| Troubleshooting | L37-L47 | Diagnosing and fixing Azure AI Search indexer and skillset errors, portal debugging, OData filter issues, private link problems, and storage/metrics discrepancies. |
| Best Practices | L48-L66 | Designing, scaling, and troubleshooting AI enrichment and indexing pipelines, optimizing performance/costs, handling data changes, and applying vector, concurrency, and responsible AI best practices. |
| Decision Making | L67-L81 | Guidance on sizing, upgrading, and migrating Azure AI Search services and SDKs, choosing regions, tiers, pricing models, and data source connectors, and planning capacity and costs. |
| Architecture & Design Patterns | L82-L88 | Architectural patterns for Azure AI Search: combining vector and keyword search, designing multitenant or isolated indexes, and building resilient multi-region search deployments. |
| Limits & Quotas | L89-L98 | Limits, quotas, and scheduling for Azure AI Search: billing/free enrichment, indexer runs/resets, schedules, execution quotas on Serverless/S3 HD, service SKUs, and vector index size caps. |
| Security | L99-L138 | Securing Azure AI Search: RBAC/ACL, Entra ID auth, keys, encryption, network isolation (VNet, private endpoints, firewalls), and secure indexer connections to data sources. |
| Configuration | L139-L222 | Configuring Azure AI Search: indexes, indexers, analyzers, skillsets, enrichment, vectorization, semantic ranker, knowledge bases, retrieval behavior, logging, and query options. |
| Integrations & Coding Patterns | L223-L308 | Connecting data sources, indexers, skills, vectorization, and query patterns to build, integrate, and query Azure AI Search knowledge bases and agentic retrieval experiences |
| Deployment | L309-L316 | Deploying and moving Azure AI Search: ARM/Bicep/Terraform provisioning, cross-region migration, and deploying C# search apps to Azure Container Apps. |