Harrison/redo docs (#130)

Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>

docs/getting_started/chains.md

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+# Using Chains
+
+Calling an LLM is a great first step, but it's just the beginning.
+Normally when you use an LLM in an application, you are not sending user input directly to the LLM.
+Instead, you are probably taking user input and constructing a prompt, and then sending that to the LLM.
+
+For example, in the previous example, the text we passed in was hardcoded to ask for a name for a company that made colorful socks.
+In this imaginary service, what we would want to do is take only the user input describing what the company does, and then format the prompt with that information.
+
+This is easy to do with LangChain!
+
+First lets define the prompt:
+
+```python
+from langchain.prompts import Prompt
+prompt = Prompt(
+    input_variables=["product"], 
+    template="What is a good name for a company that makes {product}?",
+)
+```
+
+We can now create a very simple chain that will take user input, format the prompt with it, and then send it to the LLM:
+
+```python
+from langchain.chains import LLMChain
+chain = LLMChain(llm=llm, prompt=prompt)
+```
+
+Now we can run that can only specifying the product!
+
+```python
+chain.run("colorful socks")
+```
+
+There we go! There's the first chain.
+
+That is it for the Getting Started example. 
+As a next step, we would suggest checking out the more complex chains in the [Demos section](/examples/demos.rst)