AI-native development & deployment

End-to-end GenAI (AI-native) development & deployment — two flavors: Azure / Spring AI (Java) and Python (LangChain)

Below is a compact, interview-ready walkthrough you can speak from — step-by-step from requirements → design → build → test → deploy → monitor → optimize. For each phase I list concrete tools/frameworks, why they’re used, and what outputs you produce. At the end I give a short 30-second summary you can use in interviews.

1. High-level lifecycle (common to both stacks)

1. Discovery & requirements (use cases, data, compliance)

2. Design (architecture, data flows, privacy & governance)

3. Prototype / POC (quick feedback loop)

4. Build (code, prompts, ingestion, vectorization)

5. Test & Evaluate (unit, integration, prompt regression, golden tests)

6. Harden & Secure (secrets, network, identity, policies)

7. CI/CD → Deploy (canary / blue-green)

8. Observability & Evaluation (metrics, traces, LLM eval)

9. Feedback loop & Optimize (model routing, prompt tuning, retrain)

10. Postmortem & runbook updates

2. Concrete example use case (for interview):

“Policy Q&A” — user asks regulatory/policy questions; system answers grounded in policy documents with auditability and compliance.

3. Azure + Spring AI (Java) — detailed steps & tools

Phase A — Requirements & Design

• Stakeholders: Legal, Compliance, Business SME, Infra, SecOps.

• Non-functional: latency ≤ 500ms P95, audit log retention 7 years, PII masking, RBAC per tenant.

• Output artifacts: capability map, data classification, sequence diagram, RACI, SLA matrix, risk register.

Phase B — Architecture & Components

• Components: APIM (edge), Ingress/WAF, LLM Gateway (Spring Boot), RAG Service (Spring AI microservice), Vector DB (pgvector/Azure Cognitive Search), Redis cache, Postgres (metadata/audit), Azure Key Vault, Azure OpenAI (or private model).

• Security: Azure AD (Entra) + mTLS, Private Endpoints, VNET, Workload Identity.

• Deployment: AKS for services, ACR for images, Terraform/ARM for infra.

Phase C — Development (Build)

• Project structure: Spring Boot modules:

  • llm-gateway (routing, tenant, quotas)

  • rag-service (retrieval, prompt assembly)

  • prompt-store (prompt versions API)

  • audit-service (ingest logs)

• Prompt management: Prompt-as-code in Git + Prompt Registry (Postgres) for hot updates.

• Libraries: Spring AI (ChatClient), Spring WebFlux, Micrometer/OpenTelemetry, Spring Security.

• Local dev: Docker Compose (pgvector, local redis), test API stubs for OpenAI.

Phase D — Test (coverage & frameworks)

• Unit tests: JUnit + Mockito (service logic, prompt templating).

• Integration tests: Testcontainers for Postgres/pgvector; Spring Boot test slices.

• Contract tests: Spring Cloud Contract (APIs between gateway ↔ rag).

• Prompt regression: JUnit tests that run golden Q&A pairs against model simulators or dev endpoint.

• Load test: k6 or Gatling (simulate API traffic + measure token/cost).

• Security testing: SAST (SonarQube), dependency scanning, penetration testing checklists.

Phase E — CI/CD & Deployment

• CI: Azure DevOps / GitHub Actions

  • Steps: build → unit tests → static analysis → build docker image → push to ACR → publish helm chart

• CD: GitOps (Flux/ArgoCD) or Azure DevOps release

  • Deploy to canary namespace → run automated smoke & golden tests → promote to prod.

• Canary & rollback: metric gates (error rate, latency, hallucination metric) before full rollout.

Phase F — Observability & Evaluation (runtime)

• Tracing/metrics: OpenTelemetry + Micrometer → Azure Application Insights & Grafana/Prometheus.

• Logs: ELK or Azure Log Analytics for raw logs + audit trails.

• LLM-specific: instrument spans for vector_search, prompt_build, llm_call, reranker. Compute metrics:

  • recall@K, hallucinaton rate, answer correctness, token counts, cost per call.

• Alerts: PagerDuty + Teams channel for incidents.

• Evaluation: periodic golden set evaluation job (nightly) to measure drift.

Phase G — Ops & Optimization

• Token/cost governance: token metering service in LLMOps; apply quotas in gateway.

• Model routing: A/B changes, prefer private model for sensitive tenants.

• Retrain / Reindex: reindex vector store via blue-green strategy (atomic index swap).

• Postmortem & runbooks: automated postmortem template, playbooks for rollback & kill switch.

4. Python ecosystem (LangChain / LangGraph) — detailed steps & tools

Same use case: Policy Q&A.

Phase A — Requirements & Design

• Same stakeholders and artifacts; call out LangChain PO/ML engineer and MLOps owners.

Phase B — Architecture & Components

• Components: APIM → FastAPI (API Layer) → Agent service (LangChain/LangGraph) → Retriever (pgvector/Azure Cognitive Search) → LangSmith (optional) / OpenTelemetry → Azure OpenAI.

• Containerization: Docker image (FastAPI + LangChain), deployed to AKS.

• Secrets: Key Vault → Kubernetes secret (Workload Identity).

Phase C — Development (Build)

• Structure: api/ (FastAPI endpoints), agent/ (chains, tools), ingest/ (OCR, chunk, embed), tests/.

• Prompt management: prompts in Git or Prompt Registry (Postgres) + feature flags.

• Libraries: langchain, langgraph, pydantic, fastapi, httpx, redis, transformers (if local models).

• Local dev: Docker Compose with local vector DB; use pytest and test doubles for OpenAI.

Phase D — Test (coverage & frameworks)

• Unit tests: pytest + pytest-mock.

• Integration tests: Testcontainers-python for Postgres/pgvector; use requests & TestClient for FastAPI.

• Prompt regression: pytest table of golden Q&A pairs; run against dev OpenAI endpoint or local LLM.

• Contract tests: schemathesis / pact for API contracts.

• Load test: k6; concurrency tests of agent workflows.

• Security tests: Bandit, safety checks for deps.

Phase E — CI/CD & Deployment

• CI: GitHub Actions / Azure DevOps

  • Steps: lint (ruff/flake8), black, pytest (unit), build docker image → push ACR

• CD: Helm charts / ArgoCD deploy to AKS

  • Canary: deploy with feature flag to 5% traffic; run automated tests, compare metrics

• Container runtime: include health probes, request timeouts, concurrency limits (uvicorn/gunicorn workers).

Phase F — Observability & Evaluation

• Tracing: OpenTelemetry (manual spans around retrieval, prompt_build, llm_call) → OTEL Collector → Azure Monitor / Grafana.

• If using LangSmith: enable env vars LANGCHAIN_TRACING_V2, LANGCHAIN_API_KEY and secrets in k8s to stream traces.

• Metrics: Prometheus + Grafana for latency, errors, token cost, recall@K.

• Logs: structured JSON logs to Log Analytics.

• Evaluation: nightly batch eval job (pytest + golden dataset) and human-in-the-loop reviews saved to evaluation DB.

Phase G — Ops & Optimization

• Retrain triggers: monitor recall/accuracy drop; trigger re-embedding or model retrain pipelines in MLOps.

• Cost control: token budgeting, gateway quotas.

• Model routing: route sensitive tenants to private models; non-sensitive to cloud models.

• Incident playbooks: implement circuit breaker around LLM calls, fallbacks to static rules.

5. Cross-cutting concerns (both stacks)

• Prompt versioning & approval: Git + CI, or Prompt Registry DB with status: draft/approved/active.

• Privacy & compliance: PII detection & masking before storing or sending to LLM; redact logs; keep audit trails.

• Vector index updates: do reindex as background job, swap index atomically; maintain checksum validation.

• Testing prompts: include unit tests for prompt templates + golden examples; include guardrail tests (regression for toxic outputs).

• Governance: maintain policy for what can be answered automatically vs escalate to human; maintain RACI and approval SLAs.

• Secrets: Azure Key Vault + Kubernetes Workload Identity (no env variable keys).

• Infrastructure as code: Terraform/Bicep for infra; Helm for K8s.

6.

“Given a GenAI use case — e.g., a policy Q&A — we follow a full AI-native lifecycle from discovery to continuous optimization. We begin with requirements and governance (compliance, data classification), design a RAG + LLM architecture (APIM → LLM Gateway → RAG service → vector store → LLM), then prototype and validate with a golden dataset. In Java we implement RAG using Spring AI microservices, instrument with OpenTelemetry + Micrometer exporting to Azure Application Insights, and run prompt regression in CI. In Python we implement the agent using LangChain wrapped by FastAPI, use LangSmith for deep LLM traces in R&D or OpenTelemetry in production, and run nightly evaluation jobs. Deployment is containerized (ACR → AKS), delivered through a GitOps/CD pipeline with canary gating on business and LLM metrics (latency, hallucination rate, recall@K). Post-deploy we monitor both infra (Prometheus, AppInsights) and LLM quality (golden set, hallucination alerts), trigger reindex/retrain pipelines, and feed improvements back into prompt and model versions. Security and auditability — Key Vault, private endpoints, managed identities — are enforced across the stack.”

7.

• Requirements: legal sign-off, data classification, KPI targets

• Design: RAG + LLMOps control plane (where RAG sits)

• Dev: modular microservices, prompt-as-code, versioned registry

• Test: unit + integration + prompt regression + contract + load

• CI/CD: build image → canary → gate on LLM metrics → promote

• Prod: APIM + AKS + private endpoints + Key Vault + RBAC

• Observability: traces (OTel), metrics (Prometheus/Micrometer), LLM eval (golden set)

• Ops: token governance, model routing, atomic index swaps, playbooks