SamplingParams
-llm = LLM(model='nvidia/Llama-3.1-8B-Instruct-FP8',
-          enable_trtllm_sampler=True)
+llm = LLM(model='nvidia/Llama-3.1-8B-Instruct-FP8')
 sampling_params = SamplingParams(
         temperature=1.0,
         top_k=8,
@@ -23,7 +54,24 @@ llm.generate(["Hello, my name is",
             "Hello, my name is"], sampling_params)
 ```
 
-Note: The `enable_trtllm_sampler` option is not currently supported when using speculative decoders, such as MTP or Eagle-3, so there is a smaller subset of sampling options available.
+It is also possible to specify different sampling parameters on a per-prompt basis:
+
+```python
+from tensorrt_llm import LLM, SamplingParams
+llm = LLM(model='nvidia/Llama-3.1-8B-Instruct-FP8')
+sampling_params_0 = SamplingParams(
+        temperature=1.0,
+        top_k=8,
+        top_p=0.5,
+    )
+sampling_params_1 = SamplingParams(
+        top_k=4,
+    )
+llm.generate(["Hello, my name is",
+            "Hello, my name is"],
+            [sampling_params_0,
+            sampling_params_1])
+```
 
 ### LLM API sampling behavior when using Torch Sampler
 
@@ -79,8 +127,6 @@ To enable beam search, you must:
 
 1. Enable the `use_beam_search` option in the `SamplingParams` object
 2. Set the `max_beam_width` parameter in the `LLM` class to match the `best_of` parameter in `SamplingParams`
-3. Disable overlap scheduling using the `disable_overlap_scheduler` parameter of the `LLM` class
-4. Disable the usage of CUDA Graphs by passing `None` to the `cuda_graph_config` parameter of the `LLM` class
 
 Parameter Configuration:
 - `best_of`: Controls the number of beams processed during generation (beam width)
@@ -93,10 +139,8 @@ The following example demonstrates beam search with a beam width of 4, returning
 ```python
 from tensorrt_llm import LLM, SamplingParams
 llm = LLM(model='nvidia/Llama-3.1-8B-Instruct-FP8',
-          enable_trtllm_sampler=True,
           max_beam_width=4,   # must equal SamplingParams.best_of
-          disable_overlap_scheduler=True,
-          cuda_graph_config=None)
+    )
 sampling_params = SamplingParams(
         best_of=4,   # must equal LLM.max_beam_width
         use_beam_search=True,