Copy improvements

docs/guides/ai-agents/generate-translate-copy.mdx

@@ -17,7 +17,7 @@ In this example, we'll create a workflow that generates and translates copy. Thi
 **This task:**
 
 - Uses `generateText` from [Vercel's AI SDK](https://sdk.vercel.ai/docs/introduction) to interact with OpenAI models
-- Uses `experimental_telemetry` in the source verification and historical analysis tasks to provide LLM logs
+- Uses `experimental_telemetry` to provide LLM logs
 - Generates marketing copy based on subject and target word count
 - Validates the generated copy meets word count requirements (±10 words)
 - Translates the validated copy to the target language while preserving tone

docs/guides/ai-agents/respond-and-check-content.mdx

@@ -1,12 +1,12 @@
 ---
 title: "Respond to customer inquiry and check for inappropriate content"
 sidebarTitle: "Respond & check content"
-description: "Create a AI agent workflow that responds to customer inquiries while checking if their text is inappropriate"
+description: "Create an AI agent workflow that responds to customer inquiries while checking if their text is inappropriate"
 ---
 
 ## Overview
 
-Parallelization is a workflow pattern where multiple tasks or processes run simultaneously instead of sequentially, allowing for more efficient use of resources and faster overall execution. It's particularly valuable when different parts of a task can be handled independently, such as running content analysis and response generation at the same time.
+**Parallelization** is a workflow pattern where multiple tasks or processes run simultaneously instead of sequentially, allowing for more efficient use of resources and faster overall execution. It's particularly valuable when different parts of a task can be handled independently, such as running content analysis and response generation at the same time.
 ![Parallelization](/guides/ai-agents/parallelization.png)
 
 ## Example task
@@ -16,8 +16,8 @@ In this example, we'll create a workflow that simultaneously checks content for
 **This task:**
 
 - Uses `generateText` from [Vercel's AI SDK](https://sdk.vercel.ai/docs/introduction) to interact with OpenAI models
-- Uses `experimental_telemetry` in the source verification and historical analysis tasks to provide LLM logs
-- Uses `batch.triggerByTaskAndWait` to run customer response and content moderation tasks in parallel
+- Uses `experimental_telemetry` to provide LLM logs
+- Uses [`batch.triggerByTaskAndWait`](/triggering#batch-triggerbytaskandwait) to run customer response and content moderation tasks in parallel
 - Generates customer service responses using an AI model
 - Simultaneously checks for inappropriate content while generating responses
 
@@ -115,7 +115,7 @@ export const handleCustomerQuestion = task({
 
 ## Run a test
 
-On the Test page in the dashboard, select the `respond-and-check-content` task and include a payload like the following:
+On the Test page in the dashboard, select the `handle-customer-question` task and include a payload like the following:
 
 ``` json
 {

docs/guides/ai-agents/route-question.mdx

@@ -6,7 +6,7 @@ description: "Create an AI agent workflow that routes a question to a different
 
 ## Overview
 
-Routing is a workflow pattern that classifies an input and directs it to a specialized followup task. This pattern allows for separation of concerns and building more specialized prompts, which is particularly effective when there are distinct categories that are better handled separately. Without routing, optimizing for one kind of input can hurt performance on other inputs.
+**Routing** is a workflow pattern that classifies an input and directs it to a specialized followup task. This pattern allows for separation of concerns and building more specialized prompts, which is particularly effective when there are distinct categories that are better handled separately. Without routing, optimizing for one kind of input can hurt performance on other inputs.
 
 ![Routing](/guides/ai-agents/routing.png)
 
@@ -18,6 +18,9 @@ In this example, we'll create a workflow that routes a question to a different A
 
 - Uses `generateText` from [Vercel's AI SDK](https://sdk.vercel.ai/docs/introduction) to interact with OpenAI models
 - Uses `experimental_telemetry` in the source verification and historical analysis tasks to provide LLM logs
+- Routes questions using a lightweight model (`o1-mini`) to classify complexity
+- Directs simple questions to `gpt-4o` and complex ones to `gpt-o3-mini`
+- Returns both the answer and metadata about the routing decision
 
 ```typescript
 import { openai } from "@ai-sdk/openai";
@@ -102,8 +105,6 @@ Triggering our task with a simple question shows it routing to the gpt-4o model
 }
 ```
 
-This example payload routes the question to a different AI model depending on its complexity.
-
 <video 
   src="https://content.trigger.dev/agent-routing.mp4"
   controls

docs/guides/ai-agents/translate-and-refine.mdx

@@ -6,13 +6,13 @@ description: "This guide will show you how to create a task that translates text
 
 ## Overview
 
-This example is based on an "evaluator-optimizer" pattern, where one LLM generates a response while another provides evaluation and feedback in a loop. This is particularly effective for tasks with clear evaluation criteria where iterative refinement provides measurable value.
+This example is based on the **evaluator-optimizer** pattern, where one LLM generates a response while another provides evaluation and feedback in a loop. This is particularly effective for tasks with clear evaluation criteria where iterative refinement provides better results.
 
 ![Evaluator-optimizer](/guides/ai-agents/evaluator-optimizer.png)
 
 ## Example task
 
-Our example task is designed to translate text into a target language and refine the translation over a number of iterations based on feedback provided by the LLM.
+This example task translates text into a target language and refines the translation over a number of iterations based on feedback provided by the LLM.
 
 **This task:**
 
@@ -159,7 +159,7 @@ On the Test page in the dashboard, select the `translate-and-refine` task and in
 }
 ```
 
-This example payload translates the text into French and should be suitably difficult to require a few iterations, depending on the model used.
+This example payload translates the text into French and should be suitably difficult to require a few iterations, depending on the model used and the prompt criteria you set.
 
 <video 
   src="https://content.trigger.dev/agent-evaluator-optimizer.mp4"

docs/guides/ai-agents/verify-news-article.mdx

@@ -1,27 +1,27 @@
 ---
 title: "Verify a news article"
 sidebarTitle: "Verify news article"
-description: "Create a AI agent workflow that verifies the facts in a news article"
+description: "Create an AI agent workflow that verifies the facts in a news article"
 ---
 
 ## Overview
 
-This example demonstrates the "orchestrator-workers" pattern, where a central AI agent dynamically breaks down complex tasks and delegates them to specialized worker agents. This pattern is particularly effective when tasks require multiple perspectives or parallel processing streams, with the orchestrator synthesizing the results into a cohesive output.
+This example demonstrates the **orchestrator-workers** pattern, where a central AI agent dynamically breaks down complex tasks and delegates them to specialized worker agents. This pattern is particularly effective when tasks require multiple perspectives or parallel processing streams, with the orchestrator synthesizing the results into a cohesive output.
 
 ![Orchestrator](/guides/ai-agents/orchestrator-workers.png)
 
 ## Example task
 
-Our example task uses multiple AI agents to extract claims from a news article and verify them in parallel, combining source verification and historical analysis to produce a comprehensive fact-checking report.
+Our example task uses multiple LLM calls to extract claims from a news article and analyze them in parallel, combining source verification and historical context to assess their credibility.
 
 **This task:**
 
 - Uses `generateText` from [Vercel's AI SDK](https://sdk.vercel.ai/docs/introduction) to interact with OpenAI models
-- Uses `experimental_telemetry` in the source verification and historical analysis tasks to provide LLM logs
-- Uses `batch.triggerByTaskAndWait` to orchestrate parallel processing of claims
-- Extracts factual claims from news articles using an AI model
-- Verifies claims against recent sources and analyzes historical context in parallel
-- Combines results into a comprehensive fact-checking report
+- Uses `experimental_telemetry` to provide LLM logs
+- Uses [`batch.triggerByTaskAndWait`](/triggering#batch-triggerbytaskandwait) to orchestrate parallel processing of claims
+- Extracts factual claims from news articles using the `o1-mini` model
+- Evaluates claims against recent sources and analyzes historical context in parallel
+- Combines results into a structured analysis report
 
 
 ```typescript