The Problem We Started With
Many professionals and clients face a common challenge: maintaining professionalism in English writing. Whether it's emails or reports, grammatical errors and tense inconsistencies can undermine the entire impression.
The typical approach? People write "Check my grammar" and paste their text into an AI tool. But this creates several problems:
- AI often completely rewrites the text, losing the original meaning
- No explanation of why errors occurred
- No structured feedback or scoring system
- No learning happens for the user
Our clients wanted a solution that wouldn't just be an "Editor" but would function as a "Teacher"—correcting errors while explaining why they were wrong in the first place.
Why This Is Complex
Basic proofreading tools like spell checkers only catch spelling errors but miss context.
Consider: "Me and my friend is going"—the spelling is correct, but the subject-verb agreement is wrong.
Tone consistency is another challenge. Detecting slang in formal writing or inconsistent active/passive voice usage requires contextual understanding.
Detailed feedback is the hardest part. Most AI tools simply correct text but don't cite established rules from Strunk & White, APA Style, or Chicago Manual to explain the reasoning.
Failed Approaches: What Didn't Work
Attempt 1: Simple Prompting
We tried "Fix this text." The AI corrected it, but users didn't understand what their mistakes were. No learning occurred. Results were inconsistent.
Attempt 2: Personality Prompting
We tried "You are an English Teacher." The AI became overly verbose, giving more advice than actual corrections. Users got frustrated.
Attempt 3: Generic Grammar Bot
The output structure was unpredictable—sometimes giving scores, sometimes not. Sometimes creating tables, sometimes writing paragraphs. No consistency.
The Breakthrough: Grammar Guardian System
From these failures, we realized that simply "fixing" text wasn't enough. We needed structured data analysis.
We designed a Custom GPT called Grammar Guardian with work divided into three specific phases:
Phase 1: Score and Summary
Quick quality assessment on a 1-10 scale that gives users immediate feedback on their writing quality.
Phase 2: Corrected Text
Rewrite that preserves the original meaning and tone while fixing all errors.
Phase 3: Error Breakdown
Detailed explanation of each error with references to established grammar rules.
We strictly defined in the System Instructions: "Always work silently—never ask for clarifications" and "Quote standard style guides like APA/Chicago/Strunk & White."
The Results
Before, users couldn't understand why their writing was changed. Now they receive actionable insights.
Clarity: Instead of unstructured paragraphs, feedback now comes in structured bullet points and clear headings.
Educational Value: Users now understand concepts like "Subject-Pronoun Agreement" or "Dangling Modifiers."
Tone: The bot doesn't just catch errors—it uses the "Sandwich Rule" with an encouraging tone. Example: "Great effort! Here's how to polish this further."
Processing time remained the same, but value addition increased by 100%. Users aren't just getting corrections—they're learning grammar.
Technical Insights: What We Learned
Defined Constraints Create Freedom
Rather than telling AI "do whatever you want," giving it a specific structure (Score → Rewrite → Explain) produces far more powerful results.Citations Build Trust
When the bot says "This violates the rule of parallel structure according to Strunk & White," user confidence skyrockets. Citing rules is far more effective than just saying "This is wrong."Formatting Is User Experience
Using Markdown (bold, headings, bullet points) in output isn't a luxury—it's a necessity. Scannable reports are far more user-friendly than walls of text.Silent Processing Speeds Everything Up
Repeated clarification loops destroy user experience. The "Always work silently" command dramatically increases automation speed.
Implementation Tips for Domain-Specific Bots
If you want to build a bot for a specific domain like code review or legal drafting:
Define Output Structure First
Tell the bot exactly what format the output should take. Be explicit about sections, formatting, and structure.
Set a Clear Persona
Define both expertise level ("Expert in X") and communication style ("Encouraging Tone").
Provide Example Shots
Including an "Example Input" and "Desired Output" in your prompt dramatically reduces AI confusion.
Always Request Reasoning
Don't just ask for solutions—ask why this solution. Extracting the reasoning process adds immense value.
The Core Lesson
The biggest takeaway from the Grammar Guardian project: AI is not just a tool, it's a tutor.
When we combined technical prompting with pedagogical structure, a simple grammar checker transformed into a comprehensive language expert.
The key wasn't making the AI smarter—it was structuring the interaction to maximize learning and clarity.
Your Turn
How much structured output are you ensuring in your AI systems? Have you built any custom GPTs or specialized prompts for specific tasks?
I'd love to hear about your approach to structuring AI interactions.
Try Grammar Guardian: https://chatgpt.com/g/g-67a86f00ce8881918f9d251a166badc9-grammar-guardian
Written by Faraz Farhan
Senior Prompt Engineer and Team Lead at PowerInAI
Building AI automation solutions with structural intelligence
www.powerinai.com
Tags: promptengineering, ai, customgpt, productivity, nlp, automation
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