📝 Fix typos and missing hyperlinks in "Building Block" Docs

docs/docs/building-blocks/1-language_models.md

@@ -10,7 +10,7 @@ Let's first make sure you can set up your language model. DSPy support clients f
 
 ## Setting up the LM client.
 
-You can just call the constructor that connects to the LM. Then, use `dspy.configure` to declare this as the dexfault LM.
+You can just call the constructor that connects to the LM. Then, use `dspy.configure` to declare this as the default LM.
 
 For example, to use OpenAI language models, you can do it as follows.
 

docs/docs/building-blocks/3-modules.md

@@ -6,7 +6,7 @@ sidebar_position: 3
 
 A **DSPy module** is a building block for programs that use LMs.
 
-- Each built-in module abstracts a **prompting technique** (like chain of thought or ReAct). Crucially, they are generalized to handle any [DSPy Signature].
+- Each built-in module abstracts a **prompting technique** (like chain of thought or ReAct). Crucially, they are generalized to handle any [DSPy Signature](https://dspy-docs.vercel.app/docs/building-blocks/signatures).
 
 - A DSPy module has **learnable parameters** (i.e., the little pieces comprising the prompt and the LM weights) and can be invoked (called) to process inputs and return outputs.
 
@@ -17,7 +17,7 @@ A **DSPy module** is a building block for programs that use LMs.
 
 Let's start with the most fundamental module, `dspy.Predict`. Internally, all other DSPy modules are just built using `dspy.Predict`.
 
-We'll assume you are already at least a little familiar with [DSPy signatures], which are declarative specs for defining the behavior of any module we use in DSPy.
+We'll assume you are already at least a little familiar with [DSPy signatures](https://dspy-docs.vercel.app/docs/building-blocks/signatures), which are declarative specs for defining the behavior of any module we use in DSPy.
 
 To use a module, we first **declare** it by giving it a signature. Then we **call** the module with the input arguments, and extract the output fields!
 

docs/docs/building-blocks/7-assertions.md

@@ -30,7 +30,7 @@ Specifically, when a constraint is not met:
     - Past Output: your model's past output that did not pass the validation_fn
     - Instruction: your user-defined feedback message on what went wrong and what possibly to fix
 
-If the error continues past the `max_backtracking_attempts`, then `dspy.Assert` will halt the pipeline execution, altering you with an `dspy.AssertionError`. This ensures your program doesn't continue executing with “bad” LM behavior and immediately highlights sample failure outputs for user assessment. 
+If the error continues past the `max_backtracking_attempts`, then `dspy.Assert` will halt the pipeline execution, alerting you with an `dspy.AssertionError`. This ensures your program doesn't continue executing with “bad” LM behavior and immediately highlights sample failure outputs for user assessment.
 
 - **dspy.Suggest vs. dspy.Assert**: `dspy.Suggest` on the other hand offers a softer approach. It maintains the same retry backtracking as `dspy.Assert` but instead serves as a gentle nudger. If the model outputs cannot pass the model constraints after the `max_backtracking_attempts`, `dspy.Suggest` will log the persistent failure and continue execution of the program on the rest of the data. This ensures the LM pipeline works in a "best-effort" manner without halting execution.