Remove hedge words and first-person plural from 5 prose hotspots

Author: brandonh6k

content/guides/bake/index.md

@@ -88,7 +88,7 @@ line. Here's a quick summary of the options for the `default` target:
 
 - `platforms`: Platform variants to build.
 
-To execute this build, simply run the following command in the root of the
+To execute this build, run the following command in the root of the
 repository:
 
 ```console
@@ -186,7 +186,7 @@ And in the Dockerfile, add the build stage. This stage will use the official
 > [!TIP]
 > Because this stage relies on executing an external dependency, it's generally
 > a good idea to define the version you want to use as a build argument. This
-> lets you more easily manage version upgrades in the future by collocating
+> lets you manage version upgrades in the future by collocating
 > dependency versions to the beginning of the Dockerfile.
 
 ```dockerfile {hl_lines=[2,"6-8"]}
@@ -267,7 +267,7 @@ matrix variable.
 ```
 
 You'll also want to change how the image tags are assigned to these builds.
-Currently, both matrix paths would generate the same image tag names, and
+As written, both matrix paths would generate the same image tag names, and
 overwrite each other. Update the `tags` attribute use a conditional operator to
 set the tag depending on the matrix variable value.
 
@@ -498,7 +498,7 @@ build/
 Docker Buildx Bake streamlines complex build workflows, enabling efficient
 multi-platform builds, testing, and artifact export. By integrating Buildx Bake
 into your projects, you can simplify your Docker builds, make your build
-configuration portable, and wrangle complex configurations more easily.
+configuration portable, and wrangle complex configurations.
 
 Experiment with different configurations and extend your Bake files to suit
 your project's needs. You might consider integrating Bake into your CI/CD

content/guides/genai-leveraging-rag/index.md

@@ -35,7 +35,7 @@ The system operates as follows:
 2. These patterns help find matching information in a database
 3. The LLM generates responses that blend the model's inherent knowledge with the this extra information.
 
-To hold this vector information in an efficient manner, we need a special type of database.
+To hold this vector information in an efficient manner, you need a special type of database.
 
 ## Introduction to Graph databases
 
@@ -110,7 +110,7 @@ The first startup may take some time because the system needs to download a larg
 
 ### Monitoring progress
 
-We can monitor the download and initialization progress by viewing the logs. Run the following command to view the logs:
+You can monitor the download and initialization progress by viewing the logs. Run the following command to view the logs:
 
 ```bash
 docker compose logs
@@ -128,9 +128,9 @@ Wait for specific lines in the logs indicating that the download is complete and
 
 You can now access the interface at [http://localhost:8501/](http://localhost:8501/) to ask questions. For example, you can try the sample question:
 
-When we see those lines in the logs, web apps are ready to be used.
+When those lines appear in the logs, the web apps are ready to use.
 
-Since our goal is to teach AI about things it does not yet know, we begin by asking it a simple question about Nifi at
+Since the goal is to teach AI about things it does not yet know, begin by asking it a simple question about Nifi at
 [http://localhost:8501/](http://localhost:8501/).
 ![alt text](image.png)
 
@@ -140,13 +140,13 @@ RAG: Disabled
 Hello! I'm here to help you with your question about Apache NiFi. Unfortunately, I don't know the answer to that question. I'm just an AI and my knowledge cutoff is December 2022, so I may not be familiar with the latest technologies or software. Can you please provide more context or details about Apache NiFi? Maybe there's something I can help you with related to it.
 ```
 
-As we can see, AI does not know anything about this subject because it did not exist during the time of its training, also known as the information cutoff point.
+As shown, the AI does not know anything about this subject because it did not exist during the time of its training, also known as the information cutoff point.
 
 Now it's time to teach the AI some new tricks. First, connect to [http://localhost:8502/](http://localhost:8502/). Instead of using the "neo4j" tag, change it to the "apache-nifi" tag, then select the **Import** button.
 
 ![alt text](image-1.png)
 
-After the import is successful, we can access Neo4j to verify the data.
+After the import is successful, you can access Neo4j to verify the data.
 
 After logging in to [http://localhost:7474/](http://localhost:7474/) using the credentials from the `.env` file, you can run queries on Neo4j. Using the Neo4j Cypher query language, you can check for the data stored in the database.
 
@@ -162,7 +162,7 @@ To execute this query, write in the box on the top and select the blue run butto
 
 ![alt text](image-2.png)
 
-Results will appear below. What we are seeing here is the information system downloaded from Stack Overflow and saved in the graph database. RAG will utilize this information to enhance its responses.
+Results will appear below. The information shown is downloaded from Stack Overflow and saved in the graph database. RAG will utilize this information to enhance its responses.
 
 You can also run the following query to visualize the data:
 
@@ -176,7 +176,7 @@ To check the relationships in the database, run the following query:
 CALL db.relationshipTypes()
 ```
 
-Now, we are ready to enable our LLM to use this information. Go back to [http://localhost:8501/](http://localhost:8501/), enable the **RAG** checkbox, and ask the same question again. The LLM will now provide a more detailed answer.
+You're ready to enable the LLM to use this information. Go back to [http://localhost:8501/](http://localhost:8501/), enable the **RAG** checkbox, and ask the same question again. The LLM will provide a more detailed answer.
 
 ![alt text](image-3.png)
 
@@ -209,7 +209,7 @@ For optimal results, choose a tag that the LLM is not familiar with.
 
 ### When to leverage RAG for optimal results
 
-Retrieval-Augmented Generation (RAG) is particularly effective in scenarios where standard Large Language Models (LLMs) fall short. The three key areas where RAG excels are knowledge limitations, business requirements, and cost efficiency. Below, we explore these aspects in more detail.
+Retrieval-Augmented Generation (RAG) is particularly effective in scenarios where standard Large Language Models (LLMs) fall short. The three key areas where RAG excels are knowledge limitations, business requirements, and cost efficiency. The following sections explore these aspects in more detail.
 
 #### Overcoming knowledge limitations
 

content/guides/golang/build-images.md

@@ -195,7 +195,7 @@ This should be familiar. The result of that command will be a static application
 binary named `docker-gs-ping` and located in the root of the filesystem of the
 image that you are building. You could have put the binary into any other place
 you desire inside that image, the root directory has no special meaning in this
-regard. It's just convenient to use it to keep the file paths short for improved
+regard. It's convenient to use it to keep the file paths short for improved
 readability.
 
 Now, all that is left to do is to tell Docker what command to run when your
@@ -275,7 +275,7 @@ $ docker build --tag docker-gs-ping .
 ```
 
 The build process will print some diagnostic messages as it goes through the build steps.
-The following is just an example of what these messages may look like.
+The following is an example of what these messages may look like.
 
 ```console
 [+] Building 2.2s (15/15) FINISHED
@@ -310,7 +310,7 @@ successfully built your image named `docker-gs-ping`.
 
 To see the list of images you have on your local machine, you have two options.
 One is to use the CLI and the other is to use [Docker
-Desktop](/manuals/desktop/_index.md). Since you're currently working in the
+Desktop](/manuals/desktop/_index.md). Since you're working in the
 terminal, take a look at listing images with the CLI.
 
 To list images, run the `docker image ls`command (or the `docker images` shorthand):
@@ -350,7 +350,7 @@ $ docker image tag docker-gs-ping:latest docker-gs-ping:v1.0
 ```
 
 The Docker `tag` command creates a new tag for the image. It doesn't create a
-new image. The tag points to the same image and is just another way to reference
+new image. The tag points to the same image and is another way to reference
 the image.
 
 Now run the `docker image ls` command again to see the updated list of local
@@ -459,7 +459,7 @@ ENTRYPOINT ["/docker-gs-ping"]
 Since you have two Dockerfiles now, you have to tell Docker what Dockerfile
 you'd like to use to build the image. Tag the new image with `multistage`. This
 tag (like any other, apart from `latest`) has no special meaning for Docker,
-it's just something you chose.
+it's something you chose.
 
 ```console
 $ docker build -t docker-gs-ping:multistage -f Dockerfile.multistage .

content/guides/golang/develop.md

@@ -582,7 +582,7 @@ The exact value doesn't really matter for this example, because you run Cockroac
 
 ### Merging Compose files
 
-The file name `compose.yaml` is the default file name which `docker compose` command recognizes if no `-f` flag is provided. This means you can have multiple Docker Compose files if your environment has such requirements. Furthermore, Docker Compose files are... composable (pun intended), so multiple files can be specified on the command line to merge parts of the configuration together. The following list is just a few examples of scenarios where such a feature would be very useful:
+The file name `compose.yaml` is the default file name which `docker compose` command recognizes if no `-f` flag is provided. This means you can have multiple Docker Compose files if your environment has such requirements. Furthermore, Docker Compose files are... composable (pun intended), so multiple files can be specified on the command line to merge parts of the configuration together. The following list shows a few examples of scenarios where such a feature would be very useful:
 
 - Using a bind mount for the source code for local development but not when running the CI tests;
 - Switching between using a pre-built image for the frontend for some API application vs creating a bind mount for source code;

content/guides/rust/run-containers.md

@@ -173,7 +173,7 @@ Run the `docker ps --all` command again to see that Docker removed all container
 
 Now, it's time to address the random naming issue. Standard practice is to name your containers for the simple reason that it's easier to identify what's running in the container and what application or service it's associated with.
 
-To name a container, you just need to pass the `--name` flag to the `docker run` command.
+To name a container, pass the `--name` flag to the `docker run` command.
 
 ```console
 $ docker run -d -p 3001:8000 --name docker-rust-container docker-rust-image-dhi
@@ -183,11 +183,11 @@ CONTAINER ID   IMAGE                   COMMAND                  CREATED
 219b2e3c7c38   docker-rust-image-dhi   "/server"                6 seconds ago   Up 5 seconds   0.0.0.0:3001->8000/tcp, [::]:3001->8000/tcp   docker-rust-container
 ```
 
-That’s better! You can now easily identify your container based on the name.
+That’s better! You can now identify your container based on the name.
 
 ## Summary
 
-In this section, you took a look at running containers. You also took a look at managing containers by starting, stopping, and restarting them. And finally, you looked at naming your containers so they are more easily identifiable.
+In this section, you took a look at running containers. You also took a look at managing containers by starting, stopping, and restarting them. And finally, you looked at naming your containers so they are more identifiable.
 
 Related information: