AI Feature Engineering
Build production AI features in Elixir with LLM architecture, prompt versioning, observability, and governance.
Phase 9 covers building and operating AI-powered features in production Elixir systems. These lessons assume you have completed the core curriculum and are ready to ship LLM-backed capabilities with explicit reliability, security, and operational standards.
You will move through:
- LLM application architecture with Phoenix, Oban, and provider abstraction.
- Prompt version control, evaluation harnesses, and regression gates.
- Observability, cost controls, and runtime operations for AI workloads.
- Security, privacy, and governance for AI-driven applications.
Recommended flow:
- Start with the architecture lesson to establish boundaries and patterns.
- Add prompt versioning and evals before your first production prompt change.
- Build observability and cost controls before scaling beyond initial users.
- Apply security and governance controls before handling sensitive data or regulated workloads.
If you are looking for career-focused AI content, including AI-assisted development workflows and career positioning, see Phase 8: Career and AI Development.
LLM Application Architecture in Elixir
Design reliable LLM-backed features with Phoenix, Oban, retries, and provider abstraction in Elixir systems.
Prompt Versioning, Evals, and Regression Testing
Implement prompt version control, evaluation harnesses, and regression gates for safer AI feature releases.
AI Observability, Cost, and Runtime Ops
Run AI features with telemetry, budget controls, latency SLOs, and production incident runbooks.
AI Security, Privacy, and Governance
Protect users and organizations with security, privacy, and policy controls for AI-driven Elixir applications.