Update tutorial

Author: mandel

docs/tutorial.md

@@ -33,19 +33,11 @@ Hello, world!
 ##  Calling an LLM
 
 ```yaml
-description: Hello world calling a model
-text:
-- Hello,
-- model: watsonx/ibm/granite-34b-code-instruct
-  parameters:
-    decoding_method: greedy
-    stop:
-    - '!'
-    include_stop_sequence: true
+--8<-- "./examples/tutorial/calling_llm.pdl"
 ```
 
-In this program ([file](https://github.com/IBM/prompt-declaration-language//blob/main/examples/tutorial/calling_llm.pdl)), the `text` starts with the word `Hello,`, and we call a model (`watsonx/ibm/granite-34b-code-instruct`) with this as input prompt. Notice the Watsonx model id on LiteLLM.
-The model is passed some parameters including the `decoding_method` and `stop`, which corresponds to the `stop_sequences` parameter in Watsonx. The stop sequences are to be included in the output. 
+In this program ([file](https://github.com/IBM/prompt-declaration-language//blob/main/examples/tutorial/calling_llm.pdl)), the `text` starts with the word `Hello,`, and we call a model (`watsonx/ibm/granite-34b-code-instruct`) with this as input prompt. Notice the watsonx model id on LiteLLM.
+The model is passed some parameters including the `decoding_method` and `stop`, which corresponds to the `stop_sequences` parameter in watsonx. The stop sequences are to be included in the output. 
 
 A PDL program computes 2 data structures. The first is a JSON corresponding to the result of the overall program, obtained by aggregating the results of each block. This is what is printed by default when we run the interpreter. The second is a conversational background context, which is a list of role/content pairs, where we implicitly keep track of roles and content for the purpose of communicating with models that support chat APIs. The contents in the latter correspond to the results of each block. The conversational background context is what is used to make calls to LLMs via LiteLLM.
 
@@ -62,12 +54,7 @@ where the portion ` world!` has been generated by Granite.
 
 Here's another of model call that includes an `input` field ([file](https://github.com/IBM/prompt-declaration-language//blob/main/examples/tutorial/calling_llm_with_input.pdl)):
 ```yaml
-description: Hello world calling a model
-text:
-- "Hello,"
-- model: watsonx/ibm/granite-20b-multilingual
-  input:
-    Translate the word 'world' to French
+--8<-- "./examples/tutorial/calling_llm_with_input.pdl"
 ```
 
 In this case, we make a call to the granite multilingual model, and the input passed to the model is the sentence: `Translate the word 'world' to French` and nothing else from the surrounding document. When we execute this program, we obtain:
@@ -78,9 +65,9 @@ Le mot 'world' en français est 'monde'.
 ```
 where everything after the `:` including it were generated by the model.
 
-### Parameter defaults for Watsonx Granite models
+### Parameter defaults for watsonx Granite models
 
-PDL provides the following defaults for Watsonx Granite models, when the following parameters are missing:
+PDL provides the following defaults for watsonx Granite models, when the following parameters are missing:
   - `decoding_method`: `greedy`
   - `max_new_tokens`: 1024
   - `min_new_tokens`: 1
@@ -128,18 +115,7 @@ GEN is equal to:  world!
 In PDL, we can declaratively chain models together as in the following example ([file](https://github.com/IBM/prompt-declaration-language//blob/main/examples/tutorial/model_chaining.pdl)):
 
 ```yaml
-description: Model chaining
-text:
-- Hello,
-- model: watsonx/ibm/granite-34b-code-instruct
-  parameters:
-    stop: ["!"]
-    include_stop_sequence: true
-- "\nTranslate this to French\n"
-- model: watsonx/ibm/granite-20b-multilingual
-  parameters:
-    stop: ["!"]
-    include_stop_sequence: true
+--8<-- "./examples/tutorial/model_chaining.pdl"
 ```
 
 In this program, the first call is to a granite model to complete the sentence `Hello, world!`. The following block in the document prints out the sentence: `Translate this to French`. The final line of the program takes the entire document produced so far and passes it as input to the granite multilingual model. Notice that the input passed to this model is the document up to that point, represented as a conversation. This makes it easy to chain models together and continue building on previous interactions.