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Coursera

Effective Prompt Engineering on Lovable

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Overview

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Unlock the full potential of Lovable and improve how you build web apps with AI. This course teaches you to direct Lovable's chat and agent modes with precise prompts, so the pages you request match your written spec instead of drifting off target. Throughout the course, you'll explore: Prompt Fundamentals: Apply the context–constraint–outcome framework to write unambiguous prompts and catch ambiguity early. Agent Mode Planning: Break multi-part features into ordered steps and review Lovable's build plan before it runs. UI Refinement: Fix spacing, alignment, and layout issues with short follow-up prompts guided by a screenshot. AI-Assisted Debugging: Paste a console error with structured context and turn vague complaints into reproducible bug prompts. Design Consistency: Set a standing style-guide prompt that keeps colors, spacing, and components aligned across pages. Complex Integrations: Scope third-party API work into stages, prompt for database and backend changes, and add loading and error states. By the end, you'll be able to build spec-matched pages, debug broken features, keep a multi-page app visually consistent, and integrate third-party services end to end.

Syllabus

  • How to Write Clear, Precise Prompts for Lovable
    • Wondering how to write a prompt that gets Lovable to build exactly what you pictured, on the first try instead of the fifth? This module answers exactly that question. By the end of this module you will write structured, unambiguous prompts that consistently produce accurate, on-spec UI in Lovable. You'll learn the context-constraint-outcome framework for prompt writing, compare a vague prompt against a specific one side by side, and prompt a real, spec-matched component from scratch — using Lovable's chat mode and a written product spec as your only inputs. This module is designed for developers, product builders, and no-code founders who are new to prompt engineering for AI app builders and want to stop rewriting prompts by trial and error. No coding experience is required — everything works on Lovable's free plan, and the prompting framework transfers directly to other AI coding assistants like ChatGPT or Claude if you use those too. You'll finish with a working pricing page component generated from your own written spec, plus a reusable context-constraint-outcome prompt template, ready to apply to any new feature you ask Lovable to build.
  • How to Structure Multi-Step Instructions for Lovable's Agent Mode
    • Wondering how to get Lovable's agent mode to build a multi-step feature without losing the plot halfway through? This module answers exactly that question. By the end of this module you will decompose a complex feature into an ordered instruction set and drive Lovable's agent mode through it to a finished, working build. You'll break a login/signup flow into sequential agent steps, review and approve an agent-generated build plan before execution, and turn a stakeholder's verbal feature request into a clean, numbered agent prompt — using Lovable's agent mode and a live requirements conversation. This module is designed for developers and product builders who can already write a single, clear chat prompt and now need to manage multi-step, agentic AI builds instead of one-shot generations. No prior agent-mode experience is required — no coding needed, and everything runs on Lovable's free plan. You'll finish with a complete, tested authentication flow built entirely through agent mode, plus a numbered task-decomposition template, ready to reuse whenever a feature is too large for a single prompt.
  • How to Iterate and Refine AI-Generated UI with Follow-Up Prompts
    • Wondering how to get Lovable to fix the small stuff — spacing, alignment, visual details — without re-prompting from scratch every time? This module answers exactly that question. By the end of this module you will refine AI-generated UI through a sequence of short, targeted follow-up prompts until it matches your intended design. You'll fix spacing and layout issues with iterative follow-up prompts, refine a provided component across three iterations to match a target design, and ask Lovable for its own improvement suggestions and decide what to accept — using Lovable's chat and a target design as your only inputs. This module is designed for developers and product builders who can already generate a first-pass component and now want a repeatable prompt-refinement process instead of accepting the first result. No design background is required — no coding needed, and the whole workflow runs on Lovable's free plan. You'll finish with a component refined to match a target design, plus a set of reusable UI-refinement prompt patterns, ready to apply the next time a generated component needs polishing rather than a full rebuild.
  • How to Debug and Fix Code Issues Through Prompting
    • Wondering how to turn "it's broken" into a bug report Lovable can actually act on? This module answers exactly that question. By the end of this module you will diagnose a broken feature and prompt Lovable to a working fix using a structured bug description instead of guesswork. You'll paste a real error message into chat and guide Lovable to a fix, ask Lovable to explain its own generated code before requesting a change, and interview a user to turn a vague complaint into a structured, reproducible bug prompt — using Lovable's chat mode and your browser's developer console. This module is designed for developers and product builders who can already read a basic error message and now want a reliable prompting method for AI-assisted debugging instead of pasting the whole file and hoping. No formal debugging training is required — no advanced coding knowledge needed, and everything works on Lovable's free plan. You'll finish with a fixed, working form submission plus a reusable bug-report prompt template covering expected behavior, actual behavior, and reproduction steps, ready to use the next time something breaks.
  • How to Maintain Design Consistency Across a Lovable App
    • Wondering how to stop your Lovable app from looking like five different apps stitched together? This module answers exactly that question. By the end of this module you will set a single style-guide prompt that keeps colors, spacing, and components consistent across every page Lovable generates. You'll write a style-guide prompt that Lovable references across pages, audit a page for visual drift and prompt a correction, and bring an inconsistent settings page in line with your established design system — using Lovable's chat mode and a written style reference. This module is designed for developers and product builders who already have a multi-page app in Lovable and are seeing buttons, spacing, or colors drift between pages. No design-system experience is required — no coding needed, and the workflow runs entirely on Lovable's free plan. You'll finish with a corrected settings page that matches your app's style guide, plus a standing design-consistency prompt you can drop into any new page, ready to keep your UI aligned as the app grows.
  • How to Prompt for Complex Features and Third-Party Integrations
    • Wondering how to prompt Lovable to wire up a real third-party API — payments, auth, or data — without it half-working? This module answers exactly that question. By the end of this module you will scope and prompt a complex, multi-part integration and validate that it works end-to-end inside Lovable. You'll scope an integration into stages before writing a single prompt, prompt a third-party API integration step by step, and test the integrated feature and close gaps with follow-up prompts — using Lovable's chat together with a third-party service such as a public REST API or Stripe in test mode. This module is designed for developers and product builders who are comfortable with single-feature prompts and agent-mode builds and now need to prompt for backend logic, database schema, and external services. No backend engineering background is required — no coding needed, and the module works on Lovable's free plan alongside a free-tier or test-mode account for whichever API you choose. You'll finish with a fully integrated, tested third-party feature, plus a scoping checklist for prompting database and backend changes, ready to reuse for your next integration.

Taught by

Anton Voroniuk

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