AI-Native Application Development: RAG, Agents, Evals & Real Traffic

The anatomy of an application with a model inside it: retrieval over your own data, agents behind a permission boundary, evaluation before rollout, and the plumbing that decides whether it survives real traffic. ▶ The full article, with every diagram and table: https://par2labs.com/resources/ai-nat... ▶ More in the Software Engineering playlist:    / @par2labs   CHAPTERS 0:00 Intro 0:39 The anatomy 1:18 Retrieval 2:02 Building it 4:07 Warning signs 4:37 The takeaway WHAT YOU'LL TAKE AWAY • Pick a first task whose output a human can check quickly — checkability enables everything else. • Retrieval is a search problem: hybrid, reranked, cited, incrementally reindexed. • Authorise every tool call as the end user, never as a service account. • Build the eval set before tuning, and instrument cost and p95 latency from day one. FURTHER READING • Tool use — https://docs.claude.com/en/docs/agent... • pgvector — https://github.com/pgvector/pgvector • LangGraph — https://langchain-ai.github.io/langgr... • promptfoo — https://www.promptfoo.dev/ • OpenTelemetry — GenAI semantic conventions — https://opentelemetry.io/docs/specs/s... • OWASP Cheat Sheet Series — https://cheatsheetseries.owasp.org/ FROM PAR2 LABS • Software engineering services: https://par2labs.com/services/technol... • Every Intel article: https://par2labs.com/resources?utm_so... • Start a project: https://par2labs.com/contact?utm_sour... ABOUT PAR2 LABS builds mission-grade technology, AI, electronics and films. Make Sense. Make a Difference. https://par2labs.com · contact@par2labs.com Narration is AI-voiced (ElevenLabs). The visuals are drawn from code by PAR2 LABS. #SoftwareEngineering #WebDevelopment #PAR2LABS