ADLC: building and operating AI systems
A disciplined lifecycle for AI products — from data, experimentation and evaluation to deployment, monitoring and governance.
ADLC (AI Development Lifecycle) is the specialized lifecycle for building and operating AI/ML systems — different from a traditional SDLC because it revolves around data, probability and drift. A good AI product does not end at “go-live”; it requires continuous evaluation, monitoring and risk governance throughout its life.
Most GenAI projects stall between PoC and production for lack of a clear lifecycle: no trustworthy evaluation, no control over drift and cost, no risk/compliance governance. ADLC turns AI from a hit-or-miss experiment into a repeatable production capability — especially important as Vietnam’s AI Law 134/2025/QH15 sets accountability and transparency requirements.
A 6-phase lifecycle — AI products
- 1
1 · Problem & data
Frame the problem by value, collect and govern data, and build the evaluation set.
- 2
2 · Experiment & model
Choose the approach (prompting, RAG, fine-tune) and experiment with proper tracking.
- 3
3 · Evaluation & red-team
Automated plus human evals; adversarial testing for safety, bias and hallucination.
- 4
4 · Deployment
Ship to production with guardrails and version control for models and prompts.
- 5
5 · Monitor & operate
Track quality, drift and inference cost; alert and respond promptly.
- 6
6 · Govern & improve
Risk governance, compliance and a feedback loop for continuous improvement.
Areas we go deep on
Each area is a deep content track — articles, reference frameworks and tools added over time.
The data lifecycle for AI
Collection, labeling, quality control and lineage that feed the model.
RAG, fine-tune or prompting?
A decision framework to choose the approach by problem, data and cost.
Evaluation & red-teaming
Build trustworthy eval suites and adversarial tests for AI systems.
LLMOps/MLOps in production
Deploy, version and automate the operations of AI systems.
Monitoring drift, quality & cost
Detect quality regression, data drift and control inference cost.
Responsible AI governance
Risk, transparency and compliance framework — aligned with AI Law 134/2025.
Outcomes you get
- ✓ A shorter path from PoC to production
- ✓ AI systems continuously evaluated and monitored
- ✓ Risk governance and compliance built in
Need an adoption roadmap for your enterprise?
A 30-minute conversation to assess where you are and outline a feasible first step.