AI & Intelligence Infrastructure Overview

LearnWay is architected from the ground up as an AI-powered learning platform. Rather than treating artificial intelligence as an isolated chatbot add-on, LearnWay embeds Google Gemini generative intelligence across every layer of the learner lifecycle—from initial onboarding assessments and contextual lesson tutoring, to persistent career mentorship, automated quiz generation, and multilingual curriculum localization.

Dual-Tier AI Architecture

LearnWay implements a dual-tier execution model for AI capabilities, balancing immediate learner responsiveness with scalable asynchronous processing:

1. In-Monolith AI Services (`learnway-backend`)

Scope: Direct, synchronous, and latency-critical learning workflows.
Responsibilities:
- In-Lesson AI Tutor: Instant slide-level explanations, summaries, and hints directly within active lessons.
- Project & Code Assessment: Automated evaluation of project deliverables and capstone code submissions.
- Career Roadmap Synthesis: Dynamic creation of customized learning paths based on individual career goals.
- Tier-1 Safety & Caching: Redis-backed rate limiting, profanity filtering, and 24-hour deterministic response caching.

2. Dedicated AI Microservice (`learnway-ai-service`)

Scope: Complex, agentic, multi-step, and resource-intensive AI domains.
Modules:
- ai-mentor/: Persistent mobile companion tracking learner consistency, employability, and growth trajectory.
- ai-quiz-studio/: Automated curriculum-aligned question generation, distractor validation, and taxonomy calibration.
- ai-translation/: Two-step hybrid localization pipeline translating learning content into African regional languages.
- ai-analytics/: Interaction telemetry, latency monitoring, prompt token accounting, and learner satisfaction metrics.

Model Selection & Inference Strategy

LearnWay leverages the Google Gemini model family, matching model capabilities to specific pedagogical tasks:

Use Case	Model Target	Configuration	Latency SLA	Key Objective
In-Lesson AI Tutor	`gemini-2.5-flash`	`thinkingBudget: 0`, max 150 words	`< 1200ms`	Instant clarity without cognitive overload
AI Mentor Conversations	`gemini-2.5-flash`	Conversational prompt with Catalog Guardrails	`< 1500ms`	Motivational, contextual career guidance
Onboarding Skill Assessment	`gemini-2.5-flash`	`responseMimeType: 'application/json'`	`< 2000ms`	Structured diagnostic evaluation
Capstone Project Grading	`gemini-1.5-pro`	Multimodal (Code, PDF, Images)	`< 4500ms`	In-depth criteria rubric & feedback
AI Quiz Studio Synthesis	`gemini-2.5-flash`	JSON schema with distractor validation	Batch	High-quality multiple-choice questions
Multilingual Localization	`gemini-2.5-flash` + Cloud Translate	Two-step prompt with syntax preservation	Asynchronous	Culturally contextualized African translations

Retrieval-Augmented Generation (RAG) & Context Engineering

To eliminate hallucinations and keep AI responses grounded strictly in vetted educational material, LearnWay utilizes a Deterministic RAG & Context Injection Pipeline:

[Incoming Request]
       │
       ▼
[Profanity & Guardrail Filter] ──(Rejected)──► [400 Clean Language Required]
       │
       ▼
[Context Builder]
 ├── Active Slide Content
 ├── Preceding Lesson Hierarchy
 ├── Verified Course Catalog (Single Source of Truth)
 └── Learner Skill Profile
       │
       ▼
[Prompt Assembly & Token Budget (< 3,000 chars)]
       │
       ▼
[Cache Check (Redis)] ──(Cache Hit)──► [Return Cached Response (0ms LLM)]
       │ (Cache Miss)
       ▼
[Promise.race([ Gemini API, 5000ms Timeout ])]
       │
 ┌─────┴────────────────────────┐
 │ (Success)                    │ (Timeout / Failure)
 ▼                              ▼
[Audit Log & 24h Cache]   [Graceful Fallback / Circuit Breaker]

Context Boundary Guarantees:

Catalog Truth: The AI Mentor is strictly constrained to the official Course Catalog. It is programmatically forbidden from recommending nonexistent courses or fabricated subjects.
Foundation-First Sequencing: Prompts enforce that foundational learning tracks must be completed before specialized elective tracks are recommended.
Concise Pedagogical Framing: AI Tutor system prompts enforce a strict 150-word ceiling with plain language to prevent overwhelming mobile learners.

Explore the dedicated AI architecture guides for deeper implementation specifics:

AI Tutor & Mobile AI Mentor: Prompt templates, conversational flows, and catalog guardrails.
AI Assessments & Personalization: Dynamic onboarding diagnostic, capstone evaluations, and career roadmaps.
AI Content Workflows & Translation: Automated Quiz Studio and hybrid multilingual translation engine.
AI Governance, Resilience & Safety: Multi-tier guardrails, circuit breakers, timeout races, and audit telemetry.

Getting Started

Architecture

Guides

AI & Intelligence Infrastructure Overview

AI & Intelligence Infrastructure Overview

Dual-Tier AI Architecture

1. In-Monolith AI Services (`learnway-backend`)

2. Dedicated AI Microservice (`learnway-ai-service`)

Model Selection & Inference Strategy

Retrieval-Augmented Generation (RAG) & Context Engineering

Context Boundary Guarantees:

Subsystem Navigation

​AI & Intelligence Infrastructure Overview

​Dual-Tier AI Architecture

​1. In-Monolith AI Services (learnway-backend)

​2. Dedicated AI Microservice (learnway-ai-service)

​Model Selection & Inference Strategy

​Retrieval-Augmented Generation (RAG) & Context Engineering

​Context Boundary Guarantees:

​Subsystem Navigation

AI & Intelligence Infrastructure Overview

Dual-Tier AI Architecture

1. In-Monolith AI Services (`learnway-backend`)

2. Dedicated AI Microservice (`learnway-ai-service`)

Model Selection & Inference Strategy

Retrieval-Augmented Generation (RAG) & Context Engineering

Context Boundary Guarantees:

Subsystem Navigation