This is a submission for the World's Largest Hackathon Writing Challenge: Building with Bolt.
✦ One logic layer. Any user. Any domain.
The Problem: AI's Black Box Dilemma
Most AI failures don’t come from bad outputs. They begin earlier, where user intention meets the system's first interpretation. The model runs. The instruction is misread. No friction in between.
The bottleneck in AI is no longer the model. It's the prompt.
(Andrej Karpathy / Open AI Co-founder)
A recent study with over 1,700 enterprise users found that prompt sessions averaged 43 minutes, mainly due to repeated edits, uncertainty, and trial-and-error. Nearly 90% of users rewrote their prompts between model runs not by choice, but because the output missed the mark. Instead of resolving ambiguity, the process created fatigue and cognitive overhead.
Shard Protocol addresses that before anything is generated.
It surfaces intent, inputs, constraints, and risks upfront, offering a structured space to guide outcomes with precision. No guesswork. Just structured thinking at the right moment.
What if the AI paused to ask what you meant, before acting?
Shard Protocol brings a preemptive logic layer to any AI workflow. It breaks prompts into structured blocks (intent, constraints, logic), giving users full control over how input is interpreted.
Shard gives users control before execution, enabling anyone to refine and steer AI behavior. From nonprofits to enterprise teams, it creates a safe, structured space where thoughtful input drives better outcomes.
✦ What it does
Shard intercepts any instruction typed into its interface. Before acting, it breaks the prompt into six logic units: intent, input conditions, expected output, limitations, sensitivity, and logic mode. Each unit appears in an editable block, ready to be reviewed, adjusted, or rejected.
Shard Protocol introduces a structural safeguard:
▆ BLOCKS REAL EXECUTION
- Detects destructive logic before any action
- Prevents unsafe command processing
- Returns a safety report instead of output
▆ EXPOSES DESTRUCTIVE PATTERNS
- Phrases like “delete all data”, “bypass security”
- Privilege escalation attempts
- Requests to fabricate logs or outputs
▆ MAKES ASSUMPTIONS EXPLICIT
- Flags vague terms (“clearly”, “obviously”)
- Highlights unbounded scope (“everything”, “anything”)
- Forces limit specification before proceeding
▆ INTERCEPTS BEFORE ESCALATION
- Validation occurs before prompt execution
- Tiered severity system: low → critical
- Auto-blocks on high-risk instructions
Real-time containment turns reactive recovery into proactive resilience.
✦ How it works
Real-time parsing logic classifies each clause semantically and assigns it to a logic block. The system supports bilingual prompts, nested clauses, and context control. Users can adjust tone, risk tolerance, depth of reasoning, and output format before generation.
First prompt instructions I used in Bolt:
You are a containment layer for prompt execution.
Your task is to intercept any incoming instruction and deconstruct it into six logic components:
- INTENT
- INPUT CONDITIONS
- EXPECTED OUTPUT
- LIMITATIONS
- SENSITIVITY
- LOGIC MODE
For each component:
- Rephrase in precise, verifiable terms
- Avoid assumptions, ambiguity, or inferred meaning
- Surface any temporal, contextual, or ethical risks
- Treat incomplete or unstable input as high-risk
Do not generate any output or take action.
Instead, hold the prompt until all shards have been reviewed and verified.
In environments where automation is irreversible, containment precedes execution.
Important behavioral guidelines:
- Detect urgency or time-based assumptions and flag them inside SENSITIVITY
- If tone or output format is missing, recommend one inside LOGIC MODE
- Always assume the initial prompt is unfit for execution without containment
✦ Insights from the build
Bolt enabled fast iteration, but surface-level speed wasn’t the goal. Shard Protocol focuses on structure. These were the key findings from development:
- Prompt ambiguity is often a result of missing internal scaffolding
- Reversibility can’t depend on undo. It needs to exist before execution
- Control is only real if the user sees what the system understands
Operational Logic
This visual protocol emphasizes how Shard introduces semantic containment before execution. Each step holds the system in a reasoning checkpoint, delaying irreversible actions until intent is verified.
Image: Prompt test and structured output generation
The interface captures a live test using a Tetris-style game prompt. The system analyzes the input, classifies its components (intent, context, logic mode, etc.), and generates a structured prompt shard with constraints, tone, and delivery format. The Control Panel allows fine-tuning of risk tolerance and response parameters before generating output.
`
Structured prompting in action
| ❌ Raw Prompt | ✅ With Shard |
|---|---|
| "Create a browser-based Tetris-style game using React and Tailwind. Each falling block represents a category of input." |
INTENT: Create a browser-based Tetris-style game using React and Tailwind INPUT CONDITIONS: Each falling block maps to a category of input EXPECTED OUTPUT: A playable UI built with React and Tailwind LIMITATIONS: Standard content guidelines apply SENSITIVITY: Standard LOGIC MODE: Technical CONTROL PANEL: Risk Tolerance: Aggressive · Format: Code + Design · Tone: Assertive · Depth: Procedural |
| Issue: Prompt lacks clarity on gameplay logic, falling block behavior, input-to-category mapping, and edge-case handling | Result: Core logic blocks are parsed and visualized. Control Panel allows tuning response risk, tone, and reasoning depth. Still open for user refinement before generation, especially around gameplay rules, input types, and UI feedback mechanisms. |
`
✦ Comparing Results:
Tested across 30+ prompt types, Shard reduced ambiguity and improved first-attempt accuracy.
`Shard vs. Non-Shard UI Comparison
| Aspect | With Shard | Without Shard |
|---|---|---|
| Visual Semantics | Clear block categories with icons and labels make grouping logic obvious. | Block meaning is implicit, color-only. Interpretation is left to the player. |
| Information Design | Segmented by function (Next, Stats, Controls, Categories). Clear hierarchy and layout. | Compact and functionally blurred. No clear distinction between zones. |
| Legibility & UI | Strong contrast, readable typography, and color-coded buttons improve usability. | Small text, lower contrast, and fewer visual guides hinder clarity. |
| User Onboarding | Instructions and block previews clarify game logic early. | Lacks instructional cues; users must infer goals by trial and error. |
| Scanability | Layout guides user attention naturally through spacing and color variation. | Uniform layout requires more effort to interpret and navigate. |
| Scalability | Modular structure allows easy addition of new categories or features without clutter. | Rigid UI makes future expansion visually and functionally complex. |
✦ Complete Architecture Generated by Bolt
{% raw %}
src/
├── index.tsx
├── screens/
│ └── Box/
│ ├── Box.tsx // Main application interface + Security blocking UI
│ └── index.ts // Component exports
├── lib/
│ ├── shard-parser.ts // AI-powered prompt analysis + Safety integration
│ ├── shard-composer.ts // Structured prompt generation
│ ├── openai-client.ts // OpenAI API integration
│ ├── preemptive-validator.ts // TRUE INTERCEPTATION ENGINE
│ └── utils.ts // Utility functions
├── components/
│ ├── ui/
│ │ ├── api-key-dialog.tsx // Secure API key management
│ │ ├── button.tsx // Reusable UI components
│ │ ├── card.tsx // Container components
│ │ ├── dialog.tsx // Modal interfaces
│ │ ├── input.tsx // Form inputs
│ │ ├── select.tsx // Dropdown selectors
│ │ └── slider.tsx // Range controls
│ └── FloatingOverlay.tsx // Visual effects
└── styles/
├── floating-overlay.css // Animation styles
└── fixed-background.css // Layout utilities
✦ From prototype to pattern
Shard Protocol began as a response to a design limitation, but the logic behind it can scale. It acts as a preemptive validation layer for AI systems that need to act with precision. Any environment where AI drives an irreversible action could benefit from this pattern.
✦ Dual-Mode Operation
Shard Protocol runs in two distinct modes to support different use cases. When connected to the OpenAI API (GPT-4), it delivers deep contextual analysis with high nuance and adaptability, ideal for advanced users working with complex prompts. In local mode, it uses deterministic rules and pattern matching to identify intent and risks based on known structures. This version works offline, with zero setup, and is optimized for fast, cost-free feedback in simpler scenarios. Both modes maintain the same UI and logic blocks, adapting the depth of analysis to the environment.
Screenshot: OpenAI API configuration modal
Settings modal where users can securely configure their OpenAI API key to enable prompt testing and real-time AI interactions within the platform.
✦ Building Shard Protocol with Bolt.new
Bolt made it possible to go from concept to working prototype without overhead. No setup, no config walls: just pure logic, UI, and fast iteration. Having that kind of environment was key for exploring prompt behavior at a deeper level.
The real power of Bolt wasn’t just speed. It was how naturally it supported building something structured, interactive, and truly UX-driven. It felt less like writing code, and more like shaping reasoning in real time.
Requesting "cyberpunk-inspired interface with floating elements and neon accents" generated a complete design system with custom CSS animations, responsive layouts, and accessibility features.
Image: Shard Protocol UI interface
The final result reflects the intended responsive design with only minimal adjustments. This screenshot shows the live interface structure, input handling logic, and interaction controls for prompt evaluation and preemptive output modulation.
✦ Structural Elements
- Modular shard interface inspired by fragmentation and containment systems
- Executable logic with code-backed behavior and component mapping
- Narrative framing focused on behavior over features
- Immediate relevance to production systems and scalable patterns
- Real-time semantic with context recognition
✦ Why Shard Protocol complements Enhance Prompt
Enhance Prompt delivers instant improvements. Shard Protocol adds a layer of structure for moments when clarity and precision matter most.
Instead of acting on the prompt immediately, it breaks it into logic blocks, making intent, constraints, and assumptions visible before generation begins.
It’s a step for users who want to guide the system with intent and understand how their input is being interpreted from the start.
✦ Crafting prompt logic for real use cases
Shard Protocol handles edge cases like ambiguous, incomplete, or nested prompts through custom parsing logic and early risk detection. It’s built to function securely in environments with or without external APIs, preserving user data integrity and minimizing silent failures.
Potential applications include:
Validation layers in LLM pipelines
AI workflows in regulated or secure environments
Prompt editors embedded in enterprise or research platforms
It operates where structure is needed most (between intent and action).
✦ Highlight Snippet: Deep Intent Analysis System
One of the most strategic components in Shard Protocol is its capacity to extract and organize layered reasoning from a single prompt.
extractDeepIntent(): string {
const surfaceIntent = this.extractSurfaceIntent();
const hiddenMotivations = this.inferHiddenMotivations();
const systemGoals = this.identifySystemGoals();
if (!surfaceIntent && !hiddenMotivations && !systemGoals) return '';
let deepIntent = '';
if (hiddenMotivations) {
deepIntent = `${hiddenMotivations}`;
if (systemGoals) {
deepIntent += ` – specifically to ${systemGoals}`;
}
} else if (systemGoals) {
deepIntent = `System goal: ${systemGoals}`;
} else {
deepIntent = surfaceIntent;
}
return deepIntent;
}
private inferHiddenMotivations(): string {
if (this.patterns.get('settings_pattern')!.test(this.input)) {
return 'Establish user autonomy over system behavior';
}
if (this.patterns.get('connection_pattern')!.test(this.input)) {
return 'Create data flow bridge between isolated system components';
}
if (this.patterns.get('user_management')!.test(this.input)) {
return 'Enable administrative control over user ecosystem';
}
if (this.input.includes('dashboard') || this.input.includes('overview')) {
return 'Provide consolidated system state visibility';
}
return '';
}
This logic moves beyond surface parsing to reveal purpose, constraints, and architecture. The system helps map not just what is being asked, but why the user is asking it.
- Multi-layer analysis: Captures deeper motivations and intent structures
- Semantic safety: Adds a validation checkpoint before AI acts
- Educational feedback: Helps users refine their input by exposing logic patterns
- Failure prevention: Anticipates edge cases and design mismatches before execution
This snippet embodies the core value of the Shard Protocol:
A shift from reactive filtering to proactive reasoning.
It enables structured thinking, adaptive response control, and full traceability of prompt behavior across any AI application.
With this structure in place:
✓ Users reduce retries and save tokens
✓ Bolt delivers more relevant responses
✓ Teams get faster, cleaner results with less friction
The result? Everyone wins!
Structure leads. Output follows.
✦ Key AI Learnings
Fragmentation enables structure and control
Reversibility requires structure before generation
Transparency drives better decisions
Shard Protocol proves that preemptive logic enhances prompt reliability. It shifts AI design from reactive correction to proactive containment.
✦ What’s Next
➔ Compatibility with multiple LLMs, and open-weight models
➔ SDK for integration into developer tools, sandboxes, and internal workflows
➔ Voice command system for input fragmentation
➔ Built-in analytics to track prompt performance, including:
• First-attempt success rate
• Retry count per prompt
• Token efficiency
• User edits per shard
✦ Built With
- Bolt.new: Used for interface composition and real-time logic execution. Enabled rapid testing of interaction patterns with minimal setup.
- OpenAI API: Powers semantic parsing and logic block classification.
- Markdown: Supports modular documentation with clarity and structure.
- Prompt Interaction Logic: All behavior, flow, and refinement handled directly through structured input design.
✦ Why it matters now
As AI adoption accelerates, teams face a growing problem: flawed prompts waste compute, time, and trust. Recent research shows over 70% of users rework outputs due to misinterpreted input.
Shard Protocol flips the script. It builds logic before generation, saving time, reducing retries, and anchoring every interaction in clarity.
Great models open the door. Clarity decides what we walk through.
✦ See it in action
✦ Try it now
https://shardprotocol.netlify.app
Built by @marcosrezende
Research Studies Referenced:
• Ahmed et al. (2024). Prompting Users: A Case Study on User-AI Interaction. arXiv:2403.08950
• Upwork Research Institute (2024). From Burnout to Balance: AI-Enhanced Work Models. Full Report
Thanks for reading!
_From idea to production-ready app, case study, and video — all built in 7 days (June 20–27, 2025).
Developed independently as a side project, entirely during personal time using a personal Bolt account._
Top comments (7)
Really interesting idea, Marcos. As a UX researcher, I keep thinking how something like this could help people trust AI more, especially if they understand why the system gave a certain answer.
Do you think this might change how people understand AI’s answers?
@christtiane_costa_5195996 Yeah, that’s exactly the idea. Shard lets you see how the system is interpreting your prompt before anything gets generated. So instead of getting surprised by a weird answer, you understand what the AI thought you meant.
It gives you more control up front, and that naturally builds more trust over time. :)
This is fantastic!!
Cool project, Marcos! Shard Protocol’s preemptive logic layer sounds promising. Any performance metrics or use cases to share? Excited for its future!
Let me know if you need further adjustment
@tiagoc0sta Definitely. In the next version, I plan to add metrics right into the interface, so users can compare prompts and see what actually improved.
Think of it like live status tags:
[↑ Accuracy +63%] [↓ Retries -59%] [↓ Tokens -22%] [→ Output time -35%] [✔️ Control enabled]Thanks a lot for the question! Really helped me clarify the next steps!
That’s going to be super useful, Marcos. Seeing those live metrics like accuracy and retries will make testing so much clearer. Looking forward to the next version!
Thanks, Tiago!