Testing Lovable: Lessons from Trying to Build an AI Agent

A female programmer staring at complex code in front of her computer — *Asian businesswoman working in front of desktop computer with complex programming source code on monitor screen.*

September 2025 – Experiment Log

I went into this test with a clear question:
Can Lovable serve as a platform for building an AI-powered chatbot that retrieves answers from uploaded documents?

What Worked

Clean UI scaffolding: Lovable made it easy to spin up a front-end quickly. Uploading docs and connecting them to a chat interface was straightforward.
Chunking/indexing pipeline: For plain text files (.txt), the ingestion process actually chunked content properly (e.g., my ORR refugee policy doc split into four neat chunks).
Debugging support: Chat Mode let me peek under the hood, trace the ingestion pipeline, and confirm that chunks were hitting the database.

What Didn’t Work

DOCX parsing failure: Large .docx files (tens of KBs) were truncated to ~300 characters. The mammoth parser Lovable uses failed to extract the full text.
Retriever mismatch: Even when chunks were in the database, the chat interface often reported “0 chunks.” Logs confirmed the data was there — the front-end retriever wasn’t connecting properly.
Keyword-only search: The biggest blocker to my projects. Queries were processed with PostgreSQL’s plainto_tsquery, which requires literal keywords. Ask “Who signed the policy letter?” and it fails, because the word “signed” never appears in the doc. Ask “Angie Salazar” and it works. This limitation kills the natural Q&A experience.
No semantic retrieval: Lovable doesn’t yet support embeddings (pgvector), cosine similarity, or hybrid search.

What I Learned

Best for simple sites: It shines for lightweight landing pages and demos where you control the flow.
Not ready for AI agents: Anything requiring semantic retrieval, embeddings, or flexible pipelines runs into problems.
Debugging was valuable: By stress-testing both tiny test files and realistic policy docs, I uncovered exactly where the platform’s limits are.
Not truly full-stack: Despite the marketing, Lovable isn’t a full-stack AI app builder. It gives you front-end scaffolding and a Supabase backend, but not the semantic retrieval or orchestration layer that real AI agents require.
Technical know-how required: Although Lovable is touted as a no-code platform, building anything beyond a landing page requires you to understand backend concepts like databases, retrievers, and ingestion pipelines. In practice, it’s less “no-code” and more “low-code with hidden architecture.”

In short, Lovable taught me that prototyping is easy but turning prototypes into intelligent agents requires understanding the architecture beneath the surface.

Takeaway

Lovable isn’t suitable for the kinds of AI-powered apps I’m building, at least not yet. For projects like Pitch Perfect (my AI pitch-coaching app) or a Policy Decoder, semantic retrieval isn’t optional. It’s the core of the experience.

If all you need is to demo a user interface, tools like Figma already do that well without the backend complexity. Lovable only makes sense if you want a live prototype with some backend logic. But because that backend is brittle and limited, it ends up stuck in an awkward middle ground: too much for design-only tools, too little for real AI builds.

I’ll move those builds to LangChain + Gradio/Replit, where I control ingestion, embeddings, and retrieval. Lovable taught me one thing loud and clear: UI is easy, but intelligence requires the right infrastructure.

Designer’s Note:
The experience reinforced a principle I live by: test early, fail fast, and let each setback fuel the next build.

For reflections on how SheBuilds and Lovable’s handling of communication shaped trust, see What Hackathons and Failed Prototypes Teach Us About Trust.

I’m not the only one seeing this gap. Here’s another tester’s take: Lovable – Prototyping Powerhouse with a Glass Jaw. I found this webpage AFTER writing my blogpost.