Using clearer wording to describe code blocks

docs/50-dev-env/2-setup-pre-reqs.mdx

@@ -1,3 +1,3 @@
 # 👐 Setup prerequisites
 
-Fill in any `<CODE_BLOCK>`s and run the cells under the **Step 1: Install libraries** and **Step 2: Setup prerequisites** sections in the notebook.
+Fill in any `<CODE_BLOCK_N>` placeholders and run the cells under the **Step 1: Install libraries** and **Step 2: Setup prerequisites** sections in the notebook.

docs/60-agent-tools/2-knowledge-base.mdx

@@ -2,7 +2,7 @@
 
 One of the tools that the AI research agent has access to is a question-answering tool that retrieves information from a knowledge base (MongoDB collection) and uses it to answer questions. But first, we need to create the knowledge base.
 
-Fill in any `<CODE_BLOCK>`s and run the cells under the **Step 3: Create a knowledge base** section in the notebook to download a dataset of Arxiv papers from [Hugging Face](https://huggingface.co/datasets/mongodb-eai/arxiv-embeddings) and ingest it into a MongoDB collection which will serve as our agent's knowledge base.
+Fill in any `<CODE_BLOCK_N>` placeholders and run the cells under the **Step 3: Create a knowledge base** section in the notebook to download a dataset of Arxiv papers from [Hugging Face](https://huggingface.co/datasets/mongodb-eai/arxiv-embeddings) and ingest it into a MongoDB collection which will serve as our agent's knowledge base.
 
 The answers for code blocks in this section are as follows:
 

docs/60-agent-tools/4-chat-completion-llm.mdx

@@ -4,7 +4,7 @@ Let's instantiate the chat completion LLM to use as the "brain" of our agent and
 
 We will use Fireworks AI's free AND open-source _firefunction-v1_ model via the _ChatFireworks_ API in LangChain.
 
-Fill in any `<CODE_BLOCK>`s and run the cells under the **Step 5: Instantiate chat completion LLM** section in the notebook to create an instance of _ChatFireworks_ with the _firefunction-v1_ model.
+Fill in any `<CODE_BLOCK_N>` placeholders and run the cells under the **Step 5: Instantiate chat completion LLM** section in the notebook to create an instance of _ChatFireworks_ with the _firefunction-v1_ model.
 
 The answers for code blocks in this section are as follows:
 

docs/60-agent-tools/5-create-agent-tools.mdx

@@ -12,7 +12,7 @@ We want the AI research agent to have access to the following tools:
 
 * `answer_questions_about_topics`: Answer questions about a given topic based on information in the agent's knowledge base
 
-Fill in any `<CODE_BLOCK>`s and run the cells under the **Step 6: Create agent tools** section in the notebook to create tools for the agent to use.
+Fill in any `<CODE_BLOCK_N>` placeholders and run the cells under the **Step 6: Create agent tools** section in the notebook to create tools for the agent to use.
 
 :::tip
 The `get_paper_metadata_arxiv` tool has been defined for you. Use this as inspiration to create the other two tools. The tool names and docstrings have been written out for you. All you have to do is code up the tool logic.

docs/60-agent-tools/6-add-web-search.mdx

@@ -2,7 +2,7 @@
 
 The knowledge base we created has limited information. Use web search to broaden the scope of the `answer_questions_about_topics` tool.
 
-Fill in any `<CODE_BLOCK>`s and run the cells under the **🦸Use web search to get information** section in the notebook to use DuckDuckGo search if the information required to answer a particular question is not present in the knowledge base.
+Fill in any `<CODE_BLOCK_N>` placeholders and run the cells under the **🦸Use web search to get information** section in the notebook to use DuckDuckGo search if the information required to answer a particular question is not present in the knowledge base.
 
 **CODE_BLOCK_17**
 

docs/70-create-agents/2-tool-calling-agent.mdx

@@ -2,7 +2,7 @@
 
 Let's start by creating a basic tool-calling agent using the `create_tool_calling_agent` constructor in LangChain.
 
-Fill in any `<CODE_BLOCK>`s and run the cells under the **Step 7: Create a basic tool-calling agent** section in the notebook to create a basic tool-calling agent.
+Fill in any `<CODE_BLOCK_N>` placeholders and run the cells under the **Step 7: Create a basic tool-calling agent** section in the notebook to create a basic tool-calling agent.
 
 :::tip
 Skip over the **🦸 CoT prompting** section for now. Come back to it if time permits.

docs/70-create-agents/3-react-agent.mdx

@@ -2,7 +2,7 @@
 
 Now let's try out an agent that uses ReAct prompting using the `create_react_agent` constructor in LangChain.
 
-Fill in any `<CODE_BLOCK>`s and run the cells under the **Step 8: Create a ReAct agent** section in the notebook to create a ReAct agent.
+Fill in any `<CODE_BLOCK_N>` placeholders and run the cells under the **Step 8: Create a ReAct agent** section in the notebook to create a ReAct agent.
 
 The answers for code blocks in this section are as follows:
 

docs/70-create-agents/5-agent-without-abstraction.mdx

@@ -4,7 +4,7 @@ The `create_tool_calling_agent` constructor in LangChain makes it easy to create
 
 As a challenge, try creating a tool-calling agent without using the `create_tool_calling_agent` constructor.
 
-To do this, fill in any `<CODE_BLOCK>`s and run the cells under the **🦸 Create a custom agent without using abstractions** section in the notebook.
+To do this, fill in any `<CODE_BLOCK_N>` placeholders and run the cells under the **🦸 Create a custom agent without using abstractions** section in the notebook.
 
 The answers for code blocks in this section are as follows:
 

docs/80-adding-memory/2-adding-memory.mdx

@@ -4,7 +4,7 @@ The final step in this lab is to add conversational message history as a form of
 
 Message history in this case will be stored in and retrieved from a MongoDB collection.
 
-Fill in any `<CODE_BLOCK>`s and run the cells under the **Step 9: Add memory to agents using MongoDB** section in the notebook to add memory to the basic tool calling agent we created previously.
+Fill in any `<CODE_BLOCK_N>` placeholders and run the cells under the **Step 9: Add memory to agents using MongoDB** section in the notebook to add memory to the basic tool calling agent we created previously.
 
 The answers for code blocks in this section are as follows: