Maintained reading path from EnhanceLearning.AI — practitioner-grade articles for engineers, architects, and technology leaders building production AI-native systems.
Topic on the site: AI Engineering · Full library: enhancelearning.ai/articles
A curated reading path for AI Engineering. It is not a code SDK — it points to the foundation deep-dives on EnhanceLearning.AI so you can align on concepts, critique designs, and ship production systems that hold up.
These articles span the AI engineering stack, harness design, schema validation, structured-output retries, prompt craft vs engineering, and principles for products that must fail safely.
Staff engineers, AI platform teams, and builders accountable for production SLAs.
- The Difference Between Prompt Craft and AI Engineering — Prompt craft shapes model behaviour; AI engineering ships durable features with validation, fallbacks, and ops. Know where demos end and production begins.
- AI Systems vs Traditional Software Systems — How AI systems differ from classical software in control flow, testing, failure modes, and operations — and what engineers must redesign, not just wrap.
- Why Building AI-Native Systems Requires a New Engineering Discipline — AI-native products need more than software engineering and data science — probabilistic control, evals, tool bounds, and operable failure modes.
- The AI Engineering Stack: Where Reliability, Tools, and Outputs Meet — Map structured outputs, tool calling, guardrails, and reliability controls into one stack — how AI engineering decisions connect across an AI-native feature.
- Why Schema Validation is Non-Negotiable for AI Outputs — Make schema validation a hard gate before any LLM output reaches another system — catch fluent mistakes before they become tickets or refunds.
- Building Retry Logic for LLM Structured Output Failures — Retry structured LLM outputs without burning cost or latency. Separate retriable parse failures from schema bugs that need a fix, not another loop.
- Engineering Principles for Reliable AI-Native Products — Structured outputs, tool reliability, layered guardrails, and predictable failure — the principles that separate durable AI-native products from fragile demos.
- Harness Engineering for Reliable Agents — The agent harness is the real product: tools, permissions, state, stops, and telemetry around a thin model call.
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