ai-engineering-skill_skill

Overview

This skill is a practical guide for building production-grade ML systems based on core AI engineering principles. It consolidates architecture patterns, dataset and finetuning guidance, evaluation methodologies, deployment strategies, and inference cost/latency optimizations. Use it to move models from prototype to reliable, monitored production services while keeping cost and safety in check.

How this skill works

The skill inspects design choices across system components: data pipelines, model selection (prompting, RAG, finetuning), serving patterns, and monitoring approaches. It provides step-by-step recommendations for evaluation metrics, A/B testing, drift detection, and production optimizations like quantization, batching, and caching. The guidance is actionable: pick the right technique, size compute, configure finetuning methods, and instrument production for alerts and feedback loops.

When to use it

• Designing or scaling an AI application architecture (from prototype to production).
• Choosing between prompting, RAG, or finetuning for a task.
• Building and validating datasets for training or finetuning.
• Setting up model evaluation, A/B tests, and monitoring in production.
• Optimizing inference for latency, throughput, or cost.
• Designing guardrails, safety checks, and feedback loops for user-facing models.

Best practices

• Start with clear success metrics and align evaluation datasets to real user tasks.
• Prefer RAG when knowledge freshness or grounding reduces hallucination; finetune only when repeated, task-specific behavior is required.
• Instrument pipelines: log inputs, outputs, latencies, confidence signals, and subset labels for human review.
• Optimize inference in order: eliminate waste, batch requests, quantize, then scale parallelism as needed.
• Use progressive architecture evolution: add components (router, cache, orchestrator) only when justified by traffic and failure modes.
• Implement automated drift detection and lightweight human-in-the-loop review for edge cases.

Example use cases

• Deploying a customer support assistant: start with RAG + retrieval tuning, then finetune on scored dialogs if behavior needs to be baked in.
• Reducing inference cost: quantize the model, enable batching, and add a cache for repeated queries to cut latency and spend.
• Evaluating an open-ended model: use mixed evaluation methods (automated metrics, functional tests, and AI-as-judge) and run A/B tests for user impact.
• Creating a data pipeline for finetuning: collect high-quality labeled examples, filter noise, and iterate with synthetic augmentation only after verification.
• Building an agent: design planner and executor separation, add memory via vector store, and harden with input/output guardrails.