Docs: Language pass (#167)

## Description 📝

## Quick Links 🚀

<!-- Links to the relevant place(s) in the CloudFlare Pages build. Wait
for CF comment for link. -->

## Assertion Tests 🤖

<!-- Add assertions between the comments below to have ChatGPT check the
quality of your docs contribution (Diff) and
how well it integrates with existing docs. E.g., A user should be able
to easily understand how to make a simple
query. -->

<!-- For more info, see the Action's docs in the marketplace:
https://github.com/marketplace/actions/docs-assertion-tester#usage -->

<!-- DX:Assertion-start -->
<!-- DX:Assertion-end -->

Author: robertjdominguez

docs/capabilities.mdx

@@ -62,10 +62,10 @@ The AI functions extend these capabilities beyond pure data analysis:
 | Visualize | Create interactive data visualizations.      |
 
 By combining these primitives, PromptQL can tackle complex analytical tasks. For example, it might extract structured
-data from a batch of documents, classify the results, and visualize the patterns—all in a single workflow. Or it could
+data from a batch of documents, classify the results, and visualize the patterns...all in a single workflow. Or it could
 summarize a large dataset, then extract key metrics from those summaries for deeper analysis.
 
-These combinations aren't just powerful—they're precise. Each primitive builds on the others, with error checking and
+These combinations aren't just powerful, they're precise. Each primitive builds on the others, with error checking and
 validation at every step. The result is reliable, reproducible analysis that can be turned into automated workflows.
 
 :::info Learn more
@@ -100,15 +100,15 @@ When it comes to decision-making, PromptQL acts as your analytical partner. It c
 visualize trends, and run comparisons. Each analysis can be saved for future reference, shared with your team, or used
 as part of your audit trail.
 
-The key is that PromptQL doesn't just show you numbers—it helps you understand them. Ask it to explain its methodology,
+The key is that PromptQL doesn't just show you numbers: it helps you understand them. Ask it to explain its methodology,
 break down complex metrics, or look at the same data from different angles. It's built to clarify, not just calculate.
 
 [Learn more](/decision-making.mdx) about making decisions with PromptQL.
 
 ### Automate tasks
 
 Once you've found a valuable analysis pattern, PromptQL can turn it into a reliable automation. These aren't just saved
-queries—they're full workflows that can process new data, generate reports, monitor metrics, or alert on conditions.
+queries, but full workflows that can process new data, generate reports, monitor metrics, or alert on conditions.
 
 Each automation comes with built-in error handling and can be parameterized to handle different inputs. Chain them
 together for more complex operations, or schedule them to run regularly. The goal is to turn your one-off analyses into

docs/decision-making.mdx

@@ -18,7 +18,7 @@ import Thumbnail from "@site/src/components/Thumbnail";
 
 **Decision making** with PromptQL supports deeper analysis and structured exploration across your data. You can ask
 complex, layered questions and get responses that adapt to your systems and terminology. PromptQL helps you drill into
-root causes, compare across categories, and evaluate tradeoffs—without being limited by context windows or informal
+root causes, compare across categories, and evaluate tradeoffs without being limited by context windows or informal
 language.
 
 This is useful for scenarios that require exploration and judgment, such as:
@@ -29,19 +29,19 @@ This is useful for scenarios that require exploration and judgment, such as:
 
 ## Guides
 
-Below, we've split out a few different use cases as examples. You can run these against the
-[public sandbox-quickstart project](https://promptql.console.hasura.io/public/sandbox-movie-studio/playground).
+Below, we've split out a few different use cases as examples. You can run these against the `sandbox-movie-studio`
+[project](https://promptql.console.hasura.io/public/sandbox-movie-studio/playground).
 
 ### Q&A
 
-It would seem that asking questions about data is simple, but this is difficult because business terms often map to
-multiple systems or concepts. For example, "performance" might refer to revenue, engagement, or critical ratings,
-depending on who’s asking. Systems don’t always agree on how those values are calculated, either.
-
 Take this example:
 
 > _How did our PG-13 portfolio perform against R-rated titles during the streaming transition period?_
 
+It would seem that asking questions about data is simple, but this is difficult because business terms often map to
+multiple systems or concepts. For example, "performance" might refer to revenue, engagement, or critical ratings,
+depending on who’s asking. Systems don’t always agree on how those values are calculated, either.
+
 PromptQL solves this by mapping ambiguous terms to precise system definitions and building a plan that retrieves data
 from the right sources in the correct form. The generated plan accounts for relevant time windows, content ratings, and
 distribution channels, producing a structured answer tailored to your domain.
@@ -84,8 +84,8 @@ patterns across time or categories.
 
 > _Can you analyze the ROI patterns of our genre-blending titles compared to pure-genre releases between 2010–2020?_
 
-PromptQL is unique because it treats research as a process—not just a query. It generates a plan that defines discovery
-phases, collects and segments relevant data, and evaluates each hypothesis systematically.
+PromptQL is unique because it treats research as a process and not just a query. It generates a plan that defines
+discovery phases, collects and segments relevant data, and evaluates each hypothesis systematically.
 
 <Thumbnail src="/img/get-started/playground-deep-research.png" alt="Details of a deployed automation." />
 

docs/how-to-talk-to-promptql.mdx

@@ -24,7 +24,7 @@ shaping how PromptQL thinks about your business, your data, and your language.
 ## Do's
 
 PromptQL works best when you treat it like a collaborator. Ask specific questions in natural language. There's no need
-to speak in SQL or use exact technical phrases—just describe what you’re curious about.
+to speak in SQL or use exact technical phrases. Just describe what you’re curious about.
 
 Examples:
 
@@ -34,7 +34,7 @@ Show me churn by region.
 Break this down by customer type.
 ```
 
-It helps to add context—especially for company-specific terms, business rules, or timeframes. PromptQL will do its best
+It helps to add context, especially for company-specific terms, business rules, or timeframes. PromptQL will do its best
 to infer meaning, but your clarification speeds things up and improves accuracy.
 
 As you go, don’t hold back feedback. If an answer is off, say so. Corrections make it smarter. You can say:
@@ -57,7 +57,7 @@ Show a chart of the trend.
 Group by location and limit to the top 10.
 ```
 
-If you're unsure about terminology, just ask. PromptQL can define terms or explain metrics—it’s built to clarify, not
+If you're unsure about terminology, just ask. PromptQL can define terms or explain metrics. It’s built to clarify, not
 confuse.
 
 ## Don'ts