a bit more formatting

README.md

@@ -25,7 +25,7 @@ If you know PyTorch, you know 80% of TextGrad.
 Let's walk through the key components with a simple example. Say we want to use GPT-4o to generate a punchline for TextGrad.
 ```python
 import textgrad as tg
-# Step 1: Get an initial response from an LLM
+# Step 1: Get an initial response from an LLM.
 model = tg.BlackboxLLM("gpt-4o")
 punchline = model(tg.Variable("write a punchline for my github package about optimizing compound AI systems", role_description="prompt", requires_grad=False))
 punchline.set_role_description("a concise punchline that must hook everyone")
@@ -34,23 +34,24 @@ punchline.set_role_description("a concise punchline that must hook everyone")
 Initial `punchline` from the model:
 > Supercharge your AI synergy with our optimization toolkit – where compound intelligence meets peak performance!
 
-Not bad, but we (gpt-4o, i guess) can do better! Let's optimize the punchline using TextGrad.
+Not bad, but maybe GPT-4o can do better! Let's optimize the punchline using TextGrad. In this case `punchline` would be the variable we want to optimize and improve.
 ```python
 # Step 2: Define the loss function and the optimizer, just like in PyTorch!
 loss_fn = tg.TextLoss("We want to have a super smart and funny punchline. Is the current one concise and addictive? Is the punch fun, makes sense, and subtle enough?")
 optimizer = tg.TGD(parameters=[punchline])
 ```
 
 ```python
-# Step 3: Do the loss computation, backward pass, and update the punchline
+# Step 3: Do the loss computation, backward pass, and update the punchline.
 loss = loss_fn(punchline)
 loss.backward()
 optimizer.step()
 ```
+
 Optimized punchline:
 > Boost your AI with our toolkit – because even robots need a tune-up!
 
-Okay this model isn’t really ready for a comedy show yet (and maybe a bit cringy) but it is clearly trying. But who gets to maxima in one step? 
+Okay this model isn’t really ready for a comedy show yet but it is clearly trying. But who gets to maxima in one step? 
 
 <br>
 We have many more examples around how TextGrad can optimize all kinds of variables -- code, solutions to problems, molecules, prompts, and all that!