diff --git a/credit-scorer/docs/guides/cloud_deployment.md b/credit-scorer/docs/guides/cloud_deployment.md
index 8874013b..d870457f 100644
--- a/credit-scorer/docs/guides/cloud_deployment.md
+++ b/credit-scorer/docs/guides/cloud_deployment.md
@@ -1,6 +1,6 @@
 ## ☁️ Cloud Deployment
 
-CreditScorer supports storing artifacts remotely and executing pipelines on cloud infrastructure. For this example, we'll use AWS, but you can use any cloud provider you want. You can also refer to the [AWS Integration Guide](https://docs.zenml.io/how-to/popular-integrations/aws-guide) for detailed instructions.
+CreditScorer supports storing artifacts remotely and executing pipelines on cloud infrastructure. For this example, we'll use AWS, but you can use any cloud provider you want. You can also refer to the [AWS Integration Guide](https://docs.zenml.io/stacks/popular-stacks/aws-guide) for detailed instructions.
 
 ### AWS Setup
 
@@ -75,6 +75,6 @@ Similar setup processes can be followed for other cloud providers:
 
 For detailed configuration options for these providers, refer to the ZenML documentation:
 
-- [GCP Integration Guide](https://docs.zenml.io/how-to/popular-integrations/gcp-guide)
-- [Azure Integration Guide](https://docs.zenml.io/how-to/popular-integrations/azure-guide)
-- [Kubernetes Integration Guide](https://docs.zenml.io/how-to/popular-integrations/kubernetes)
+- [GCP Integration Guide](https://docs.zenml.io/stacks/popular-stacks/gcp-guide)
+- [Azure Integration Guide](https://docs.zenml.io/stacks/popular-stacks/azure-guide)
+- [Kubernetes Integration Guide](https://docs.zenml.io/stacks/popular-stacks/kubernetes)
diff --git a/databricks-production-qa-demo/steps/deployment/deployment_deploy.py b/databricks-production-qa-demo/steps/deployment/deployment_deploy.py
index b7407dcf..265bcd40 100644
--- a/databricks-production-qa-demo/steps/deployment/deployment_deploy.py
+++ b/databricks-production-qa-demo/steps/deployment/deployment_deploy.py
@@ -45,7 +45,7 @@ def deployment_deploy() -> Annotated[
     In this example, the step can be configured to use different input data.
     See the documentation for more information:
 
-        https://docs.zenml.io/how-to/pipeline-development/use-configuration-files
+        https://docs.zenml.io/concepts/steps_and_pipelines/yaml_configuration
 
     Args:
         dataset_inf: The inference dataset.
diff --git a/databricks-production-qa-demo/steps/etl/data_loader.py b/databricks-production-qa-demo/steps/etl/data_loader.py
index 7ed2f850..42704739 100644
--- a/databricks-production-qa-demo/steps/etl/data_loader.py
+++ b/databricks-production-qa-demo/steps/etl/data_loader.py
@@ -44,7 +44,7 @@ def data_loader(
     In this example, the step can be configured with number of rows and logic
     to drop target column or not. See the documentation for more information:
 
-        https://docs.zenml.io/how-to/build-pipelines/use-pipeline-step-parameters
+        https://docs.zenml.io/concepts/steps_and_pipelines#pipeline-parameterization
 
     Args:
         is_inference: If `True` subset will be returned and target column
diff --git a/databricks-production-qa-demo/steps/etl/train_data_preprocessor.py b/databricks-production-qa-demo/steps/etl/train_data_preprocessor.py
index 011bf422..5b31bcb4 100644
--- a/databricks-production-qa-demo/steps/etl/train_data_preprocessor.py
+++ b/databricks-production-qa-demo/steps/etl/train_data_preprocessor.py
@@ -52,7 +52,7 @@ def train_data_preprocessor(
     columns and normalize numerical columns. See the documentation for more
     information:
 
-        https://docs.zenml.io/how-to/build-pipelines/use-pipeline-step-parameters
+        https://docs.zenml.io/concepts/steps_and_pipelines#pipeline-parameterization
 
     Args:
         dataset_trn: The train dataset.
diff --git a/databricks-production-qa-demo/steps/etl/train_data_splitter.py b/databricks-production-qa-demo/steps/etl/train_data_splitter.py
index ae541459..002fe294 100644
--- a/databricks-production-qa-demo/steps/etl/train_data_splitter.py
+++ b/databricks-production-qa-demo/steps/etl/train_data_splitter.py
@@ -41,7 +41,7 @@ def train_data_splitter(
     In this example, the step can be configured to use different test
     set sizes. See the documentation for more information:
 
-        https://docs.zenml.io/how-to/pipeline-development/use-configuration-files
+        https://docs.zenml.io/concepts/steps_and_pipelines/yaml_configuration
 
     Args:
         dataset: Dataset read from source.
diff --git a/databricks-production-qa-demo/steps/hp_tuning/hp_tuning_single_search.py b/databricks-production-qa-demo/steps/hp_tuning/hp_tuning_single_search.py
index b4c3eb0d..7f5ab7e1 100644
--- a/databricks-production-qa-demo/steps/hp_tuning/hp_tuning_single_search.py
+++ b/databricks-production-qa-demo/steps/hp_tuning/hp_tuning_single_search.py
@@ -50,7 +50,7 @@ def hp_tuning_single_search(
     to use different input datasets and also have a flag to fall back to default
     model architecture. See the documentation for more information:
 
-        https://docs.zenml.io/how-to/pipeline-development/use-configuration-files
+        https://docs.zenml.io/concepts/steps_and_pipelines/yaml_configuration
 
     Args:
         model_package: The package containing the model to use for hyperparameter tuning.
diff --git a/databricks-production-qa-demo/steps/inference/inference_predict.py b/databricks-production-qa-demo/steps/inference/inference_predict.py
index d0854065..5d8359b5 100644
--- a/databricks-production-qa-demo/steps/inference/inference_predict.py
+++ b/databricks-production-qa-demo/steps/inference/inference_predict.py
@@ -43,7 +43,7 @@ def inference_predict(
     In this example, the step can be configured to use different input data.
     See the documentation for more information:
 
-        https://docs.zenml.io/how-to/pipeline-development/use-configuration-files
+        https://docs.zenml.io/concepts/steps_and_pipelines/yaml_configuration
 
     Args:
         dataset_inf: The inference dataset.
diff --git a/databricks-production-qa-demo/steps/promotion/compute_performance_metrics_on_current_data.py b/databricks-production-qa-demo/steps/promotion/compute_performance_metrics_on_current_data.py
index a9722c0a..71fff7d6 100644
--- a/databricks-production-qa-demo/steps/promotion/compute_performance_metrics_on_current_data.py
+++ b/databricks-production-qa-demo/steps/promotion/compute_performance_metrics_on_current_data.py
@@ -44,7 +44,7 @@ def compute_performance_metrics_on_current_data(
     and target environment stage for promotion.
     See the documentation for more information:
 
-        https://docs.zenml.io/how-to/pipeline-development/use-configuration-files
+        https://docs.zenml.io/concepts/steps_and_pipelines/yaml_configuration
 
     Args:
         dataset_tst: The test dataset.
diff --git a/databricks-production-qa-demo/steps/promotion/promote_with_metric_compare.py b/databricks-production-qa-demo/steps/promotion/promote_with_metric_compare.py
index d23a0c37..5558b7f3 100644
--- a/databricks-production-qa-demo/steps/promotion/promote_with_metric_compare.py
+++ b/databricks-production-qa-demo/steps/promotion/promote_with_metric_compare.py
@@ -46,7 +46,7 @@ def promote_with_metric_compare(
     and target environment stage for promotion.
     See the documentation for more information:
 
-        https://docs.zenml.io/how-to/pipeline-development/use-configuration-files
+        https://docs.zenml.io/concepts/steps_and_pipelines/yaml_configuration
 
     Args:
         latest_metric: Recently trained model metric results.
diff --git a/databricks-production-qa-demo/steps/training/model_trainer.py b/databricks-production-qa-demo/steps/training/model_trainer.py
index 03edae2e..39b8d2fe 100644
--- a/databricks-production-qa-demo/steps/training/model_trainer.py
+++ b/databricks-production-qa-demo/steps/training/model_trainer.py
@@ -72,7 +72,7 @@ def model_trainer(
     hyperparameters to the model constructor. See the documentation for more
     information:
 
-        https://docs.zenml.io/how-to/pipeline-development/use-configuration-files
+        https://docs.zenml.io/concepts/steps_and_pipelines/yaml_configuration
 
     Args:
         dataset_trn: The preprocessed train dataset.
diff --git a/deep_research/README.md b/deep_research/README.md
index aa03937c..f17830b5 100644
--- a/deep_research/README.md
+++ b/deep_research/README.md
@@ -632,6 +632,22 @@ This pipeline can integrate with:
 - **Alerting Systems**: Schedule research on key topics and receive regular reports
 - **Other ZenML Pipelines**: Chain with downstream analysis or processing
 
+## ☁️ Cloud Orchestrator Configuration
+
+When running the pipeline with a cloud orchestrator (like Kubernetes, AWS SageMaker, etc.), the configuration files automatically use environment variable substitution to pick up your API keys from the environment.
+
+The configuration files in `configs/` use environment variable substitution like:
+```yaml
+settings:
+  docker:
+    environment:
+      OPENROUTER_API_KEY: ${OPENROUTER_API_KEY}
+      TAVILY_API_KEY: ${TAVILY_API_KEY}
+      # ... other keys
+```
+
+Simply ensure your environment variables are set in your orchestrator environment, and the pipeline will automatically pick them up. For security, consider using your cloud provider's secret management services (AWS Secrets Manager, Azure Key Vault, etc.) to inject these environment variables into your orchestrator runtime.
+
 ## 📄 License
 
 This project is licensed under the Apache License 2.0.
diff --git a/deep_research/configs/balanced_research.yaml b/deep_research/configs/balanced_research.yaml
index 4f8bfa23..b292304c 100644
--- a/deep_research/configs/balanced_research.yaml
+++ b/deep_research/configs/balanced_research.yaml
@@ -23,57 +23,68 @@ langfuse_project_name: "deep-research"
 # Research parameters for balanced research
 parameters:
   query: "Default research query"
-  
+
 steps:
   initial_query_decomposition_step:
     parameters:
       llm_model: "sambanova/DeepSeek-R1-Distill-Llama-70B"
-      max_sub_questions: 10  # Balanced number of sub-questions
-  
+      max_sub_questions: 10 # Balanced number of sub-questions
+
   process_sub_question_step:
     parameters:
       llm_model_search: "sambanova/Meta-Llama-3.3-70B-Instruct"
       llm_model_synthesis: "sambanova/DeepSeek-R1-Distill-Llama-70B"
-      cap_search_length: 20000  # Standard cap for search length
-  
+      cap_search_length: 20000 # Standard cap for search length
+
   cross_viewpoint_analysis_step:
     parameters:
       llm_model: "sambanova/DeepSeek-R1-Distill-Llama-70B"
-      viewpoint_categories:
-        [
+      viewpoint_categories: [
           "scientific",
           "political",
           "economic",
           "social",
           "ethical",
           "historical",
-        ]  # Standard viewpoints
-  
+        ] # Standard viewpoints
+
   generate_reflection_step:
     parameters:
       llm_model: "sambanova/DeepSeek-R1-Distill-Llama-70B"
-  
+
   get_research_approval_step:
     parameters:
-      timeout: 3600  # 1 hour timeout
-      max_queries: 2  # Moderate additional queries
-  
+      timeout: 3600 # 1 hour timeout
+      max_queries: 2 # Moderate additional queries
+
   execute_approved_searches_step:
     parameters:
       llm_model: "sambanova/Meta-Llama-3.3-70B-Instruct"
       cap_search_length: 20000
-      
+
   pydantic_final_report_step:
     parameters:
       llm_model: "sambanova/DeepSeek-R1-Distill-Llama-70B"
-      
+
 # Environment settings
 settings:
   docker:
     requirements:
-      - openai>=1.0.0
       - tavily-python>=0.2.8
+      - exa-py>=1.0.0
       - PyYAML>=6.0
       - click>=8.0.0
       - pydantic>=2.0.0
-      - typing_extensions>=4.0.0
\ No newline at end of file
+      - typing_extensions>=4.0.0
+      - requests
+      - anthropic>=0.52.2
+      - litellm==1.69.1
+      - langfuse==2.60.8
+    environment:
+      OPENROUTER_API_KEY: ${OPENROUTER_API_KEY}
+      TAVILY_API_KEY: ${TAVILY_API_KEY}
+      EXA_API_KEY: ${EXA_API_KEY}
+      ANTHROPIC_API_KEY: ${ANTHROPIC_API_KEY}
+      LANGFUSE_PUBLIC_KEY: ${LANGFUSE_PUBLIC_KEY}
+      LANGFUSE_SECRET_KEY: ${LANGFUSE_SECRET_KEY}
+      LANGFUSE_HOST: ${LANGFUSE_HOST}
diff --git a/deep_research/configs/deep_research.yaml b/deep_research/configs/deep_research.yaml
index 61cc4c2b..0ff22b83 100644
--- a/deep_research/configs/deep_research.yaml
+++ b/deep_research/configs/deep_research.yaml
@@ -73,9 +73,21 @@ steps:
 settings:
   docker:
     requirements:
-      - openai>=1.0.0
       - tavily-python>=0.2.8
+      - exa-py>=1.0.0
       - PyYAML>=6.0
       - click>=8.0.0
       - pydantic>=2.0.0
-      - typing_extensions>=4.0.0
\ No newline at end of file
+      - typing_extensions>=4.0.0
+      - requests
+      - anthropic>=0.52.2
+      - litellm==1.69.1
+      - langfuse==2.60.8
+    environment:
+      OPENROUTER_API_KEY: ${OPENROUTER_API_KEY}
+      TAVILY_API_KEY: ${TAVILY_API_KEY}
+      EXA_API_KEY: ${EXA_API_KEY}
+      ANTHROPIC_API_KEY: ${ANTHROPIC_API_KEY}
+      LANGFUSE_PUBLIC_KEY: ${LANGFUSE_PUBLIC_KEY}
+      LANGFUSE_SECRET_KEY: ${LANGFUSE_SECRET_KEY}
+      LANGFUSE_HOST: ${LANGFUSE_HOST}
\ No newline at end of file
diff --git a/deep_research/configs/enhanced_research.yaml b/deep_research/configs/enhanced_research.yaml
index 1933efa9..956d30a2 100644
--- a/deep_research/configs/enhanced_research.yaml
+++ b/deep_research/configs/enhanced_research.yaml
@@ -63,9 +63,21 @@ steps:
 settings:
   docker:
     requirements:
-      - openai>=1.0.0
       - tavily-python>=0.2.8
+      - exa-py>=1.0.0
       - PyYAML>=6.0
       - click>=8.0.0
       - pydantic>=2.0.0
       - typing_extensions>=4.0.0
+      - requests
+      - anthropic>=0.52.2
+      - litellm==1.69.1
+      - langfuse==2.60.8
+    environment:
+      OPENROUTER_API_KEY: ${OPENROUTER_API_KEY}
+      TAVILY_API_KEY: ${TAVILY_API_KEY}
+      EXA_API_KEY: ${EXA_API_KEY}
+      ANTHROPIC_API_KEY: ${ANTHROPIC_API_KEY}
+      LANGFUSE_PUBLIC_KEY: ${LANGFUSE_PUBLIC_KEY}
+      LANGFUSE_SECRET_KEY: ${LANGFUSE_SECRET_KEY}
+      LANGFUSE_HOST: ${LANGFUSE_HOST}
diff --git a/deep_research/configs/enhanced_research_with_approval.yaml b/deep_research/configs/enhanced_research_with_approval.yaml
index 73d6fe42..419d38be 100644
--- a/deep_research/configs/enhanced_research_with_approval.yaml
+++ b/deep_research/configs/enhanced_research_with_approval.yaml
@@ -69,9 +69,21 @@ steps:
 settings:
   docker:
     requirements:
-      - openai>=1.0.0
       - tavily-python>=0.2.8
+      - exa-py>=1.0.0
       - PyYAML>=6.0
       - click>=8.0.0
       - pydantic>=2.0.0
-      - typing_extensions>=4.0.0
\ No newline at end of file
+      - typing_extensions>=4.0.0
+      - requests
+      - anthropic>=0.52.2
+      - litellm==1.69.1
+      - langfuse==2.60.8
+    environment:
+      OPENROUTER_API_KEY: ${OPENROUTER_API_KEY}
+      TAVILY_API_KEY: ${TAVILY_API_KEY}
+      EXA_API_KEY: ${EXA_API_KEY}
+      ANTHROPIC_API_KEY: ${ANTHROPIC_API_KEY}
+      LANGFUSE_PUBLIC_KEY: ${LANGFUSE_PUBLIC_KEY}
+      LANGFUSE_SECRET_KEY: ${LANGFUSE_SECRET_KEY}
+      LANGFUSE_HOST: ${LANGFUSE_HOST}
\ No newline at end of file
diff --git a/deep_research/configs/quick_research.yaml b/deep_research/configs/quick_research.yaml
index b210f18f..aac4ece7 100644
--- a/deep_research/configs/quick_research.yaml
+++ b/deep_research/configs/quick_research.yaml
@@ -51,9 +51,21 @@ steps:
 settings:
   docker:
     requirements:
-      - openai>=1.0.0
       - tavily-python>=0.2.8
+      - exa-py>=1.0.0
       - PyYAML>=6.0
       - click>=8.0.0
       - pydantic>=2.0.0
       - typing_extensions>=4.0.0
+      - requests
+      - anthropic>=0.52.2
+      - litellm==1.69.1
+      - langfuse==2.60.8
+    environment:
+      OPENROUTER_API_KEY: ${OPENROUTER_API_KEY}
+      TAVILY_API_KEY: ${TAVILY_API_KEY}
+      EXA_API_KEY: ${EXA_API_KEY}
+      ANTHROPIC_API_KEY: ${ANTHROPIC_API_KEY}
+      LANGFUSE_PUBLIC_KEY: ${LANGFUSE_PUBLIC_KEY}
+      LANGFUSE_SECRET_KEY: ${LANGFUSE_SECRET_KEY}
+      LANGFUSE_HOST: ${LANGFUSE_HOST}
diff --git a/deep_research/configs/rapid_research.yaml b/deep_research/configs/rapid_research.yaml
index e69982bf..1962d0ba 100644
--- a/deep_research/configs/rapid_research.yaml
+++ b/deep_research/configs/rapid_research.yaml
@@ -51,9 +51,21 @@ steps:
 settings:
   docker:
     requirements:
-      - openai>=1.0.0
       - tavily-python>=0.2.8
+      - exa-py>=1.0.0
       - PyYAML>=6.0
       - click>=8.0.0
       - pydantic>=2.0.0
       - typing_extensions>=4.0.0
+      - requests
+      - anthropic>=0.52.2
+      - litellm==1.69.1
+      - langfuse==2.60.8
+    environment:
+      OPENROUTER_API_KEY: ${OPENROUTER_API_KEY}
+      TAVILY_API_KEY: ${TAVILY_API_KEY}
+      EXA_API_KEY: ${EXA_API_KEY}
+      ANTHROPIC_API_KEY: ${ANTHROPIC_API_KEY}
+      LANGFUSE_PUBLIC_KEY: ${LANGFUSE_PUBLIC_KEY}
+      LANGFUSE_SECRET_KEY: ${LANGFUSE_SECRET_KEY}
+      LANGFUSE_HOST: ${LANGFUSE_HOST}
diff --git a/end-to-end-computer-vision/README.md b/end-to-end-computer-vision/README.md
index f3fa762b..7e5a6532 100644
--- a/end-to-end-computer-vision/README.md
+++ b/end-to-end-computer-vision/README.md
@@ -69,7 +69,7 @@ zenml login <INSERT_ZENML_URL_HERE>
 We will use GCP in the commands listed below, but it will work for other cloud
 providers.
 
-1) Follow our guide to set up your credentials for GCP [here](https://docs.zenml.io/how-to/auth-management/gcp-service-connector)
+1) Follow our guide to set up your credentials for GCP [here](https://docs.zenml.io/stacks/service-connectors/connector-types/gcp-service-connector)
 2) Set up a bucket in GCP to persist your training data
 3) Set up a bucket to use as artifact store within ZenML
 Learn how to set up a GCP artifact store stack component within ZenML
diff --git a/eurorate-predictor/README.md b/eurorate-predictor/README.md
index bc61a60a..8893298b 100644
--- a/eurorate-predictor/README.md
+++ b/eurorate-predictor/README.md
@@ -92,8 +92,7 @@ output "zenml_stack_name" {
 }
 ```
 To learn more about the terraform script, read the 
-[ZenML documentation.](https://docs.zenml.io/how-to/
-stack-deployment/deploy-a-cloud-stack-with-terraform) or 
+[ZenML documentation.](https://docs.zenml.io/stacks/deployment/deploy-a-cloud-stack-with-terraform) or 
 see
 the [Terraform registry](https://registry.terraform.io/
 modules/zenml-io/zenml-stack).
@@ -163,4 +162,6 @@ For detailed documentation on using ZenML to build your own MLOps pipelines, ple
 
 ## 🔄 Continuous Improvement
 
-EuroRate Predictor is designed for continuous improvement of your interest rate forecasts. As new ECB data becomes available, simply re-run the pipelines to generate updated predictions.
\ No newline at end of file
+EuroRate Predictor is designed for continuous improvement of your interest rate
+forecasts. As new ECB data becomes available, simply re-run the pipelines to
+generate updated predictions.
diff --git a/huggingface-sagemaker/steps/promotion/promote_metric_compare_promoter.py b/huggingface-sagemaker/steps/promotion/promote_metric_compare_promoter.py
index aab26ed0..ebba4575 100644
--- a/huggingface-sagemaker/steps/promotion/promote_metric_compare_promoter.py
+++ b/huggingface-sagemaker/steps/promotion/promote_metric_compare_promoter.py
@@ -48,7 +48,7 @@ def promote_metric_compare_promoter(
     In this example, the step can be configured to use different input data.
     See the documentation for more information:
 
-        https://docs.zenml.io/how-to/pipeline-development/use-configuration-files
+        https://docs.zenml.io/concepts/steps_and_pipelines/yaml_configuration
 
     Args:
         latest_metrics: Recently trained model metrics results.
diff --git a/huggingface-sagemaker/steps/tokenizer_loader/tokenizer_loader.py b/huggingface-sagemaker/steps/tokenizer_loader/tokenizer_loader.py
index 513bc8d6..e331d8c8 100644
--- a/huggingface-sagemaker/steps/tokenizer_loader/tokenizer_loader.py
+++ b/huggingface-sagemaker/steps/tokenizer_loader/tokenizer_loader.py
@@ -46,7 +46,7 @@ def tokenizer_loader(
     For more information on how to configure steps in a pipeline, refer to the
     following documentation:
 
-        https://docs.zenml.io/how-to/pipeline-development/use-configuration-files
+        https://docs.zenml.io/concepts/steps_and_pipelines/yaml_configuration
 
     Args:
         lower_case: A boolean value indicating whether to convert the input text to
diff --git a/llm-complete-guide/README.md b/llm-complete-guide/README.md
index 403f4d72..f352d2bf 100644
--- a/llm-complete-guide/README.md
+++ b/llm-complete-guide/README.md
@@ -166,7 +166,7 @@ zenml service-account create <SERVICE_ACCOUNT_NAME>
 ```
 
 For more information on this part of the process, please refer to the [ZenML
-documentation](https://docs.zenml.io/how-to/project-setup-and-management/connecting-to-zenml/connect-with-a-service-account).
+documentation](https://docs.zenml.io/concepts/service_connectors).
 
 Once you have your service account API token and store URL (the URL of your
 deployed ZenML tenant), you can update the secrets with the following command:
@@ -279,10 +279,11 @@ loss function](https://www.philschmid.de/fine-tune-embedding-model-for-rag) whic
 
 The basic RAG pipeline will run using a local stack, but if you want to improve
 the speed of the embeddings step you might want to consider using a cloud
-orchestrator. Please follow the instructions in [documentation on popular integrations](https://docs.zenml.io/how-to/popular-integrations) (currently available for
-[AWS](https://docs.zenml.io/how-to/popular-integrations/aws-guide) and
-[GCP](https://docs.zenml.io/how-to/popular-integrations/gcp-guide)) to learn how you
-can run the pipelines on a remote stack.
+orchestrator. Please follow the instructions in documentation on popular integrations (currently available for
+[AWS](https://docs.zenml.io/stacks/popular-stacks/aws-guide),
+[GCP](https://docs.zenml.io/stacks/popular-stacks/gcp-guide), and
+[Azure](https://docs.zenml.io/stacks/popular-stacks/azure-guide)) to learn how
+you can run the pipelines on a remote stack.
 
 If you run the pipeline using a cloud artifact store, logs from all the steps as
 well as assets like the visualizations will all be shown in the ZenML dashboard.
@@ -299,7 +300,7 @@ You can also self-host the ZenML dashboard. Instructions are available in our
 
 ## 📜 Project Structure
 
-The project loosely follows [the recommended ZenML project structure](https://docs.zenml.io/how-to/setting-up-a-project-repository/best-practices):
+The project loosely follows [the recommended ZenML project structure](https://docs.zenml.io/user-guides/best-practices/set-up-your-repository):
 
 ```
 .
diff --git a/llm-complete-guide/steps/url_scraper.py b/llm-complete-guide/steps/url_scraper.py
index 923f7351..98d5e5c3 100644
--- a/llm-complete-guide/steps/url_scraper.py
+++ b/llm-complete-guide/steps/url_scraper.py
@@ -45,18 +45,18 @@ def url_scraper(
             "https://docs.zenml.io/getting-started/system-architectures",
             "https://docs.zenml.io/getting-started/core-concepts",
             "https://docs.zenml.io/user-guides/llmops-guide/rag-with-zenml/rag-85-loc",
-            "https://docs.zenml.io/how-to/track-metrics-metadata/logging-metadata",
-            "https://docs.zenml.io/how-to/debug-and-solve-issues",
+            "https://docs.zenml.io/concepts/metadata",
+            "https://docs.zenml.io/user-guides/best-practices/debug-and-solve-issues",
             "https://docs.zenml.io/stack-components/step-operators/azureml",
-            # "https://docs.zenml.io/how-to/interact-with-secrets",
-            # "https://docs.zenml.io/how-to/infrastructure-deployment/auth-management/service-connectors-guide",
-            # "https://docs.zenml.io/how-to/infrastructure-deployment/auth-management/hyperai-service-connector",
+            # "https://docs.zenml.io/concepts/secrets",
+            # "https://docs.zenml.io/stacks/service-connectors/service-connectors-guide",
+            # "https://docs.zenml.io/stacks/service-connectors/hyperai-service-connector",
             # "https://docs.zenml.io/stack-components/data-validators/evidently",
             # "https://docs.zenml.io/stack-components/data-validators",
             # "https://docs.zenml.io/stack-components/step-operators/sagemaker",
             # "https://docs.zenml.io/stack-components/alerters/slack",
-            # "https://docs.zenml.io/how-to/infrastructure-deployment/auth-management/kubernetes-service-connector",
-            # "https://docs.zenml.io/how-to/infrastructure-deployment/auth-management/azure-service-connector"
+            # "https://docs.zenml.io/stacks/service-connectors/kubernetes-service-connector",
+            # "https://docs.zenml.io/stacks/service-connectors/azure-service-connector"
         ]
     else:
         docs_urls = get_all_pages(docs_url)
diff --git a/magic-photobooth/README.md b/magic-photobooth/README.md
index 4fdd7d61..37eca445 100644
--- a/magic-photobooth/README.md
+++ b/magic-photobooth/README.md
@@ -78,7 +78,7 @@ Behind the scenes, Magic Photobooth employs Low-Rank Adaptation (LoRA) technolog
 
 Magic Photobooth requires GPU resources for optimal performance. We recommend deploying on a cloud infrastructure:
 
-1. **Set up your cloud environment** using our [1-click deployment guide](https://docs.zenml.io/how-to/stack-deployment/deploy-a-cloud-stack) for AWS, GCP, or Azure.
+1. **Set up your cloud environment** using our [1-click deployment guide](https://docs.zenml.io/stacks/deployment/deploy-a-cloud-stack) for AWS, GCP, or Azure.
 
 2. **Configure your GPU quotas** to ensure sufficient resources for model training and inference.
 
diff --git a/omni-reader/README.md b/omni-reader/README.md
index 8043af45..82ac3b2b 100644
--- a/omni-reader/README.md
+++ b/omni-reader/README.md
@@ -189,7 +189,7 @@ streamlit run app.py
 
 ## ☁️ Cloud Deployment
 
-OmniReader supports storing artifacts remotely and executing pipelines on cloud infrastructure. For this example, we'll use AWS, but you can use any cloud provider you want. You can also refer to the [AWS Integration Guide](https://docs.zenml.io/how-to/popular-integrations/aws-guide) for detailed instructions.
+OmniReader supports storing artifacts remotely and executing pipelines on cloud infrastructure. For this example, we'll use AWS, but you can use any cloud provider you want. You can also refer to the [AWS Integration Guide](https://docs.zenml.io/stacks/popular-stacks/aws-guide) for detailed instructions.
 
 ### AWS Setup
 
@@ -264,9 +264,9 @@ Similar setup processes can be followed for other cloud providers:
 
 For detailed configuration options for these providers, refer to the ZenML documentation:
 
-- [GCP Integration Guide](https://docs.zenml.io/how-to/popular-integrations/gcp-guide)
-- [Azure Integration Guide](https://docs.zenml.io/how-to/popular-integrations/azure-guide)
-- [Kubernetes Integration Guide](https://docs.zenml.io/how-to/popular-integrations/kubernetes)
+- [GCP Integration Guide](https://docs.zenml.io/stacks/popular-stacks/gcp-guide)
+- [Azure Integration Guide](https://docs.zenml.io/stacks/popular-stacks/azure-guide)
+- [Kubernetes Integration Guide](https://docs.zenml.io/stacks/popular-stacks/kubernetes)
 
 ### 🐳 Docker Settings for Cloud Deployment
 
diff --git a/oncoclear/README.md b/oncoclear/README.md
index b0090366..3b48ec62 100644
--- a/oncoclear/README.md
+++ b/oncoclear/README.md
@@ -388,11 +388,11 @@ OncoClear follows a modular architecture:
 
 ## 📚 Learn More
 
-For detailed documentation on building MLOps pipelines with ZenML, visit the [ZenML Documentation](https://docs.zenml.io/). In particular, the [Production Guide](https://docs.zenml.io/user-guide/production-guide/) goes into more detail about transitioning pipelines to production in the cloud.
+For detailed documentation on building MLOps pipelines with ZenML, visit the [ZenML Documentation](https://docs.zenml.io/). In particular, the [Production Guide](https://docs.zenml.io/user-guides/production-guide/) goes into more detail about transitioning pipelines to production in the cloud.
 
 The best way to get a production ZenML instance up and running with all batteries included is with [ZenML Pro](https://zenml.io/pro). Check it out!
 
 Also, make sure to join our <a href="https://zenml.io/slack" target="_blank">
     <img width="15" src="https://cdn3.iconfinder.com/data/icons/logos-and-brands-adobe/512/306_Slack-512.png" alt="Slack"/>
     <b>Slack Community</b> 
-</a> to become part of the ZenML family! 
\ No newline at end of file
+</a> to become part of the ZenML family! 
diff --git a/oncoclear/steps/data_loader.py b/oncoclear/steps/data_loader.py
index dbf6d7da..7519f988 100644
--- a/oncoclear/steps/data_loader.py
+++ b/oncoclear/steps/data_loader.py
@@ -37,7 +37,7 @@ def data_loader(
     In this example, the step can be configured with number of rows and logic
     to drop target column or not. See the documentation for more information:
 
-        https://docs.zenml.io/how-to/build-pipelines/use-pipeline-step-parameters
+        https://docs.zenml.io/concepts/steps_and_pipelines#pipeline-parameterization
 
     Args:
         random_state: Random state for sampling
diff --git a/oncoclear/steps/data_preprocessor.py b/oncoclear/steps/data_preprocessor.py
index 2b52329e..47ed54f8 100644
--- a/oncoclear/steps/data_preprocessor.py
+++ b/oncoclear/steps/data_preprocessor.py
@@ -53,7 +53,7 @@ def data_preprocessor(
     columns and normalize numerical columns. See the documentation for more
     information:
 
-        https://docs.zenml.io/how-to/build-pipelines/use-pipeline-step-parameters
+        https://docs.zenml.io/concepts/steps_and_pipelines#pipeline-parameterization
 
     Args:
         random_state: Random state for sampling.
diff --git a/oncoclear/steps/data_splitter.py b/oncoclear/steps/data_splitter.py
index b8aeb3b8..dd8825a7 100644
--- a/oncoclear/steps/data_splitter.py
+++ b/oncoclear/steps/data_splitter.py
@@ -40,7 +40,7 @@ def data_splitter(
     In this example, the step can be configured to use different test
     set sizes. See the documentation for more information:
 
-        https://docs.zenml.io/how-to/build-pipelines/use-pipeline-step-parameters
+        https://docs.zenml.io/concepts/steps_and_pipelines#pipeline-parameterization
 
     Args:
         dataset: Dataset read from source.
diff --git a/oncoclear/steps/inference_predict.py b/oncoclear/steps/inference_predict.py
index c893ab31..13b5c15b 100644
--- a/oncoclear/steps/inference_predict.py
+++ b/oncoclear/steps/inference_predict.py
@@ -40,7 +40,7 @@ def inference_predict(
     In this example, the step can be configured to use different input data.
     See the documentation for more information:
 
-        https://docs.zenml.io/how-to/build-pipelines/use-pipeline-step-parameters
+        https://docs.zenml.io/concepts/steps_and_pipelines#pipeline-parameterization
 
     Args:
         model: Trained model.
diff --git a/oncoclear/steps/model_evaluator.py b/oncoclear/steps/model_evaluator.py
index 27e7a060..c75c1a61 100644
--- a/oncoclear/steps/model_evaluator.py
+++ b/oncoclear/steps/model_evaluator.py
@@ -57,7 +57,7 @@ def model_evaluator(
     does not meet the minimum criteria. See the documentation for more
     information:
 
-        https://docs.zenml.io/how-to/build-pipelines/use-pipeline-step-parameters
+        https://docs.zenml.io/concepts/steps_and_pipelines#pipeline-parameterization
 
     Args:
         model: The pre-trained model artifact.
diff --git a/vertex-registry-and-deployer/README.md b/vertex-registry-and-deployer/README.md
index 83689ce1..63c3989b 100644
--- a/vertex-registry-and-deployer/README.md
+++ b/vertex-registry-and-deployer/README.md
@@ -3,7 +3,7 @@
 
 Welcome to your ZenML project for deploying ML models using Google Cloud's Vertex AI! This project provides a hands-on experience with MLOps pipelines using ZenML and Vertex AI. It contains a collection of ZenML steps, pipelines, and other artifacts to help you efficiently deploy your machine learning models.
 
-Using these pipelines, you can run data preparation, model training, registration, and deployment with a single command while using YAML files for [configuration](https://docs.zenml.io/user-guides/production-guide/configure-pipeline). ZenML takes care of tracking your metadata and [containerizing your pipelines](https://docs.zenml.io/how-to/customize-docker-builds).
+Using these pipelines, you can run data preparation, model training, registration, and deployment with a single command while using YAML files for [configuration](https://docs.zenml.io/user-guides/production-guide/configure-pipeline). ZenML takes care of tracking your metadata and [containerizing your pipelines](https://docs.zenml.io/concepts/containerization).
 
 
 ## 🏃 How to run
@@ -19,7 +19,7 @@ source .venv/bin/activate
 pip install -r requirements.txt
 ```
 
-We will need to set up access to Google Cloud and Vertex AI. You can follow the instructions in the [ZenML documentation](https://docs.zenml.io/how-to/auth-management/gcp-service-connector)
+We will need to set up access to Google Cloud and Vertex AI. You can follow the instructions in the [ZenML documentation](https://docs.zenml.io/stacks/service-connectors/connector-types/gcp-service-connector)
 to register a service connector and set up your Google Cloud credentials.
 
 Once you have set up your Google Cloud credentials, we can create a stack and run the deployment pipeline:
@@ -66,7 +66,7 @@ python run.py --inference-pipeline
 
 ## 📜 Project Structure
 
-The project loosely follows [the recommended ZenML project structure](https://docs.zenml.io/how-to/setting-up-a-project-repository/best-practices):
+The project loosely follows [the recommended ZenML project structure](https://docs.zenml.io/user-guides/best-practices/set-up-your-repository):
 
 ```
 .
@@ -84,4 +84,4 @@ The project loosely follows [the recommended ZenML project structure](https://do
 ├── README.md                                   # This file
 ├── requirements.txt                            # Extra Python dependencies
 └── run.py                                      # CLI tool to run pipelines with ZenML                                    # CLI tool to run pipelines on ZenML Stack
-```
\ No newline at end of file
+```
diff --git a/zencoder/README.md b/zencoder/README.md
index 4a19d532..854e4708 100644
--- a/zencoder/README.md
+++ b/zencoder/README.md
@@ -32,7 +32,7 @@ One of the first jobs of somebody entering MLOps is to convert their manual scri
 2. Type annotating the steps properly
 3. Connecting the steps together in a pipeline
 4. Creating the appropriate YAML files to [configure your pipeline](https://docs.zenml.io/user-guides/production-guide/configure-pipeline)
-5. Developing a Dockerfile or equivalent to encapsulate [the environment](https://docs.zenml.io/how-to/customize-docker-builds).
+5. Developing a Dockerfile or equivalent to encapsulate [the environment](https://docs.zenml.io/concepts/containerization).
 
 Frameworks like [ZenML](https://github.com/zenml-io/zenml) go a long way in alleviating this burden by abstracting much of the complexity away. However, recent advancement in Large Language Model based Copilots offer hope that even more repetitive aspects of this task can be automated.
 
diff --git a/zenml-support-agent/README.md b/zenml-support-agent/README.md
index d0d11689..08f803c3 100644
--- a/zenml-support-agent/README.md
+++ b/zenml-support-agent/README.md
@@ -136,10 +136,9 @@ It is much more ideal to run a pipeline like the agent creation pipeline on a re
 you have to [deploy ZenML](https://docs.zenml.io/user-guides/production-guide/deploying-zenml) 
 and set up a stack that supports 
 [our scheduling
-feature](https://docs.zenml.io/how-to/build-pipelines/schedule-a-pipeline). If you
+feature](https://docs.zenml.io/concepts/steps_and_pipelines/scheduling). If you
 wish to deploy the slack bot on GCP Cloud Run as described above, you'll also
 need to be using [a Google Cloud Storage Artifact
 Store](https://docs.zenml.io/stack-components/artifact-stores/gcp). Note that
 certain code artifacts like the `Dockerfile` for this project will also need to
-be adapted for your own particular needs and requirements. Please check [our docs](https://docs.zenml.io/how-to/setting-up-a-project-repository/best-practices) 
-for more information.
+be adapted for your own particular needs and requirements. Please check [our docs](https://docs.zenml.io/user-guides/best-practices/set-up-your-repository) for more information.