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#AI Platform#AI Gateway#Model Routing#Model Registry#Prompt Management#RAG Infrastructure#Evaluation#Data Pipelines#MLOps#LLMOps#Observability#Multi-Tenancy#Deployment#Educational AI

AI Platform Architecture & Engineering

A production-oriented guide to designing an AI platform that supports multiple models, RAG systems, agents, evaluation, observability, governance, and educational AI workloads.

AI Platform Architecture & Engineering

1. Introduction#

A production Generative AI application is rarely just:

Architecture & Data Flow
Frontend
 |
 v
LLM API

A serious AI platform typically needs:

Architecture & Data Flow
Users
 |
 v
Application
 |
 v
AI Gateway
 |
 +--> Model Routing
 +--> Authentication
 +--> Rate Limits
 +--> Policy
 +--> Cost Controls
 |
 v
AI Services
 |
 +--> LLMs
 +--> Embeddings
 +--> Rerankers
 +--> Vision
 +--> Speech
 |
 +--> RAG
 +--> Agents
 +--> Tools
 |
 v
Evaluation + Observability

The goal of this notebook is to understand how to engineer that platform.

2. Learning Objectives

By the end of this notebook, you should understand:

What an AI platform is
AI platform vs AI application
Platform architecture
AI gateways
Model provider abstraction
Model routing
Model registries
Prompt management
Configuration management
RAG infrastructure
Embedding pipelines
Document ingestion
Evaluation pipelines
Dataset management
Model versioning
Deployment automation
Observability
AI-specific metrics
Cost tracking
Multi-tenancy
Authorization
Data isolation
Reliability patterns
Caching
Queue-based processing
Batch inference
GPU infrastructure
Kubernetes-style deployment concepts
CI/CD for AI systems
Educational AI platform architecture
Platform governance
End-to-end platform engineering

3. AI Application vs AI Platform

An application solves a user problem.

Example:

›AI Tutor

A platform provides reusable infrastructure.

text
AI Gateway
Model Router
RAG Service
Evaluation
Observability
Authentication

Therefore:

Architecture & Data Flow
 AI PLATFORM
 |
 +-------------+-------------+
 | | |
 v v v
 Tutor Teacher AI Admin AI

4. Why Build a Platform?

Without a platform, each application may implement:

text
Authentication
LLM calls
RAG
Logging
Evaluation
Cost tracking
Retries
Caching

independently.

This creates duplicated engineering work.

A platform centralizes common capabilities.

5. Platform Layers

A useful architecture:

text
Layer 1: Applications
Layer 2: AI Orchestration
Layer 3: AI Gateway
Layer 4: Models
Layer 5: Data / Retrieval
Layer 6: Evaluation
Layer 7: Observability
Layer 8: Infrastructure
Layer 9: Security / Governance

6. Reference Architecture

Architecture & Data Flow
 USERS
 |
 v
 APPLICATION LAYER
 |
 v
 API GATEWAY
 |
 v
 AI PLATFORM
 |
 +-------------------+-------------------+
 | | |
 v v v
 AI Gateway RAG Service Agent Runtime
 | | |
 v v v
 Model Router Vector Store Tools
 | | |
 +-------------------+-------------------+
 |
 v
 MODEL REGISTRY
 |
 +----------------+----------------+
 | | |
 v v v
 Cloud Self-hosted Local
 Models Models Models
 |
 v
 OBSERVABILITY
 |
 v
 EVALUATION

7. AI Gateway

An AI gateway is a central entry point for model interactions.

Instead of applications calling providers directly:

Architecture & Data Flow
Application
 |
 +--> Provider A
 +--> Provider B
 +--> Provider C

use:

Architecture & Data Flow
Application
 |
 v
AI Gateway
 |
 +--> Provider A
 +--> Provider B
 +--> Provider C

8. Why an AI Gateway?

A gateway can centralize:

text
Authentication
Authorization
Rate limiting
Model routing
Retries
Fallbacks
Logging
Cost tracking
Caching
Policy enforcement

9. Provider Abstraction

Application code should ideally depend on an internal interface.


🐍 PythonInteractive WebAssembly
class ModelClient:

 def generate(self, messages, **kwargs):
 raise NotImplementedError

Provider implementations can then differ internally.

Architecture & Data Flow
Application
 |
 v
ModelClient
 |
 +--> Provider A
 +--> Provider B
 +--> Local model

This reduces vendor lock-in.

10. Standardized Request

A platform can normalize requests:


🐍 PythonInteractive WebAssembly
request = {
 "model": "reasoning-large",
 "messages": messages,
 "temperature": 0.2,
 "max_tokens": 2000,
 "tenant_id": tenant_id
}

The gateway translates this into provider-specific APIs.

11. Model Routing

Different models are useful for different tasks.

Example:

Architecture & Data Flow
Simple classification
 -> Small model

Complex reasoning
 -> Large reasoning model

Embedding
 -> Embedding model

Image analysis
 -> Vision model

12. Rule-Based Routing

A simple router:


🐍 PythonInteractive WebAssembly
def route(task):

 if task == "classification":
 return "small-model"

 if task == "reasoning":
 return "large-model"

 if task == "embedding":
 return "embedding-model"

 return "default-model"

This is often a good starting point.

13. Cost-Aware Routing

Suppose:

Mathematical Formulation
Model A = cheap
Model B = expensive

The router can use:

›Cheap model for easy requests
Expensive model for difficult requests

A production policy may consider:

text
Quality
Latency
Cost
Availability
Data sensitivity
Tenant policy

14. Capability-Aware Routing

A model registry can describe:

json
{
 "model": "model-x",
 "capabilities": [
 "text",
 "vision"
 ],
 "context_length": 128000,
 "supports_tools": true
}

The router can select only compatible models.

15. Sovereignty-Aware Routing

For sensitive workloads:

Architecture & Data Flow
Sensitive data
 |
 v
On-prem/private model

For low-risk workloads:

Architecture & Data Flow
General data
 |
 v
Approved cloud model

This enables policy-based model selection.

16. Model Registry

A model registry tracks model information.

Example:

text
Model name
Version
Provider
Capabilities
Context length
License
Deployment location
Cost
Latency
Status
Evaluation score

17. Model Registry Example


🐍 PythonInteractive WebAssembly
model = {
 "name": "education-model",
 "version": "1.2",
 "capabilities": ["text"],
 "deployment": "private-cloud",
 "status": "production",
 "quality_score": 0.91
}

18. Model Lifecycle

A model may progress through:

Architecture & Data Flow
Development
 |
 v
Evaluation
 |
 v
Staging
 |
 v
Canary
 |
 v
Production
 |
 v
Retired

19. Prompt Management

Prompts should be treated as versioned assets.

Instead of:


🐍 PythonInteractive WebAssembly
prompt = "You are a tutor..."

inside application code, use:

›Prompt Registry

with:

text
prompt_name
version
template
variables
owner
evaluation_score
status

20. Prompt Versioning

Example:

text
Tutor Prompt
v1
v2
v3

You can compare:

text
Quality
Latency
Cost
Safety

before production rollout.

21. Prompt Templates


🐍 PythonInteractive WebAssembly
template = """
You are an educational tutor.

Topic:
{topic}

Student question:
{question}

Explain at the student's level.
"""

Variables should be explicitly controlled.

22. Prompt Evaluation

A prompt change can affect:

text
Correctness
Verbosity
Safety
Hallucination
Citation quality

Therefore prompt changes should pass evaluation before deployment.

23. Configuration Management

Separate configuration from code.

Example:

yaml
models:
 default: education-small
 reasoning: education-large

limits:
 max_tokens: 4000
 max_tool_calls: 8

rag:
 top_k: 8

Never store secrets directly in configuration files.

24. Secrets Management

Secrets include:

text
API keys
Database credentials
Signing keys
Cloud credentials

Use a dedicated secrets system rather than source code.

25. RAG Infrastructure

A platform can provide a reusable RAG service.

Architecture & Data Flow
Documents
 |
 v
Ingestion
 |
 v
Parsing
 |
 v
Chunking
 |
 v
Embedding
 |
 v
Vector Store

26. Document Ingestion Pipeline

Architecture & Data Flow
Upload
 |
 v
File validation
 |
 v
Virus/security scan
 |
 v
Parsing
 |
 v
Metadata extraction
 |
 v
Chunking
 |
 v
Embedding
 |
 v
Index

27. Incremental Indexing

Do not reprocess every document after every change.

Track:

text
Document hash
Version
Last indexed timestamp
Embedding version
Parser version

Then process only changed documents.

28. Embedding Versioning

If you change the embedding model:

›Old embeddings

may not be directly compatible with:

›New embeddings

Track:

text
embedding_model
embedding_version
vector_dimension

29. Vector Store Design

A multi-tenant vector record might contain:


🐍 PythonInteractive WebAssembly
{
 "id": "chunk-123",
 "tenant_id": "tenant-1",
 "course_id": "course-42",
 "document_id": "doc-9",
 "text": "...",
 "embedding": [...],
 "metadata": {...}
}

Authorization must be enforced before returning results.

30. RAG Service API

A reusable service might expose:

text
POST /documents
POST /documents/{id}/index
POST /search
POST /retrieve
POST /rerank

Applications do not need to know how the vector infrastructure works.

31. Evaluation Platform

A mature AI platform needs automated evaluation.

Architecture & Data Flow
Dataset
 |
 v
Prompt / Model
 |
 v
Generated outputs
 |
 v
Evaluator
 |
 v
Metrics
 |
 v
Dashboard

32. Evaluation Dataset

A dataset may contain:


🐍 PythonInteractive WebAssembly
{
 "input": "Explain Newton's second law.",
 "expected": "Force equals mass times acceleration.",
 "metadata": {
 "subject": "physics",
 "grade": 8
 }
}

33. Evaluation Types

Use multiple levels:

text
Unit evaluation
Retrieval evaluation
Model evaluation
Prompt evaluation
Agent evaluation
End-to-end evaluation
Human evaluation

34. Regression Evaluation

Suppose:

Mathematical Formulation
Version 1 accuracy = 91%
Version 2 accuracy = 86%

The deployment pipeline should detect the regression.

Example:

Mathematical Formulation
Quality threshold = 90%

Version 2
86%

Deployment:
BLOCK

35. Evaluation Gates

A CI/CD pipeline can enforce:

Architecture & Data Flow
Tests
 |
 v
Evaluation
 |
 +--> Pass -> Deploy
 |
 +--> Fail -> Block

36. Data Pipeline

AI systems depend on data pipelines.

Architecture & Data Flow
Source data
 |
 v
Validation
 |
 v
Transformation
 |
 v
Storage
 |
 v
Indexing

For educational platforms:

text
Course content
Student activity
Assessment results
Teacher content

may feed different systems.

37. Data Quality

Check:

text
Missing values
Duplicate records
Invalid metadata
Corrupt files
Unexpected formats
Schema changes

Bad data produces bad AI behavior.

38. Event-Driven AI Architecture

Instead of processing everything synchronously:

Architecture & Data Flow
Upload document
 |
 v
Event
 |
 v
Queue
 |
 v
Worker
 |
 v
Embedding / indexing

This improves scalability.

39. Queues

Queues are useful for:

text
Document processing
Embedding
Video transcription
Batch inference
Evaluation
Large imports

They separate request handling from expensive processing.

40. Async Architecture

Architecture & Data Flow
Client
 |
 v
API
 |
 +--> Store request
 |
 +--> Queue job
 |
 v
Immediate response

Worker
 |
 v
Process job
 |
 v
Store result

41. Batch Inference

Some workloads do not require real-time responses.

Examples:

text
Generate embeddings
Evaluate 100,000 examples
Summarize archived documents
Generate practice questions

Batch processing can improve efficiency.

42. Caching

Useful cache levels:

text
Response cache
Embedding cache
Retrieval cache
Prompt-prefix cache
Model result cache

Cache keys should include relevant versions and authorization context.

43. Cache Safety

Do not accidentally share:

›Tenant A result

with:

›Tenant B

Cache keys must account for:

text
Tenant
User authorization
Prompt version
Model version
Relevant data version

44. Observability

AI observability includes:

text
Logs
Metrics
Traces
Evaluations
Costs

45. Request Trace

A single request may produce:

Architecture & Data Flow
API
 |
 v
Router
 |
 v
Retriever
 |
 v
Reranker
 |
 v
LLM
 |
 v
Validator

A trace should connect all stages.

46. Useful Metrics

Track:

text
Request count
Error rate
Latency
TTFT
Tokens/sec
Input tokens
Output tokens
Cost
Cache hit rate
Retrieval latency
Tool latency

47. AI-Specific Observability

Also track:

text
Hallucination rate
Citation correctness
Retrieval relevance
Tool success rate
Agent steps
Fallback rate
Safety blocks
Evaluation scores

48. Structured Logging

Example:


🐍 PythonInteractive WebAssembly
log = {
 "request_id": request_id,
 "tenant_id": tenant_id,
 "model": model,
 "prompt_version": prompt_version,
 "input_tokens": input_tokens,
 "output_tokens": output_tokens,
 "latency_ms": latency_ms,
 "status": "success"
}

Avoid logging sensitive user content unnecessarily.

49. Cost Tracking

Estimate request cost from:

text
Input tokens
+
Output tokens
+
Model pricing

Track by:

text
Tenant
Application
Model
Feature
User
Time period

This enables FinOps.

50. Multi-Tenancy

A platform may serve:

text
School A
School B
School C

Data must remain isolated.

Architecture & Data Flow
Tenant A
 |
 +--> Users
 +--> Courses
 +--> Documents
 +--> Vectors

Tenant B
 |
 +--> Users
 +--> Courses
 +--> Documents
 +--> Vectors

51. Tenant Isolation

Possible strategies:

text
Tenant IDs
Database row-level security
Separate databases
Separate vector namespaces
Separate storage buckets

The correct strategy depends on risk and scale.

52. Authorization

Authentication asks:

›Who are you?

Authorization asks:

›What are you allowed to access?

AI systems need both.

53. Educational Authorization

Example:

Architecture & Data Flow
Student
 -> own progress

Teacher
 -> assigned classes

School admin
 -> school-level analytics

Platform admin
 -> platform-level administration

The AI layer must respect these boundaries.

54. GPU Infrastructure

Self-hosted models may require GPUs.

Consider:

text
GPU memory
Compute capability
Model size
Quantization
Batch size
Concurrency
KV cache

55. Model Memory Estimate

A rough estimate for model weights:

Mathematical Formulation
Memory ≈ parameters × bytes per parameter

For a 7B model at FP16:

Mathematical Formulation
7 billion × 2 bytes
≈ 14 GB

Actual runtime memory is higher because of:

text
KV cache
Activations
Framework overhead
CUDA/runtime allocations

56. Quantization

Quantization reduces memory.

Examples:

text
FP16
INT8
INT4

Lower precision can improve deployment efficiency, but may affect quality.

Evaluate the actual model after quantization.

57. Inference Serving

A model server typically handles:

text
Requests
Batching
Scheduling
GPU execution
Streaming
KV cache

The application should not manage GPU execution directly.

58. Model Serving Layer

Architecture & Data Flow
AI Gateway
 |
 v
Model Router
 |
 v
Inference Server
 |
 v
GPU

Multiple replicas can provide:

text
Availability
Scaling
Load balancing

59. Autoscaling

Scale based on signals such as:

text
Request rate
Queue depth
GPU utilization
Latency

AI workloads may require more sophisticated scaling than ordinary HTTP services.

60. Reliability Patterns

Use:

text
Timeouts
Retries
Circuit breakers
Fallbacks
Health checks
Rate limits
Bulkheads
Queues

61. Retry Carefully

Retries are useful for:

›Transient network failures
Temporary provider errors

Do not blindly retry:

text
Authorization failure
Invalid request
Policy violation

62. Fallback Models

Example:

Architecture & Data Flow
Primary model
 |
 +--> failure
 |
 v
 Fallback model

The fallback should meet the minimum required capability and policy.

63. Circuit Breaker

If a provider repeatedly fails:

Architecture & Data Flow
Provider
 |
 v
Failure threshold reached
 |
 v
Circuit OPEN
 |
 v
Use fallback

After recovery testing:

›Circuit HALF-OPEN

then:

›Healthy -> CLOSED

64. Rate Limiting

Limit by:

text
User
Tenant
API key
Feature
Model
IP

Example:

text
Student:
100 AI requests/hour

School:
100,000 requests/day

Limits should match product requirements.

65. Security Architecture

Architecture & Data Flow
Identity
 |
 v
Authorization
 |
 v
AI Gateway
 |
 v
Policy Engine
 |
 v
Model / Tool

Security should exist before the model call, not only after it.

66. Policy Engine

A policy engine can determine:

text
Which model?
Which data?
Which tools?
Which region?
Which actions?
Which retention policy?

Example:

Architecture & Data Flow
PII detected
 |
 v
Private model required

67. Deployment Environments

Separate:

text
Development
Staging
Production

Avoid testing unvalidated prompts or models directly in production.

68. CI/CD for AI

A production pipeline:

Architecture & Data Flow
Code
 |
 v
Unit tests
 |
 v
Integration tests
 |
 v
Prompt tests
 |
 v
Evaluation
 |
 v
Security checks
 |
 v
Deploy staging
 |
 v
Canary
 |
 v
Production

69. AI Release Checklist

Before deployment:

text
Model evaluated
Prompt evaluated
RAG evaluated
Safety evaluated
Latency evaluated
Cost evaluated
Security reviewed
Rollback available
Observability enabled

70. Canary Deployment

Instead of:

›100% -> new version

use:

›95% -> old
5% -> new

Compare:

text
Quality
Errors
Latency
Cost
Safety

Then gradually increase traffic.

71. Rollback

Always preserve:

text
Previous model version
Previous prompt
Previous configuration
Previous application version

Rollback should be fast.

72. Platform API Design

Example:

text
POST /v1/generate
POST /v1/embed
POST /v1/rerank
POST /v1/search
POST /v1/agents/run
POST /v1/evaluate
GET /v1/models
GET /v1/prompts

Version APIs explicitly.

73. Internal Service Architecture

A modular platform might contain:

text
ai-gateway/
model-router/
model-registry/
prompt-service/
rag-service/
agent-service/
evaluation-service/
observability-service/
policy-service/

These can begin as modules in a monolith and later become separate services when scale requires it.

74. Avoid Premature Microservices

A common mistake is:

›10 services
0 users

Start with clear boundaries.

Split services when there is a real need for:

text
Independent scaling
Independent deployment
Strong ownership
Isolation

75. Educational AI Platform

A practical platform might expose:

text
AI Tutor
Teacher Assistant
Content Generator
Assessment Generator
Study Planner
Document Assistant
Voice Tutor

All reuse:

text
AI Gateway
RAG
Model Router
Evaluation
Observability
Policy

76. Educational AI Platform Architecture

Architecture & Data Flow
 EDUCATIONAL AI
 |
 +----------------------+----------------------+
 | | |
 v v v
 Student Teacher Admin
 | | |
 +----------------------+----------------------+
 |
 v
 AI Gateway
 |
 +---------------+---------------+
 | | |
 v v v
 Model Router RAG Agent Runtime
 | | |
 v v v
 Model Registry Course Data Tools
 | | |
 +---------------+---------------+
 |
 v
 Policy / Authorization
 |
 v
 Evaluation / Safety
 |
 v
 Observability

77. Course Content Pipeline

Architecture & Data Flow
Teacher uploads content
 |
 v
Validation
 |
 v
Parser
 |
 v
Chunker
 |
 v
Embedding
 |
 v
Course Vector Index
 |
 v
Tutor / Teacher Assistant

78. Multimodal Educational Content

A course may contain:

text
PDF
Markdown
Images
Audio
Video
Slides

The ingestion system can normalize these into searchable representations:

Architecture & Data Flow
PDF -> text + images
Image -> OCR + visual embeddings
Audio -> transcript
Video -> transcript + frames + timestamps

79. Multimodal Retrieval

A student asks:

›"Explain the diagram shown in lesson 4."

The platform may retrieve:

text
Lesson text
+
Diagram image
+
Nearby explanation

The final model should receive only authorized course content.

80. Student Progress Integration

AI can combine:

text
Course RAG
+
Student progress
+
Assessment results
+
Current question

Example:

Architecture & Data Flow
Question
 |
 v
Retrieve lesson
 |
 v
Read learning state
 |
 v
Generate personalized explanation

81. Teacher AI Assistant

Teacher request:

›"Create a quiz from chapter 3
for students who struggled with fractions."

Platform:

Architecture & Data Flow
Student analytics
 |
 v
Identify weak topic
 |
 v
Retrieve chapter content
 |
 v
Generate quiz
 |
 v
Evaluate questions
 |
 v
Teacher review

82. Admin AI

Admin may ask:

›"Which courses have the highest dropout rate?"

This should usually use:

›Structured analytics / SQL

rather than RAG.

The AI platform can route the request appropriately.

83. SQL + AI

A safe pattern:

Architecture & Data Flow
Natural language
 |
 v
Intent
 |
 v
Approved query generation
 |
 v
SQL validation
 |
 v
Database
 |
 v
Structured result
 |
 v
LLM explanation

Never allow an unrestricted model to execute arbitrary database operations.

84. Platform Data Stores

Different workloads may require different stores:

Architecture & Data Flow
Relational DB
 -> users, courses, permissions

Object storage
 -> PDFs, videos, images

Vector DB
 -> embeddings

Cache
 -> fast temporary data

Queue
 -> asynchronous jobs

Analytics warehouse
 -> reporting

Graph DB
 -> relationships

Use each for its appropriate workload.

85. Platform Control Plane vs Data Plane

A useful distinction:

Control Plane#

Manages:

text
Models
Prompts
Policies
Tenants
Configurations
Deployments

Data Plane#

Handles:

text
User requests
Inference
Retrieval
Tool calls
Responses

86. Control Plane

Architecture & Data Flow
Admin
 |
 v
Control Plane
 |
 +--> Model Registry
 +--> Prompt Registry
 +--> Policies
 +--> Tenant Config
 +--> Evaluation
 +--> Deployments

87. Data Plane

Architecture & Data Flow
User
 |
 v
API
 |
 v
AI Gateway
 |
 v
Router
 |
 +--> RAG
 +--> Model
 +--> Agent
 +--> Tools
 |
 v
Response

88. Why the Separation Matters

Separating control and data planes helps with:

text
Security
Operations
Scalability
Governance
Change management

A model configuration change should not require changing application code.

89. Platform Maturity Levels

Level 1: Direct APIs#

›Application -> Model API

Level 2: Shared Gateway#

›Applications -> AI Gateway -> Models

Level 3: AI Platform#

Add:

text
RAG
Routing
Evaluation
Observability
Policies

Level 4: Intelligent Platform#

Add:

text
Agent runtime
Automated evaluation
Dynamic routing
Model optimization

Level 5: Enterprise AI Platform#

Add:

text
Governance
Sovereignty
Multi-region
Advanced security
FinOps
Self-service

90. Platform Engineering Project 1

Build a basic AI gateway.

Requirements:

›POST /generate

Support:

text
Provider A
Provider B
Local model

Implement:

text
Authentication
Logging
Timeout
Retry
Fallback

91. Platform Engineering Project 2

Build a model router.

Inputs:

text
task
complexity
data_sensitivity
latency_requirement

Output:

›selected_model

Test routing decisions using a dataset.

92. Platform Engineering Project 3

Build a prompt registry.

Features:

text
Create prompt
Version prompt
Activate version
Evaluate version
Rollback

Example:

text
GET /prompts/tutor
POST /prompts/tutor/versions
POST /prompts/tutor/activate

93. Platform Engineering Project 4

Build a reusable RAG service.

Support:

text
Document upload
Parsing
Chunking
Embedding
Search
Metadata filtering

Add:

text
tenant_id
course_id
document_id

to every retrieval record.

94. Platform Engineering Project 5

Build an evaluation service.

Input:

text
dataset
model
prompt

Output:

text
accuracy
groundedness
relevance
latency
cost

Store every evaluation run.

95. Platform Engineering Project 6

Build an AI observability dashboard.

Track:

text
Requests
Latency
Errors
Tokens
Cost
Models
Tenants
Features

Add:

text
Evaluation scores
Safety blocks
Fallback rate

96. Platform Engineering Project 7

Build an educational AI platform.

Applications:

text
Tutor
Teacher Assistant
Quiz Generator
Study Planner

Shared infrastructure:

text
AI Gateway
Model Router
RAG
Agent Runtime
Evaluation
Observability
Policy

97. Platform Engineering Project 8: Sovereign Educational AI

Design a platform where:

text
Student data
Course data
Inference
Embeddings
Vector database

remain inside an approved private environment.

Add routing:

Architecture & Data Flow
Sensitive data
 -> sovereign/private model

Public/non-sensitive data
 -> approved external model

Measure:

text
Quality
Cost
Latency
Operational complexity

98. Advanced Exercise: Design a Model Router

Create a policy table:

Condition	Preferred Model
Simple question	Small model
Complex reasoning	Large model
Vision request	Vision model
Embedding	Embedding model
Sensitive enterprise data	Approved private model
Provider outage	Fallback model

Then implement the routing logic.

99. Advanced Exercise: Design Tenant Isolation

Compare:

text
Shared database + tenant_id
Separate schemas
Separate databases
Separate vector namespaces

Evaluate:

text
Security
Cost
Complexity
Scalability

100. Advanced Exercise: Design an AI Release Pipeline

Create:

Architecture & Data Flow
Code
 |
 v
Tests
 |
 v
Evaluation
 |
 v
Security
 |
 v
Staging
 |
 v
Canary
 |
 v
Production

Define a rollback condition.

101. Advanced Exercise: Platform Architecture Review

Design an architecture for:

text
10 schools
100,000 students
10,000 teachers
1 million documents

Define:

text
API layer
AI gateway
Model serving
RAG
Databases
Queues
Caching
Observability
Security

Explain your scaling assumptions.

102. Common Mistakes

Mistake 1: Calling providers directly everywhere#

Centralize model access when multiple applications share infrastructure.

Mistake 2: No model abstraction#

Provider changes become expensive.

Mistake 3: No prompt versioning#

You cannot reliably reproduce behavior.

Mistake 4: No evaluation gate#

Bad model or prompt changes reach production.

Mistake 5: Logging everything#

This can create privacy and security problems.

Mistake 6: Ignoring tenant isolation#

One authorization bug can become a major data breach.

Mistake 7: Building microservices too early#

Complexity can grow faster than the product.

Mistake 8: No cost attribution#

AI spending becomes difficult to control.

Mistake 9: No fallback#

A single provider outage can affect the entire product.

Mistake 10: Treating AI infrastructure like ordinary CRUD infrastructure#

LLM systems have unique concerns:

text
Tokens
Context
GPU memory
Model quality
Prompt versions
Retrieval quality
Agent behavior

103. Final Mental Model

Think of the AI platform as a nervous system for AI applications.

Architecture & Data Flow
 AI PLATFORM
 |
 +-----------------+-----------------+
 | | |
 v v v
 CONTROL INTELLIGENCE OPERATIONS
 PLANE | |
 | | |
 v v v
 Models RAG / Agents Observability
 Prompts Models Evaluation
 Policies Tools Cost
 Tenants Retrieval Reliability
 |
 v
 APPLICATIONS
 |
 +--------------+--------------+
 | | |
 v v v
 Tutor Teacher Admin

The platform should make AI capabilities:

text
Reusable
Observable
Evaluatable
Secure
Scalable
Governed
Cost-aware

104. Key Takeaways

An AI platform provides reusable infrastructure for multiple AI applications.
An AI gateway centralizes model access.
Provider abstraction reduces vendor lock-in.
Model routing selects models according to task and policy.
Model registries track lifecycle and capabilities.
Prompt templates should be versioned.
Prompt changes should pass evaluation.
RAG infrastructure should be reusable across applications.
Document ingestion should be asynchronous for expensive workloads.
Embeddings need version tracking.
Metadata is essential for retrieval and security.
Evaluation should be integrated into CI/CD.
AI observability must include AI-specific metrics.
Cost should be tracked by tenant, model, feature, and application.
Multi-tenancy requires strong authorization and isolation.
Queues help separate user-facing APIs from long-running AI jobs.
Caches must respect authorization and model/data versions.
Self-hosted inference requires GPU and memory planning.
Quantization can reduce deployment requirements but requires quality testing.
Reliability needs retries, timeouts, fallbacks, and circuit breakers.
AI systems benefit from control-plane and data-plane separation.
Educational AI platforms can share infrastructure across tutor, teacher, and administrative applications.
Structured analytics should generally use databases rather than RAG.
Multimodal education platforms need specialized ingestion and retrieval pipelines.
Sovereign AI architectures can route sensitive workloads to private infrastructure.
Start with clear modular boundaries before introducing many microservices.
A mature AI platform connects models, data, evaluation, security, and operations.
Platform engineering is what turns individual AI demos into reliable products.

105. Knowledge Check

Question 1#

What is the difference between an AI application and an AI platform?

Question 2#

Why would multiple applications benefit from an AI gateway?

Question 4#

What factors can influence model routing?

Question 7#

Why are queues useful for document ingestion?

Question 8#

What is the purpose of an evaluation gate in CI/CD?

Question 9#

Which AI-specific metrics should an observability system track?

Question 10#

Why is cache isolation important in a multi-tenant system?

Question 11#

What is the difference between a control plane and a data plane?

Question 12#

Why should SQL-based analytics not automatically be implemented as RAG?

Question 13#

What are the major considerations when serving a self-hosted LLM?

Question 14#

When should an organization introduce separate microservices?

Question 15#

How would you design a sovereign educational AI platform?

106. Course Progression

The Generative AI track now progresses through:

Architecture & Data Flow
Generative AI Foundations
 |
 v
Transformers & LLM Architecture
 |
 v
RAG, Embeddings & Vector Databases
 |
 v
LangChain, LangGraph & Agents
 |
 v
LLM Evaluation, Safety & Guardrails
 |
 v
Multimodal Generative AI
 |
 v
Fine-Tuning, LoRA, QLoRA & PEFT
 |
 v
Open-Source, Open-Weight & Sovereign AI
 |
 v
LLMOps, Inference Optimization & Production
 |
 v
End-to-End GenAI Application Projects
 |
 v
Security, Privacy, Governance & Responsible AI
 |
 v
Advanced RAG & Agent Architectures
 |
 v
AI Platform Architecture & Engineering

The next stage should move into advanced AI engineering and infrastructure, including distributed inference, GPU scheduling, model serving at scale, multimodal serving, data/feature pipelines, Kubernetes deployment patterns, advanced AI observability, platform APIs, and production-scale architecture.

Knowledge Checkpoint

AI Platform Architecture Checkpoint

Q1.What is the primary role of an Enterprise GenAI Gateway (e.g. Kong AI Gateway, LiteLLM)?

ATo provide a unified API abstraction layer with centralized authentication, rate limiting, budget quotas, observability, guardrail moderation, and model routing.

BTo train neural networks from scratch.

CTo manage physical network switches.

DTo replace front-end web browsers.

Q2.Why is asynchronous token streaming (via Server-Sent Events - SSE) essential in GenAI web architectures?

AIt delivers generated tokens incrementally to the user's browser in real-time, reducing perceived latency from tens of seconds to sub-second TTFT.

BIt encrypts the browser window.

CIt forces the LLM to run on the client's laptop.

DIt prevents web page crashes.

Q3.What is OpenTelemetry (OTel) instrumentation in GenAI platform observability?

AA standardized framework for capturing traces, spans, latency metrics, token consumption, and prompt-response payloads across distributed AI agent components.

BAn open-source compiler for C++.

CA hardware monitoring chip for GPUs.

DA tool for creating diagrams.

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