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Foundations of Vibe Coding

Vibe Coding Principles and Mental Models

Core Promptcraft for Consistent Outcomes

Model and Tooling Selection

Workflow Design and Integration

Designing Vibe Coding Pipelines

Interop with Existing Codebases and CI/CD

Data, Context Windows, and Retrieval

Quality, Safety, and Compliance

Security and Privacy by Design

Responsible Vibe Coding: Safety and Accessibility

Production Readiness and Governance

Hands-on Implementation

Building a Minimal Viable Vibe App

Integrating with Deployment Pipelines

Assessment: Knowledge Checks

Quiz: Foundations and Promptcraft

Quiz: Models, Tools, and Retrieval

Quiz: Workflow and CI Integration

Quiz: Security, Privacy, and Safety

Quiz: Production Readiness and Governance

Final Feedback

Learner Survey: Course Experience and Confidence

Production-Ready Vibe Coding: Workflows, Integration, and Compliance is a practitioner’s guide to shipping reliable LLM features that your team can trust in production. Instead of brittle, one-off prompts, you’ll learn a principled approach to intent-first development, evaluation loops, and guardrails that translate creativity into consistent results. We’ll start by establishing the core mental models of vibe coding, then dive into promptcraft, model/tool selection, and data retrieval patterns that keep outputs grounded and reproducible. You’ll design end-to-end pipelines with checkpoints, human-in-the-loop reviews, and regression-safe golden paths. From there, you’ll embed vibe coding into your existing Git workflows and CI/CD, with automated evaluations, drift alerts, and cost/latency observability. A full section is dedicated to quality, safety, and compliance—covering security, privacy, accessibility, governance, and audit trails—so your organization can move fast without breaking trust. Finally, you’ll build a minimal viable vibe app, wire it to deployment pipelines, and practice rollback strategies based on evaluation thresholds. By the end, you’ll have the skills and templates to deliver LLM-powered features that meet real production standards: reliable, measurable, and compliant.

Production-Ready Vibe Coding: Workflows, Integration, and Compliance

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            <p>Production-Ready Vibe Coding: Workflows, Integration, and Compliance is designed to help engineering teams move beyond ad hoc prompt tinkering and into reliable, governed delivery. You’ll learn how vibe coding differs from traditional spec-first development by centering intent, iterative shaping, and measurable outcomes. We begin with foundational principles and mental models—such as intent-first prompts, evaluation loops, guardrails, and alignment metrics like iteration velocity and latency to insight—then translate them to real team roles and SDLC stages. From there, you’ll develop core promptcraft: structuring roles and objectives, setting constraints and rubrics, and applying few-shot and structured prompting, stop conditions, temperature control, and output schemas for deterministic, consistent behavior.</p>

<p>The course then turns to workflow design and integration—composing end-to-end pipelines (prompt → run → observe → critique → refine), checkpoints, fallbacks, and human-in-the-loop approvals. You’ll learn to embed vibe coding into existing codebases and CI/CD with PR templates, automated evaluations, drift alerts, and versioned artifacts, while establishing observability via tracing, prompt logs, and cost dashboards. We’ll cover data strategies that respect context windows, including retrieval patterns, summarization, chunking, and grounding validation with citations and hallucination detection. A dedicated quality, safety, and compliance track equips you to threat-model prompts and tools, minimize and mask data, harden tool calling, and uphold accessibility and localization standards—culminating in production readiness practices like SLAs/SLOs, canaries, feature flags, audit trails, and compliance mapping for privacy, IP, and data residency.</p>

<p>Hands-on modules guide you in building a minimal viable vibe app using an SDK, implementing structured JSON outputs, adding an evaluation harness with automatic rubric scoring, and instrumenting telemetry and cost tracking. You’ll integrate with deployment pipelines through CI jobs for prompt checks, evals, and linting; establish staging, canary, and production environments; and automate rollback using evaluation thresholds, supported by clear runbooks and on-call procedures. Knowledge checks throughout reinforce fundamentals of promptcraft, model and tooling selection, workflow and CI integration, and security and governance. By the end, you’ll be able to ship LLM-powered features with confidence—operationally observable, cost-aware, safe by design, and aligned to your organization’s reliability targets.</p>
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