Update example-llm-workflows/README.md

Co-authored-by: Brian Ginsburg <[email protected]>
Signed-off-by: Zeeshan Lakhani <[email protected]>

example-llm-workflows/README.md

@@ -323,7 +323,7 @@ Here's an example response provided by the model from the executed workflow:
 curl localhost:3000/run --json @map_reduce.json
 ```
 
-To more applicability encode the [MapReduce][map-reduce] example from the [Crowdforge][crowdforge] paper, I implemented a `prompt_chain` Wasm/Wasi function registered on the Host that takes in a system prompt (e.g. "You are journalist writing about cities."), an input (e.g. an ongoing article), a map step prompt with a `{{text}}` placeholder that is filled in, a reduce step, which folds over (combines) the generated text(s) from the map step, and then the optional LLaMA model stored as a [`gguf`][gguf]. If the optional model path is not provided, the Host will fall back to the default `Meta-Llama-3-8B-Instruct.Q4_0.gguf` model.
+To more applicability encode the [MapReduce][map-reduce] example from the [Crowdforge][crowdforge] paper, I implemented a `prompt_chain` Wasm/WASI function registered on the Host that takes in a system prompt (e.g. "You are journalist writing about cities."), an input (e.g. an ongoing article), a map step prompt with a `{{text}}` placeholder that is filled in, a reduce step, which folds over (combines) the generated text(s) from the map step, and then the optional LLaMA model stored as a [`gguf`][gguf]. If the optional model path is not provided, the Host will fall back to the default `Meta-Llama-3-8B-Instruct.Q4_0.gguf` model.
 
 ```rust
 async fn prompt_chain(