AI Engineering Atlas is an interactive map for understanding modern AI systems—from foundational concepts to production architectures.
AI Engineering Atlas is an interactive map for understanding modern AI systems—from foundational concepts to production architectures. Explore architecture, engineering patterns, and production practices.
Today's AI engineering knowledge is fragmented across papers, blogs, tutorials, and videos. Developers and architects often understand individual technologies — but struggle to see how these components connect into a complete AI system.
AI Engineering Atlas provides a visual and structured way to explore the AI engineering landscape: Models ↓ Retrieval & Knowledge ↓ Agents & Workflows ↓ Runtime & Infrastructure ↓ Memory & Context ↓ Evaluation & Observability ↓ Governance & Production Systems
Instead of learning isolated concepts, users can navigate AI as an interconnected engineering ecosystem.
Modern AI systems are no longer just "LLM + Prompt".
Production-grade AI requires understanding:
- How models become reliable capabilities
- How RAG evolves into enterprise knowledge systems
- How agents are orchestrated and controlled
- How runtime manages planning, execution, memory, and observation
- How AI systems are evaluated, monitored, and governed
AI Engineering Atlas aims to become a map and learning infrastructure for this emerging AI engineering discipline.
Explore:
- Large Language Models (LLMs)
- Multimodal Models
- Embeddings
- Fine-tuning
- Inference architecture
Explore:
- RAG architectures
- Vector databases
- Knowledge graphs
- Document intelligence
- Enterprise knowledge platforms
Explore:
- Agent architectures
- Planning
- Tool usage
- Multi-agent collaboration
- Agent workflows
Explore:
- Agent runtime design
- Context management
- Memory systems
- Execution loops
- Scheduling and orchestration
Explore:
- Evaluation frameworks
- Observability
- Safety mechanisms
- Human-in-the-loop systems
- Enterprise AI governance
AI Engineering Atlas is designed for:
Understand architecture patterns and production implementations.
Navigate the transition from traditional software engineering to AI-native systems.
Understand AI capabilities, limitations, and product possibilities.
Design scalable enterprise AI platforms.
AI is becoming a new computing paradigm.
The next generation of software will not be built only around applications and APIs, but around intelligent systems composed of:
- Models
- Knowledge
- Reasoning
- Tools
- Runtime
- Governance
AI Engineering Atlas is building the map for this new engineering landscape.
- AI system architecture maps
- Technology landscape visualization
- Engineering concept relationships
- Interactive dependency graphs
- Architecture patterns
- Production examples
- Implementation demos
- Community contributions
- Engineering playbooks
- AI system design patterns
- Real-world case studies
- Next.js
- React
- Tailwind CSS
- Vercel
- v0.app
This repository is automatically synchronized with your deployments on v0.app.
Changes made through v0 deployments are automatically pushed to this repository.
Live deployment:
https://vercel.com/heguang005-5755s-projects/v0-ai-engineering-demo-platform
Development workspace: