| Troubleshooting | L37-L48 | Diagnosing and fixing Azure AI Search indexer/skillset issues, including errors, warnings, OData filters, portal debug sessions, private link, and storage/metrics discrepancies |
| Best Practices | L49-L69 | Designing, scaling, and troubleshooting Azure AI Search indexing/query pipelines, including enrichment, data modeling, concurrency, cost/perf tuning, chunking, vectors, and handling blob/SQL changes. |
| Decision Making | L70-L83 | Guidance on planning and managing Azure AI Search services: pricing and tiers, capacity estimation and upgrades, regional choices, SDK/API migrations, and cost optimization. |
| Architecture & Design Patterns | L84-L90 | Architectural guidance for Azure AI Search: RAG patterns, knowledge store design, multitenancy and tenant isolation, and multi-region/high-availability deployment designs. |
| Limits & Quotas | L91-L100 | Limits, quotas, and scheduling for Azure AI Search: billing/free enrichment, indexer run windows and runtime caps, service/index/vector size limits by tier and platform. |
| Security | L101-L138 | Securing Azure AI Search: identity/RBAC, keys and encryption, private networking, indexer access to data sources, document-level ACLs, and policy/compliance controls. |
| Configuration | L139-L238 | Configuring Azure AI Search: data sources, indexers, indexes, analyzers, skills/enrichment, vectors, semantic ranker, monitoring, and agentic retrieval/answer synthesis settings. |
| Integrations & Coding Patterns | L239-L300 | Patterns and code for integrating Azure AI Search with data sources, indexers, vectorization, OData/Lucene queries, semantic ranking, custom skills, and knowledge stores/Power BI. |
| Deployment | L301-L307 | Deploying and moving Azure AI Search services with ARM/Bicep/Terraform, plus guidance on cross-region moves and checking regional feature and SKU availability. |