Your AI Prompts Are Secretly Failing

The 2,236-Prompt Wake-Up Call

Blame usually lands on the model. Users assume GPT-4o, Claude 3.5, or Cursor’s built‑ins are too dumb, too buggy, or simply “not there yet.” An analysis of 2,236 real AI coding prompts says otherwise: the failure point sits almost entirely on our side of the keyboard.

Across those 2,236 prompts, the average quality score landed at 4.3 out of 10 when measured against prompt-writing best practices from OpenAI and Anthropic. Not an edge case, not a few beginners fumbling around—this is how most people are talking to production-grade AI tools today. The models are capable; the instructions are not.

The dataset includes prompts from working developers, no‑code tinkerers, and people building full apps inside tools like Cursor, Windsurf, and Cline. One typical request: “build an advanced portfolio for me.” No tech stack, no pages, no components, no constraints. The user had a clear mental picture; the AI got a vague wish and had to guess.

That gap between wish and instruction turns into three concrete failures. You waste time in endless back‑and‑forth because the model must reverse‑engineer what you meant. You burn money as newer “thinking” models grind for 10–30 minutes on unclear tasks. Worst of all, you lose confidence in your own work when broken assumptions hide inside code that appears to run just fine.

One student using a long‑horizon model like “GBD5 Codex Medium” watched it churn for 10 minutes on “This project uses Supabase. Can we connect its MCP server, please?” before it finally came back with a clarifying question. That’s not AI magic; that’s a $200‑a‑month subscription paying to be confused.

To understand how often this happens, I pulled the dry, scattered documentation from OpenAI and Anthropic, plus their research notes on effective prompting. Then I compressed it into 15 concrete principles, from “be explicit about constraints” to “show examples of what you want,” and scored every one of those 2,236 prompts against them. The results were brutal—and they explain why your AI prompts keep secretly failing, even when the code compiles.

The Hidden Rules of AI Communication

Large language models don’t read minds; they read text. They behave less like psychic coworkers and more like ultra-literal interpreters that only understand what you actually say, not what you meant to say in your head. When 75% of 2,236 prompts fail on clarity alone, the problem isn’t intelligence, it’s missing instructions.

OpenAI and Anthropic have both shipped pages of prompt guidelines for a reason. Their research teams repeatedly show that models perform best when you specify role, task, constraints, and format. Robin Ebers distilled that firehose into 15 principles and then stress-tested them against real prompts; the “brutal” part is how many users ignore those basics.

Think of every prompt as defining an interpretation space. “Build a portfolio” gives the model a vast search area with millions of plausible outputs. Every extra detail you add shrinks that space and reduces the odds the AI wanders into something you never wanted.

Users, meanwhile, walk in with a vivid internal spec: the stack, the vibe, the must-have features. In their head, they’re asking for a sleek, single-page Next.js site with animations, email validation, and Shadcn components. On screen, they type “build an advanced portfolio for me” and expect the model to reverse-engineer their imagination.

Look at the gap between these two prompts:

• “Build me a portfolio.”
• “Build me a single-page Next.js portfolio with three projects, validated email signup, a dark mode toggle, and Shadcn components.”

Both feel similar to the human who “knows what they mean.” To the model, they are different universes. The second collapses the interpretation space so aggressively that you trade five frustrating iterations and 45 minutes for one solid response in about 10.

Mistake #1: From Specific Task to Vague Wish

Seventy-five percent of real-world prompts Robin Ebers analyzed failed for a simple reason: they weren’t clear. People thought they were giving instructions; they were actually lobbing vague wishes at a system that only understands what you spell out.

Consider the real prompt he pulls from his feed: “build an advanced portfolio for me.” That’s all the model gets. No tech stack, no layout, no content, no target user, no constraints.

Missing details stack up fast. The AI has to guess at basics like: - Next.js, React, or plain HTML? - Single-page or multi-page? - Which sections: hero, about, skills, projects, contact? - Any design system like Shadcn, Tailwind, or Material UI? - Functional features: email validation, dark mode, animations, CMS?

The person behind that prompt almost certainly knows these answers. They just never tell the model, so it picks its own interpretation. You then stare at a generic template and think the AI “doesn’t get it,” when you never actually said what “it” was.

Contrast that with a concrete version: “Build a single-page Next.js portfolio with three projects, email validation, a dark mode toggle, and use Shadcn components.” Now the model has a specific task: framework, page count, feature list, and UI library all locked in. There is far less room for it to drift into something you didn’t intend.

This is exactly what OpenAI and Anthropic describe in their prompt guides and research. OpenAI’s own Prompt engineering | OpenAI API docs hammer on specificity, structure, and explicit constraints for a reason: every missing detail becomes an assumption the model has to invent.

The cost shows up in your timeline. Ebers’ analysis found that what should be a single 10-minute prompt often mutates into five prompts over roughly 45 minutes of back-and-forth. You correct the stack, then the layout, then the components, then the copy, then the edge cases—things you could have defined up front.

Multiply that pattern across a workday and you’re burning hours on rework that never needed to happen. The model isn’t underperforming; your prompt is under-specifying. The more complex and “advanced” your ask, the more that gap between wish and instruction turns into real lost time, money, and momentum.

Mistake #2: Burning Cash on Confused AI

Models like GPT-4o, Claude 3.5 Sonnet, and the new “agentic” GPT-4o-based coders quietly flipped the script on how AI works. You’re no longer chatting with a glorified autocomplete box; you’re spinning up an autonomous worker that can plan, browse, edit files, and refactor code for 10–30 minutes at a time.

That long horizon is the selling point: you hand off a complex task and watch the AI grind through docs, APIs, and edge cases while you do something else. But the same feature turns vague prompts into a money shredder, because these models will happily spend your entire compute budget wandering in the dark before they admit they don’t understand you.

Older chat models made their mistakes instantly. You’d get a bad answer in three seconds, sigh, and try again. With agents and “artifacts” that stick around and iterate, the failure mode changes: you get 600 seconds of silent, confident wrongness before the model surfaces a single confused question.

One of Robin Ebers’ students learned this the hard way. He asked an advanced long-running coder, “This project uses Superbase. Can we connect its MCP server, please?” Then he watched it “think” for 10 straight minutes, only for the AI to come back with: “I just want to make sure that we’re on the same page.”

Those 10 minutes weren’t spent wiring up Supabase, testing connections, or generating usable artifacts. They were spent thrashing through guesses about what “MCP server” meant in this context, which project files to touch, and what “connect” should actually do. All that paid compute bought nothing but a clarifying question he could have answered in the original prompt.

Now map that to your subscriptions. If you’re paying $20–$200 a month for GPT-4o-based agents, Claude, or tools like Cursor and Windsurf, every unclear instruction turns into billable confusion. You’re not paying for the AI to work; you’re paying for it to be confused, in 10-minute blocks, over and over again.

Mistake #3: The Landmine in Your Code

Most AI disasters don’t start with a red error message. They start with a green checkmark, a successful build, and a quiet, invisible wrong turn the model took because your prompt left too much room to guess.

Call it silent failure. You ask for “user authentication with JWTs,” the AI scaffolds a working flow, the login form behaves, tokens get issued, everything looks fine. Two weeks later you realize it never handled token rotation, refresh expiry, or secure storage, and now your “working” auth system is a security incident waiting to happen.

Language models fill in gaps with confident assumptions. When your prompt doesn’t define the architecture, data flow, or constraints, the model invents them. It might choose server-side sessions over JWT, REST over WebSockets, or a single-tenant database layout where you needed strict multi-tenant isolation. The app boots, tests pass, demo goes well — and you just locked in a foundation you never actually approved.

That’s where the damage multiplies. You don’t just ship one flawed feature; you stack new features on top of that hidden assumption. You wire more endpoints into the wrong auth layer, spread the leaky data model across 20 files, and copy-paste patterns the AI invented on day one. By the time someone notices, the “fix” means unwinding dozens of commits, not tweaking a single function.

Technical debt from silent failure doesn’t look like debt at first. It looks like progress. Sprints close, PRs merge, velocity charts go up. Only when you try to add something non-trivial — role-based access control, multi-region support, a different billing provider — do you discover that the original AI-generated architecture painted you into a corner.

A prompt that fails loudly is annoying but manageable. You see the stack trace, you see the nonsense code, you roll back and try again. A prompt that fails quietly behaves like a landmine: everything seems safe until you step on the exact combination of edge case, feature request, or scale requirement that triggers the blast.

Once that happens, you don’t just lose time. You lose confidence in your AI-assisted codebase. Every seemingly “good” output now comes with an asterisk: what hidden assumptions did the model bake in this time?

Decoding the 15 Principles of Clarity

Most AI prompt advice reads like vibes. Robin Ebers went the opposite way: he sifted through sprawling OpenAI and Anthropic docs, then pressure-tested their ideas against 2,236 real coding prompts. Out of that collision came 15 brutally practical principles of clarity.

At the core sit a few deceptively simple moves. Define a role: “You are a senior Python developer who specializes in FastAPI and Postgres.” Specify the task: “Refactor this handler to be fully async and add input validation.” Wrap user code and files in delimiters like `###` or ```"""``` so the model can separate instructions, context, and artifacts.

Research from both labs keeps circling back to structure. Models like GPT-4o and Claude 3.5 Sonnet ingest prompts as long token streams; clear sectioning reduces guesswork. When you mark blocks as “CONTEXT,” “EXISTING CODE,” and “TODO,” you compress the search space of plausible interpretations and cut hallucinations. Few-shot examples—3–5 labeled “bad” vs “good” snippets—anchor the pattern even further.

Some of the 15 principles sound almost boring until you see the failure modes they prevent. Ebers emphasizes: - State constraints: performance limits, security rules, tech stack - Define outputs: “Return a single .ts file” or “Respond only with JSON” - Demand reasoning: “Think step by step, then show the final diff only”

Those moves match public guidance like Prompt engineering - Anthropic, which pushes explicit roles, delimiters, and examples as first-class tools. They work not by “making the model smarter,” but by making your intent line up with how transformers actually parse tokens.

Most developers will not memorize 15 rules, so Ebers built a checker that does it for you. Paste a prompt in, and it scores you—4.8/10 in one demo—while pointing to missing context, absent examples, and fuzzy goals, before you burn 20 minutes of autonomous agent time.

Meet Your Free AI Prompt Coach

Meet Prompt Coach, Robin Ebers’ answer to the quiet prompt failures hiding in your workflow. Instead of guessing whether your instructions will land, you paste your prompt into a simple web form and get a verdict grounded in research from OpenAI and Anthropic, not vibes. No login, no paywall, just a brutally honest prompt audit in under a minute.

Under the hood, Prompt Coach scores your prompt against 15 principles of clarity distilled from dense technical docs most developers will never read. It doesn’t just spit out a single number; it breaks that score down by category: how clear your task is, how much context you provide, whether you specify format, style, constraints, and success criteria. Each weak spot comes with concrete, rewrite-this-like-so suggestions.

Think of it as a pre-flight check for AI coding. Before you hand a 30-minute autonomous run to GPT-4o or Claude 3.5 Sonnet, you run the prompt through Prompt Coach and catch the “build an advanced portfolio for me” problem before it burns your credits. The tool flags issues like missing tech stack (Next.js vs. plain HTML), absent UX details (dark mode toggle, Shadcn components), or fuzzy requirements that usually trigger those 10-minute “just to clarify” detours.

Prompt Coach doesn’t just nag; it rewrites. Under each principle, it proposes sharper language and even full “try this prompt instead” variants that bake in specifics: number of pages, data sources, validation rules, edge cases, and testing expectations. You copy, tweak, and only then hit enter in Cursor, Windsurf, or your favorite AI IDE.

Those 2,236 prompts Ebers analyzed didn’t stay in a spreadsheet. They power Prompt Coach’s scoring and examples, reflecting patterns from thousands of real AI coders. When your prompt comes back as a 4.8 out of 10, you’re not being graded against theory; you’re seeing how your instructions stack up against a very common, very expensive problem.

From 4/10 to Perfect: A Prompt Makeover

Most people start with something like: “Create a landing page for a seminar.” Short, confident, and almost useless. Robin Ebers drops that exact kind of prompt into Prompt Coach, waits 30 seconds, and the tool spits back a brutal verdict: 4.8 out of 10.

Prompt Coach doesn’t just flash a bad grade; it explains why. Under “Be clear about what you want” it scores the prompt 4/10 and points out everything missing: What is the seminar about? When and where does it happen? What goes on the page? What should the copy say to get people to actually sign up?

Another principle, “Show what you’re looking for,” gets an even harsher 3/10. The tool calls out the total lack of examples: no reference sites, no design direction, no vibe. It pushes you to decide whether you want “simple and clean,” “colorful and bold,” “professional,” or “fun” before the model writes a single line of HTML.

The feedback doesn’t stop at criticism. Prompt Coach suggests concrete next moves: share a link to a landing page you like or describe a style such as “Apple’s website — clean and simple,” or “bright colors with big buttons.” That tiny nudge turns a foggy idea into a brief an actual designer—or model—can execute.

Scroll down and the real magic shows up under “Try this prompt instead.” The tool rewrites your vague request into a structured template, with placeholders where your missing details should go. It might look like: “Create a responsive landing page for a seminar about [TOPIC] happening on [DATE] in [LOCATION] targeting [AUDIENCE].”

The upgraded prompt continues with explicit content and layout requirements: hero section with a headline and subheadline, schedule overview, speaker bios, FAQ, and a signup form wired for email validation. It bakes in style cues too: “Use a design similar to [REFERENCE SITE], focusing on [STYLE TRAITS] like minimal layout, large typography, and high contrast CTA buttons.”

You go from a five-word wish to a multi-line spec that any modern model—GPT-4o, Claude 3.5 Sonnet, whatever—can follow almost mechanically. No guesswork, no “Is this what you meant?” loop after 10 minutes of autonomous thrashing.

Those extra 60 seconds up front replace half an hour of retries, rewrites, and quiet doubt about whether your code base rests on a hidden landmine. Specificity isn’t polish; it’s insurance.

Mastering the Prompting Pro-Moves

Advanced prompting starts where “be more specific” ends. Once your instructions hit Robin Ebers’ 15 principles, you unlock a second layer: techniques that shape how models like GPT-4o and Claude 3.5 Sonnet actually think, not just what they output.

First up is Chain-of-Thought prompting. When you tell the model “think step-by-step” or “show your reasoning before the final answer,” accuracy on complex tasks—multi-file refactors, tricky auth flows, gnarly data migrations—jumps dramatically. OpenAI and Anthropic both show that explicit reasoning cuts error rates on hard problems, especially where a single silent mistake can poison an entire codebase.

You can push this further with structured reasoning scaffolds. Instead of a vague “explain,” force stages: “1) restate the goal, 2) list constraints, 3) propose 2–3 options, 4) pick one and justify, 5) output the code.” That template turns a one-shot guess into a mini design review baked into every response.

Next is few-shot prompting: give 3–5 concrete input/output pairs to define style, format, and depth. For a code review bot, you might show examples that always include: - A short summary - A numbered list of issues - Concrete code suggestions

Once those examples sit above your real request, the model snaps to that pattern. You get consistent comment tone, stable markdown structure, and fewer “surprise” interpretations when you plug the system into CI.

Structure around those techniques matters. Research-backed best practice says: lead with a role like “You are a senior TypeScript engineer and security reviewer,” then separate sections with clear delimiters such as `### CONTEXT`, `### CODE`, `### TASK`, wrapped in `"""` or ``` fences. Delimiters fence off instructions from payloads so the model does not hallucinate where your prompt ends and user data begins.

If you want to go deeper than Robin’s video and the 15 principles, resources like **The Ultimate Guide to Prompt Engineering in 2025 - Lakera** catalog these patterns, plus newer tricks like tool-aware prompting and retrieval-augmented examples. Combined with Prompt Coach, those pro-moves turn “hope this works” prompts into reproducible systems.

Your New Pre-Flight Checklist for Prompts

Your prompts now need a pre-flight check as seriously as your code does. Models like GPT-4o and Claude 3.5 Sonnet will happily burn 10–30 minutes and a chunk of your subscription on a vague wish, then hand you code that only looks correct. Treat prompting as an engineering artifact, not a throwaway chat message.

Start with step one: Define the goal and context. Spell out what you’re doing, why, and for whom. “Refactor this for performance” becomes “Refactor this Next.js API route to handle 10x traffic, keep response times under 200ms, and preserve existing TypeScript types.”

Next, specify format and tech stack. Models guess badly when you skip this. Tell it exactly what to output and where it lives:

• Tech: “Next.js 14, App Router, TypeScript, Tailwind, Supabase”
• Format: “Return a single React component,” “Only SQL,” or “Diff-style patch”
• Constraints: file paths, frameworks, libraries, and coding standards

Then provide an example. Few-shot prompting still punches above its weight. Paste a “good” component, API handler, or test file and say, “Match this structure, naming, and comment style,” or link to a public repo and describe what to mirror.

Layer on a role or persona so the model optimizes for the right trade-offs. “You are a senior full-stack engineer optimizing for security and long-term maintainability” yields different decisions than “You are a scrappy prototyper optimizing for speed.” Use this to bias toward tests, docs, or performance.

Before you hit enter, run the draft through a checker like Prompt Coach. Robin Ebers’ tool scores your prompt against 15 principles distilled from OpenAI and Anthropic research, then shows exactly why your “8/10 in your head” is a 4.8/10 in reality—and how to fix it.

Intentional, structured prompting has crossed the line from party trick to baseline literacy for AI development. Your next move: grab your last “build me a thing” prompt, run it through Prompt Coach, ship the improved version, and share how far your score—and your output—jumped.

Frequently Asked Questions

What is the most common reason AI prompts fail?

According to an analysis of over 2,200 prompts, 75% fail because they are not clear or specific enough. Users often write vague 'wishes' instead of detailed instructions.

How do bad prompts waste money with newer AI models?

New autonomous AI models can work for minutes or hours on a single prompt. An unclear prompt causes the AI to waste expensive compute time trying to interpret your request, burning through your subscription budget without producing useful results.

What is a 'silent failure' in AI prompting?

A silent failure is when an AI produces code that appears to work correctly but is built on a flawed assumption due to a vague prompt. This creates a 'landmine' of technical debt that can take weeks to fix later.

How can I instantly improve my AI prompts?

Be hyper-specific. Instead of 'build a portfolio,' define the technology (Next.js), pages (single-page), features (dark mode, email validation), and components (Shadcn) to give the AI less room for misinterpretation.