✨ Add usage documentation

docs/getting-started/usage.md

@@ -0,0 +1,44 @@
+# Usage
+
+To write your cognitive architectures with the funcchain syntax you need to import the `chain` function from the `funcchain` package.
+
+```python
+from funcchain import chain
+```
+
+This chain function it the core component of funcchain.
+It takes the docstring, input arguments and return type of the function and compiles everything into a langchain prompt.
+It then executes the prompt with your input arguments if you call the function and returns the parsed result.
+
+```python
+def hello(lang1: str, lang2: str, lang3: str) -> list[str]:
+    """
+    Say hello in these 3 languages.
+    """
+    return chain()
+
+hello("German", "French", "Spanish")
+```
+
+The underlying chat in the background will look like this:
+
+```html
+<HumanMessage>
+LANG1: German
+LANG2: French
+LANG3: Spanish
+
+Say hello in these 3 languages.
+</HumanMessage>
+
+<AIMessage>
+{
+    "value": ["Hallo", "Bonjour", "Hola"]
+}
+</AIMessage>
+```
+
+Funcchain is handling all the redundant and complicated structuring of your prompts so you can focus on the important parts of your code.
+
+All input arguments are automatically added to the prompt so the model has context about what you insert.
+The return type is used to force the model using a json-schema to always return a json object in the desired shape.