Production ML Playbook
A practical playbook for building production ML and LLM systems that stay reliable over time. Concrete gates, boundaries, and operational habits you can reuse.
This playbook is opinionated by design. It reflects how I build systems that fail loudly instead of silently.
What you'll get here
A small set of rules that keep systems honest: evaluation first, explicit boundaries, and operability by design.
Reusable system patterns for production ML and LLM features, with where I use them in my projects.
Short pre-ship checklists for reliability, monitoring, and regression prevention.
Core Principles
If you can't measure behavior, you can't ship reliably. Treat evals as part of the product, not a one-off experiment.
Define what the system can and cannot do, enforce it, and make abstention a first-class path.
Monitoring, regression tests, and failure-mode instrumentation are part of system design.
When the system is uncertain or inputs drift, it should surface that state and fall back safely.
Slice metrics by user intent, tenant, data source, and failure mode so regressions don’t hide in averages.
Practical Patterns
What: A pre-ship and pre-answer gate that checks retrieval quality, constraints, and expected behavior.
Why: Prevents confident wrong answers and turns “unknown” into an explicit system state.
Where used: Used in Enterprise RAG Platform.
What: A contract that defines when the system must refuse/abstain and how it communicates uncertainty.
Why: Makes failures visible and keeps the product trustworthy under missing context or ambiguity.
Where used: Applied across RAG systems and QA workflows.
What: Golden sets + hard-case suites run automatically on each change (prompt, retrieval, model, data).
Why: Treats evaluation like software tests: repeatable, versioned, and non-negotiable.
Where used: Patterns show up in my case studies.
What: Track failures by category: retrieval miss, low confidence, contract violation, unsupported claim.
Why: Turns debugging from anecdotes into metrics and shortens time-to-fix.
Where used: Used in reliability-focused work and reported in writing.
Checklists
- Define allowed scope + refusal behavior
- Create a golden set + hard cases
- Add regression thresholds (go / no-go)
- Log inputs/outputs + trace IDs
- Monitor latency + cost distributions
- Define retrieval boundaries + sources
- Add retrieval quality checks (before answer)
- Add faithfulness / attribution checks
- Implement abstain + user-safe messaging
- Track failure modes (miss / low confidence)
- Schema + data quality checks
- Slice dashboards by intent/segment
- Alert on failure-mode rate changes
- Track regression over time (weekly runs)
- Document runbooks for incidents