How Radar Fix Prompts and the AI Advisor Fix Your AEO

A Failing AEO Score Is Useless Until You Can Fix It

A score tells you that you have a problem. It does not fix the problem. That gap is where most AI visibility tools stop and where teams stall. When we analyzed why 9 in 10 websites fail AEO, the average score across 59 audited domains was just 26 out of 100. The findings were clear. What to actually do about them, on a specific page, was not.

Radar is built to close that gap. Every finding becomes an action item, every action item generates a fix prompt pre-filled with your audit data, and a multi-turn AI advisor is there for the judgment calls a static report cannot answer. This post is the fix companion to the AEO analysis: how the fix prompts and the advisor turn a failing score into shipped changes, usually without rewriting a word of your content.

The point of this piece is not that AEO is hard. It is that the fix is mechanical once you have the right prompt and a way to resolve the few judgment calls. That is the entire job of the two Radar features below.

How Do Radar Fix Prompts Work?

A Radar fix prompt is a context-rich prompt generated for each action item, pre-filled with your own audit data. That is the whole idea, and it is the difference between a tool that reports problems and a tool that helps you solve them.

When the AEO Auditor finishes, Radar converts each finding into an action item on the dashboard. Next to each one is a generated prompt that already contains the specific page, the exact missing signal, and the surrounding context the model needs. You do not describe your situation to an AI. The prompt has done that for you. You copy it, paste it into Claude, ChatGPT, or Cursor, and you get output you can ship. For the most common fixes, Radar also includes dedicated llms.txt and schema markup generators so you do not even need the round trip.

Why pre-filled context changes the output

Anyone can ask an AI "how do I add FAQ schema." The answer is generic because the question is generic. A fix prompt pre-filled with your audit data asks a different question: here is this page, here is the FAQ schema it is missing, here are the questions and answers already on it, generate the markup. The model now has everything it needs to return something correct on the first try.

This is also why the fixes rarely require a content rewrite. The prompts target structure and markup: schema, headings, front-loaded answers, freshness signals. The words you already wrote are usually fine. What was missing is the machine-readable scaffolding around them, and that is exactly what a pre-filled prompt is good at generating.

What a Fix Prompt Looks Like for Each AEO Failure

The AEO analysis identified five structural failures that account for almost all lost points. Each one maps cleanly to a fix prompt.

Four of the five are pure markup and structure, which means a content owner can apply them without engineering. Only the JavaScript-rendering fix sometimes needs a developer, because moving content from a client-side render into server-rendered HTML is a build change rather than a copy and paste. The advisor, covered next, is how you tell the difference before you start.

The FAQPage schema and Speakable schema prompts tend to deliver the fastest gains, because missing structured data is the single most common AEO failure and schema is the cheapest thing on the board to add.

What Does the Radar AI Advisor Actually Help With?

The Radar AI Advisor is a multi-turn assistant you talk to after running an audit, and it exists for the questions a static report cannot answer. A report can tell you a page scored 24 out of 100 and lists six findings. It cannot tell you which finding to fix first for your situation, whether the schema you just added is enough, or why your score barely moved after a change you thought was significant.

The advisor can, because it has your audit context. You ask in plain language and it answers against your actual results rather than generic AEO theory. Common questions it handles well include which fix will move the score most on a specific page, whether a finding is worth the effort for your goals, how two findings relate to each other, and what to check when a fix did not land the way you expected.

In practice the fix prompts and the advisor work as a pair. The prompts do the mechanical work of generating fixes. The advisor handles the judgment that decides which prompts to run, in what order, and whether you are done. If you want the full step-by-step of where these live in the dashboard, the Radar user guide walks through every screen.

How the Six Implementation Threads Sequence the Work

Radar does not hand you a flat list of disconnected findings. It groups fixes into six implementation threads, each with ordered steps and progress tracking, so you work a coherent sequence instead of guessing what to do next.

For a failing AEO score, the Structured Data and AI Answer Optimization threads carry most of the points. The threads also keep you honest. Because each one tracks progress, you can see what is done and what remains, and the audit is repeatable, so you confirm each fix rather than assuming it worked.

Which Fix Should I Tackle First?

Start with the cheapest fixes that move the score most: front-load an answer at the top of each section, then add FAQ and Speakable schema. Both are low effort and high impact, and together they often clear the gap between an F and a passing grade.

After that, repair the heading hierarchy, add Article and Organization schema with an honest dateModified, and address JavaScript-only rendering last because it is the one fix that may need engineering. The advisor will rank these for your specific page, and the AI Answer Optimization thread sequences them for you. The bar is low enough that small moves matter: in our dataset only 2 of 59 domains reached a C or better, so a single page that climbs from an F to a C is already ahead of nearly the entire field.

The workflow that ties it together is a loop, not a one-time pass. Run the AEO Auditor, let Radar turn the findings into fix prompts, paste them into Claude or Cursor, ask the advisor about anything ambiguous, ship the changes, and re-run the audit to confirm. Each pass clears findings and moves the score, and the implementation threads show you how far you have come.

A Worked Example: From an F to Passing on One Page

Here is the loop the way it actually runs on a single page. Suppose a services page scores 24 out of 100 on the AEO Auditor. The report lists the findings plainly: no FAQ or Speakable schema, the opening of each section buries the answer instead of leading with it, two heading levels are skipped, and there is no dateModified anywhere. Crawl access is fine, so every lost point is downstream, on answerability rather than reachability.

Radar turns each finding into an action item, and the first one, missing FAQ schema, generates a fix prompt that already contains the page, the questions and answers detected on it, and the exact schema gap. It reads roughly like this.

You paste that into Claude or Cursor, get valid FAQPage JSON-LD back, and drop it into the page. Two minutes. The next action item front-loads the answers: its prompt rewrites the first sentence of each section into a standalone, quotable answer using the content already there, no new research required. A few minutes more.

Then a judgment call comes up that no report can resolve. You added FAQ schema, but the page also has a long comparison section, and you are not sure whether that passage is worth Speakable schema or whether FAQ is enough. You ask the advisor. Because it has your audit context, it answers for this page rather than in general: the comparison is the most quotable passage on the page, so mark it Speakable and skip it elsewhere. That is the kind of call you would never get from a flat list of findings, and getting it wrong wastes effort in both directions, either marking up everything or marking up nothing.

From there you repair the heading hierarchy with its prompt, add Article and Organization schema with an honest dateModified, and re-run the audit. The score climbs out of the F band, the resolved findings clear, and the Structured Data and AI Answer Optimization threads show most steps complete. Total time is an afternoon, with no content rewrite and no engineering beyond confirming the page was already server-rendered.

That is the whole workflow in one page. The fix prompts did the mechanical generation, the advisor resolved the single judgment call, and the repeatable audit confirmed the result landed. Multiply that across the pages that matter and a failing AEO score stops being a verdict and becomes a checklist.

Turn the Score Into Shipped Fixes

The audit is the easy part. The reason most sites stay stuck at a failing AEO score is the distance between a finding and a fix on a real page. Radar collapses that distance: fix prompts pre-filled with your audit data, an advisor for the judgment calls, and six threads that sequence and track the work. The five AEO failures are structural, the prompts generate the structure, and the loop confirms it landed. And because every fix is verified by a re-audit rather than assumed, the score you finish with is real, not aspirational. Run it on the pages that matter most and the gains compound across the whole site, page by page, fix by fix.

If you have a score and no plan, start the loop on one page that should be getting cited and is not.

Ready to fix yours?

• Run the AEO Auditor - Free, scores any page and produces the action items that become fix prompts
• Open the Radar platform - Generate fix prompts, talk to the advisor, and track the implementation threads
• Read the AEO analysis - Why 9 in 10 sites fail AEO and what the five failures are
• Talk to Pixelmojo - If you want the fixes shipped for you, not just generated