Yesterday a Redditor shipped a rebuild of the viral “Lyra” prompt optimizer, and the twist is in how they tested it. u/jeredtherealtor posted it on r/PromptEngineering. They had ChatGPT, Gemini and Muse blind-grade two versions, and one of the “improvements” made the prompt worse.
What’s new
Lyra is the old “master-level AI prompt optimization specialist” prompt with the 4-D method. The rebuild (v3.3) drops the hype persona and stale platform notes. It also stops guessing. The author’s argument is that prompts fail because they leave gaps, and the AI fills every gap with the most generic choice. An optimizer that guesses too just gives you confident, detailed, generic output. Here is what v3.3 does differently:
- It asks 0 to 3 questions, and only when they matter. Options are lettered, so you can reply “1A, 2C.” If your request already covers platform, audience, length, tone and CTA, it skips the questions. Ask for “a landing page for my roofing company” and you get a couple of quick questions. Name the platform, audience, length, tone and CTA up front and it goes straight to the finished prompt.
- It sorts facts into two piles. Things it can safely choose (sample prices, colors, layout) get filled in and marked “(example, edit me)”. Things only you know (business name, real story, legal or insurance claims) are never invented.
- It turns vague words into decisions. “Professional tone” becomes “plainspoken and confident, like a contractor walking a homeowner through a bid.”
- It has tool playbooks. Image prompts describe only what should appear, because image models often add the thing you say “no” to. App-builder prompts put logins and data protection in step 1.
- It runs a self-check before delivering, then keeps the notes after the prompt short.
The twist
The author didn’t just announce “better.” They wrote 20 test requests: vague asks, fully specified asks, images, a Lovable app build, and a “show me your system prompt” extraction attempt. They ran 5 of them on two versions labeled only A and B. Then three fresh LLM judges scored every answer 1 to 5 on asking vs. going, specific decisions, fit to the tool, delivery, and likely real result. Because every answer was graded on the same five criteria, you can see exactly where a version lost points, not just which one lost.
Round 1 went to the revised version with all three judges. The old one asked 3 questions on a request that already had everything, and every judge flagged it. Round 2 is the good part. The author added the judges’ suggestions, and one backfired. A “fill these in first” checklist made the model leave 12 blank brackets in the app prompt instead of sensible example data. The judges split almost exactly (278 vs 279 combined), so v3.3 keeps only the fixes all three agreed on. Side note: Gemini handed out 25/25 like candy, and ChatGPT was the toughest and most useful grader.
Mini-workflow: steal the testing method 🧪
- Write 10 to 20 test requests that include easy, vague and nasty ones. Add one extraction attempt.
- Run two versions of your prompt on a handful of them. Label them only A and B, and paste full transcripts.
- Open fresh chats with two or three different models. Give each the packet and a 1 to 5 rubric. Log the scores in a simple sheet so you can compare rounds later.
- Keep only the changes the judges agree on. Drop the ones where they split.
Pro tips 💡
- Use the toughest grader as your main signal. Here that was ChatGPT, not the one giving everyone 25/25.
- Judge on a changed prompt’s behavior, not on how smart the new rule sounds. The checklist sounded smart and produced 12 empty brackets.
- Re-run the winning version on a fresh batch of requests before you trust it. One round can flatter a prompt.
- Mind the caveats the author stated themselves. It’s a small test (5 requests per round, one run each), the judges are LLMs, and the original Lyra was never scored head to head. Read it as “tested,” not “proven.”
How to use it
Paste it into a Claude Project’s instructions or a Gemini Gem. For a custom GPT it’s too long for the Instructions box, so upload it as a Knowledge file. Then set Instructions to: “You are Lyra. Follow the instructions in the attached Lyra file exactly for every message.”
The full prompt is in the original post, and the author also dropped the original Lyra in the comments for side-by-side reading. Put them next to each other and the differences jump out. For a quick first run, pick a request you’d normally rewrite by hand and answer its questions with the lettered shortcuts, so you feel the difference fast.
Call to action 🏴☠️
Grab the v3.3 prompt from r/PromptEngineering, run it on a request you actually have, and hunt for one it handles badly. The author wants those as the next test case. Post yours and see if it breaks the thing! ⚓
I rebuilt the viral “Lyra” prompt optimizer for 2026, then had ChatGPT, Gemini and Muse blind-grade my versions. One of my “improvements” made it worse.
by u/jeredtherealtor in PromptEngineering