Profile LMS attendance data and publish tenant-safe dashboards every weekday before 8am.
LMS
Map enrichment sources. Connect crawlers, course catalogs, internal product data, LLM calls, prompt versions, and delivery targets.
LLM crawling and enrichment workflows
Course, catalog, and content enrichment needs crawl orchestration, LLM classification, validation, retries, and product delivery.
Mage
I mapped this education use case as a Mage workflow.
- connect crawlers, catalogs, internal data, LLM calls, and prompt versions
- fan out crawl jobs, classify content, validate quality, and retry failures
- publish product search, catalog, BI, and LLM-pipeline outputs
- preserve crawl state, approvals, prompt lineage, and delivery history
You can inspect the crawler state, enrichment logic, validation results, retry paths, prompt versions, approvals, and delivery targets before product teams rely on the output.
Use Mage to run LLM crawling and enrichment workflows for education products
Course, catalog, and content enrichment needs crawl orchestration, LLM classification, validation, retries, and product delivery.
Crawlers, catalogs, internal data, and LLMs feed Mage fan-out, enrichment, validation, retry, and delivery workflows for product search, catalogs, BI, and LLM pipelines.
From education source chaos to governed action
Use Mage as the execution layer between the specific sources, validation rules, governed delivery targets, and reusable context this education workflow needs.
Why did attendance freshness drop?
The LMS export arrived 42 minutes late, delaying the tenant-scoped attendance metric.
LMS export delayed 42 min
Validate generated outputs. Validate crawl completeness, duplicate content, classification quality, schema fit, retry status, and product-readiness.
Learning statsRun delayed
Inspect run
Late event stream foundPrivacy checks readyOpen run logs
Register enrichment context. Store crawl state, classifications, validation results, retries, prompt versions, approvals, and delivery history as reusable context.
Pipeline
Table
Chart
File
AnswerAttendance freshness dropped after the LMS export arrived late.
Deliver product-ready content. Publish enriched course content to search, catalogs, BI, product features, and downstream LLM pipelines.
How Mage runs LLM crawling and enrichment
Mage turns crawling and LLM enrichment into governed workflows for education product search, catalogs, BI, and AI pipelines.
Run the course enrichment workflow. Fan out crawlers and catalog sources, enrich with LLMs, validate quality, retry failures, deliver product outputs, and register context.
The LLM enrichment workflow is ready to review. I fanned out crawler and catalog sources, enriched content, validated quality, handled retries, delivered product outputs, and registered enrichment context.
Enrichment comparisonWithin tolerance
Current modelModernized workflow
98.6% matched · Course tags, levels, and catalog fields checked
Start with the enrichment. Start with the course crawl, catalog enrichment, LLM classification, or product search update that needs orchestration.
Fan out sources. Connect crawlers, course catalogs, internal product data, LLM calls, prompt versions, and delivery targets.
Validate enrichment quality. Validate crawl completeness, duplicate content, classification quality, schema fit, retry status, and product-readiness.
Model enrichment logic. Make classification, enrichment, approval, prompt, and product-delivery logic inspectable before it becomes a product dependency.
Deliver enriched content. Publish enriched course content to search, catalogs, BI, product features, and downstream LLM pipelines.
Register enrichment context. Keep crawl state, classifications, validation results, retries, prompt versions, approvals, and delivery history together.
What the workflow includes
Orchestrate content enrichment Mage orchestrates crawler fan-out, course catalogs, internal data, and LLM enrichment jobs so content workflows are visible and recoverable.
Validate generated content Validation, retry, review, and classification checks run before enriched course or catalog content reaches product systems.
Deliver product-ready context Search, catalogs, BI, LLM pipelines, and product features receive versioned outputs with run history and enrichment context.
Use case details
The workflow view shows sources on the left, Mage execution in the middle, and governed outputs plus AI context on the right.
Business problem
Course, catalog, and content enrichment needs crawl orchestration, LLM classification, validation, retries, and product delivery.
Mage workflow story
Run crawler fan-out, catalog enrichment, LLM classification, validation, retry, approval, and product-delivery workflows for education content.
Workflow diagram
Crawlers, catalogs, internal data, and LLMs feed Mage fan-out, enrichment, validation, retry, and delivery workflows for product search, catalogs, BI, and LLM pipelines.
Queued sources
Crawler sources
Course catalogs
Internal systems
Nightly warehouse
Mage crawl runcourse_enrichment
Fan-out crawlsource clusters
LLM classify + enrichsearch-ready metadata
Review alert1 low-confidence result
Search index
Product catalog
Bring Mage the crawlers, catalogs, internal data, LLM prompts, validation checks, retry needs, and product targets. Mage will map the enrichment run from crawl fan-out through governed delivery.