From prompts to production learning platform.
Monday: 3 core prompts for personalization. Tuesday: automated agent code. Wednesday: team workflows across instructors, data scientists, and engineers. Thursday: complete technical architecture. Agents, ML pipelines, data flows, scaling patterns, and FERPA compliance for 100,000+ students.
Key Assumptions
System Requirements
Functional
- Ingest student interactions (video watch, quiz attempts, forum posts) in real-time
- Generate personalized learning paths based on mastery levels and learning styles
- Recommend next-best content (videos, readings, exercises) with reasoning
- Detect learning gaps and trigger interventions (hints, alternate explanations)
- Generate adaptive assessments matching student proficiency
- Provide instructor dashboards with class-level and individual insights
- Support multiple content types (video, text, interactive simulations, assessments)
Non-Functional (SLOs)
💰 Cost Targets: {"per_student_per_month_usd":2.5,"ml_inference_per_1k_requests_usd":0.15,"storage_per_student_per_year_usd":0.5}
Agent Layer
planner
L3Decomposes learning requests, selects appropriate tools and models
🔧 Feature Store API, Model Registry lookup, Student profile service
⚡ Recovery: Fallback to rule-based recommendations if ML unavailable, Cache recent plans for similar students, Degrade to generic content if personalization fails
executor
L4Executes learning path generation, content recommendations, assessment creation
🔧 ML Inference Service (recommendation model), LLM API (GPT-4 for question generation), Content metadata service, Prerequisite graph traversal
⚡ Recovery: Retry with exponential backoff (3 attempts), Switch to backup model if primary fails, Return cached recommendations if all models down, Log to human review queue for manual intervention
evaluator
L3Validates content quality, checks prerequisites, ensures learning objective alignment
🔧 Content quality classifier, Prerequisite graph validator, Learning objective alignment scorer
⚡ Recovery: Flag low-confidence items for human review, Apply conservative filtering (reject if uncertain), Escalate to instructor dashboard
guardrail
L2Enforces FERPA/COPPA compliance, PII redaction, content safety, age-appropriateness
🔧 PII detection service (AWS Comprehend, custom NER), Content safety classifier, RBAC policy engine, Age-appropriateness filter
⚡ Recovery: Default to deny if policy check fails, Block all operations until guardrail service recovers, Immediate alert to security team
intervention
L3Detects struggling students, triggers automated interventions (hints, alternate content)
🔧 Anomaly detection model (time-series), Hint generation LLM, Notification service
⚡ Recovery: Queue interventions for manual review if confidence < 0.7, Rate limit interventions to avoid student overwhelm, Fallback to instructor notification
analytics
L2Aggregates learning data, generates insights for instructors, identifies cohort trends
🔧 Time-series aggregation (TimescaleDB), Statistical analysis (percentiles, distributions), Visualization data prep
⚡ Recovery: Serve cached dashboards if real-time unavailable, Degrade to daily batch updates, Alert instructor of stale data
ML Layer
Feature Store
Update: Real-time for critical metrics (mastery, engagement), hourly batch for aggregates
- • student_mastery_scores (per learning objective)
- • avg_time_on_task (7-day rolling)
- • quiz_attempt_count
- • video_completion_rate
- • forum_participation_score
- • peer_collaboration_count
- • learning_style_vector (visual/auditory/kinesthetic)
- • engagement_trend (increasing/stable/declining)
- • prerequisite_completion_status
- • content_difficulty_preference
- • session_frequency (daily/weekly)
- • time_of_day_preference
- • device_type (mobile/desktop/tablet)
- • content_type_preference (video/text/interactive)
- • assessment_performance_trend
Model Registry
Strategy: Semantic versioning (major.minor.patch), Git SHA for reproducibility
- • content_recommender_v3
- • mastery_predictor_v2
- • difficulty_estimator_v1
- • intervention_trigger_v1
Observability Stack
Real-time monitoring, tracing & alerting
0 activeDeployment Variants
Startup Architecture
Fast to deploy, cost-efficient, scales to 100 competitors
Infrastructure
Risks & Mitigations
⚠️ Student data breach (FERPA violation)
Low✓ Mitigation: Encryption at rest + in transit, strict RBAC, audit logs, penetration testing quarterly, SOC 2 Type II certification, cyber insurance
⚠️ Algorithmic bias (unfair recommendations by race/gender/socioeconomic status)
Medium✓ Mitigation: Bias audits during model training, fairness metrics (demographic parity, equalized odds), diverse training data, human review of flagged cases, transparency reports
⚠️ Model drift (recommendations degrade over time)
High✓ Mitigation: Daily monitoring of online metrics (CTR, completion rate), automated retraining triggers (>10% drift), A/B testing before rollout, rollback procedures
⚠️ LLM cost explosion (GPT-4 tokens exceed budget)
Medium✓ Mitigation: Token usage monitoring, rate limiting per tenant, caching of common queries, switch to cheaper models (GPT-3.5) for non-critical tasks, cost alerts ($500/hour threshold)
⚠️ Dependency on third-party APIs (OpenAI, Anthropic outage)
Medium✓ Mitigation: Multi-provider strategy (GPT + Claude + Gemini), fallback to rule-based systems, cached responses, SLA guarantees from vendors
⚠️ Instructor resistance (low adoption)
Medium✓ Mitigation: User research, instructor training, gradual rollout, feedback loops, champion program, demonstrate ROI (time saved, student outcomes)
⚠️ Scalability bottleneck (database/cache overload)
Low✓ Mitigation: Load testing (10x expected traffic), auto-scaling policies, read replicas, sharding strategy, CDN for static content, chaos engineering
Evolution Roadmap
Progressive transformation from MVP to scale
Phase 1: MVP (0-3 months)
Phase 2: Scale (3-6 months)
Phase 3: Enterprise (6-12 months)
Complete Systems Architecture
9-layer architecture from student interface to data storage
Presentation
4 components
API Gateway
4 components
Agent Layer
4 components
ML Layer
5 components
Integration
4 components
Data
4 components
External
4 components
Observability
5 components
Security
5 components
Sequence Diagram - Learning Event Flow
Automated data flow every hour
Data Flow - Learning Event Processing
From student action to personalized recommendation in 350ms
Key Integrations
LMS Integration (Canvas, Blackboard, Moodle)
Content Provider APIs
SSO Integration (SAML, OIDC)
Analytics Export (xAPI, Caliper)
Security & Compliance
Failure Modes & Recovery
| Failure | Fallback | Impact | SLA |
|---|---|---|---|
| LLM API down (OpenAI/Anthropic outage) | Switch to backup LLM provider (GPT → Claude or vice versa), then rule-based recommendations | Degraded personalization quality, but system remains operational | 99.5% |
| ML inference service down | Serve cached recommendations (last 24 hours), then fallback to popularity-based ranking | Stale recommendations, reduced personalization | 99.9% |
| Feature store unavailable | Use cached features (Redis), then default to last-known-good features | Slightly outdated student profiles, minimal impact on recommendations | 99.9% |
| Database connection pool exhausted | Queue requests, scale up connection pool, reject non-critical requests (analytics) | Increased latency for 30-60 seconds, no data loss | 99.9% |
| Guardrail agent detects FERPA violation | Block request immediately, log to security audit, notify admin | Request denied (correct behavior) | 100% (must never allow violations) |
| Content CDN down | Serve from origin (S3), then fallback to cached content | Slower content delivery (500ms → 2s) | 99.5% |
| LMS integration broken (Canvas API change) | Queue grade sync requests, notify instructors, manual grade entry | Delayed grade updates (hours instead of real-time) | 99.0% |
RAG vs Fine-Tuning
Hallucination Detection
Evaluation Framework
Dataset Curation
Agentic RAG
Cold Start Problem
Tech Stack Summary
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