# AI-Systems-Engineering

This section bridges traditional Systems Engineering with the AI infrastructure content already in the wiki. Treat AI systems (models, agents, platforms) as engineered systems requiring the same rigor: needs analysis, architecture, trade studies, V&V, and operational excellence.

## Philosophy

AI systems are software-intensive, data-centric, and often non-deterministic. Apply SE discipline while adapting for:
- **Non-determinism** - Evaluation over exact assertion
- **Rapid evolution** - Models, prompts, and tools change frequently
- **Data dependency** - Quality of data often matters more than code
- **Cost sensitivity** - Inference costs are real operating expenses
- **Safety concerns** - Guardrails are requirements, not features

## Contents

| Topic | File(s) | Purpose |
|---|---|---|
| **Inference** | Link to `../Inference/` | Model serving, engines, batching, optimization |
| **MLOps/LLMOps** | Link to `../MLOps/` | Deployment, registry, rollout, lifecycle |
| **Observability** | Link to `../Observability/` | Tracing, metrics, evals in production |
| **Reliability** | Link to `../Reliability/` | Guardrails, circuit breakers, fallbacks |
| **EdgeAI** | Link to `../EdgeAI/` | On-device, quantization, local inference |
| **Platform** | Link to `../Platform/` | IDP, prompt management, cost showback |

## Applying SE to AI Systems

| SE Phase | AI Application |
|---|---|
| **Needs** | "Reduce support costs by X%" not "Build an LLM app" |
| **Requirements** | Include eval thresholds (faithfulness > 0.9), latency SLOs, cost limits |
| **Trade Studies** | Model selection, hosting (API vs self-host), framework choice |
| **Architecture** | Agent topology, tool design, memory, retrieval |
| **V&V** | Eval suites, red teaming, human evaluation, A/B tests |
| **Risk** | Hallucination, PII, prompt injection, cost overruns |
| **Operations** | Continuous eval, drift detection, model updates |

## Related Notes

- [[../Foundations/Systems-Thinking.md]] — Holistic thinking
- [[../Requirements-Engineering/Needs-Vs-Requirements.md]] — Start from needs
- [[../Trade-Studies/Trade-Study-Methodology.md]] — Model selection trade studies
- [[../Verification-Validation/V-Model-And-VV-Planning.md]] — Eval-driven V&V
- [[../Risk-Reliability/Resilience-Engineering.md]] — AI system resilience
- [[../../AgenticEngineering/Production/]] — Production patterns
- [[../../AgenticEngineering/Evaluation/]] — Evaluation frameworks

## Sources

* ML Engineering by Chip Huyen  
* Designing Machine Learning Systems by Chip Huyen  
* LLM Engineer's Handbook  
* OpenAI Cookbook, Anthropic docs  
* Eugene Yan's blog (eugeneyan.com)  
* vLLM/SGLang documentation