Weeding out the nonsense and fostering clarity.
We measure the “Dumpiness” of an input by quantifying Noise vs. Effort, Context, and Details. 😅
If you develop AI tools, you’ve probably encountered how surprisingly unproductive or nonsensical some human input can be. These "dumpy" requests waste both time and money! After extensive testing, I realized that filtering such input was no easy task. Large Language Models (LLMs) often provide answers to even the most irrelevant or unclear queries.
-> So, I thought: why not solve this problem with math? And that’s exactly what happened!
The Anti-Dump Algorithm calculates the ADI (Anti-Dump Index). It is specifically designed to evaluate and filter out unproductive or irrelevant input—commonly referred to as "noise"—while prioritizing clarity, effort, and context. This algorithm measures the quality of input based on several key parameters, ensuring only meaningful contributions are processed.
Table of Contents
- Introduction & Core Idea
- Basic Formula: DumpIndex
- Zones & Visual Representation
- Extension: Dumpiness Gradient
- Advanced Factors & Global Anti-Dump Index (ADI)
- Examples & Edge Cases
- Extended Logic
- Applications
- AI Integration & Workflow
- Example Code (Sketch)
- Conclusion
- License & Acknowledgments
- How to use
If you run AI tools, forums, or support systems, you know the problem:
- Many requests are vague ("Help plz urgent asap!!!").
- Essential details (error messages, code snippets, versions) are missing.
To mathematically identify dumpiness (noise in a request), I introduced a scoring system into my applications that calculates the quality of an input. This quality score is called the Anti-Dump Index (ADI), also known as DumpIndex in simpler terms.
- Identify Noise
- Measure Effort and Details
- Evaluate Context
Goal: Reject inputs with high noise and low content or ask the user for improvements, while prioritizing solid contributions.
The simple version of the algorithm is defined by the DumpIndex:
- {Noise}: Proportion of irrelevant words/phrases (e.g., "pls fix," "ASAP," "???").
- {Effort}: Clarity and structure (meaningful keywords, sentences, formatting).
- {Context}: Reference to OS, framework, environment, etc.
- {Details}: Depth of technical information (error messages, code snippets).
Interpretation:
- High DumpIndex (\Rightarrow) High dumpiness, lots of noise, little effort.
- Low DumpIndex (below 0) (\Rightarrow) Good contribution, worth addressing.
- Input: "Pls fix my code, urgent!"
- Noise: 3/4 = 0.75
- Effort: 1
- Context: 0
- Details: 0
- Input: "Error: 'KeyError' in Python. Occurs when accessing a dictionary with missing key."
- Noise: 0 (no filler words)
- Effort: 3 (clear & structured)
- Context: 2 (Python, Dictionary)
- Details: 1 (specific error)
- DumpZone: (\text{DumpIndex} > 1
- GrayArea: 0 ≤ {DumpIndex} ≤ 1
- GeniusZone: DumpIndex} < 0
The values can be graphically represented in a chart to quickly identify whether a request lands in the "Dump Zone" or "Genius Zone."
To understand how sensitive the DumpIndex is to changes in Noise and Effort, we define a Gradient:
This allows on-the-fly evaluation of inputs and shows how much a small increase in Noise worsens the result (or how much Effort saves it).
To incorporate additional aspects—such as bonus and penalty points—we developed the global formula:
Parameters:
- {Noise}\
- {Effort}
- {Context}
- {Details}
- {BonusFactors}: Points for clean structure, accurate terms, code blocks.
- {PenaltyFactors}: Deductions for ALL CAPS, irrelevant jargon, overuse of "!!!" etc.
Weighting Factors: (w_N, w_E, w_C, w_D, w_B, w_P)
- Allow fine-tuning (e.g., support systems vs. general forums).
Interpretation:
- ADI > 1: Reject input, ask for revision.
- 0 ≤ ADI ≤ 1: Medium-priority input.
- ADI < 0: High-quality input, prioritize response.
"Help plssss! My code doesn’t work. Fix it! ASAP!!!"
- Noise = 4/8 = 0.5
- Effort = 0
- Context = 0
- Details = 0
"My Python script throws a KeyError. I don't know how to fix it."
- Noise = 0
- Effort = 1
- Context = 1
- Details = 0
"I get a ‘KeyError’ in Python when accessing a dictionary with a missing key. Here’s the code: …"
- Noise = 0
- Effort = 2
- Context = 2
- Details = 1
- Not every flood of typos should drastically increase the Noise value.
- Above a certain threshold (>10%), it may indicate negligence.
def calculate_typos(self, text: str) -> float:
"""
Berechnet den Anteil von Tippfehlern im Text und passt den Noise-Wert basierend auf der Fehlerquote an.
"""
words = text.split()
total_words = len(words)
# Pseudo-Wörter, die wahrscheinlich Tippfehler sind (z. B. häufige Tippfehler, einfache Wortlisten)
typo_pattern = r'\b[a-zA-Z]{1,2}\b|\b[^\s]+[^a-zA-Z0-9\s]+\b'
typos = len(re.findall(typo_pattern, text))
typo_percentage = typos / max(total_words, 1)
# Wenn die Tippfehlerquote mehr als 10 % beträgt, wird dies als potenziell nachlässig gewertet
return typo_percentage
Identifies inputs that sound fancy but lack substance:
def calculate_profiling_index(self, text: str) -> float:
"""
Berechnet den Profiling-Index, der Inputs bewertet, die auf den ersten Blick kompetent klingen, aber keine substanzielle Information bieten.
"""
# Beispielhafte Pseudo-Terme, die als "fancy" aber wenig substantiell gelten
pseudo_terms = r'\b(optimal|synergy|innovative|disruptive|synergize)\b'
pseudo_matches = len(re.findall(pseudo_terms, text.lower()))
# Profiling-Index ist eine Kombination aus Pseudo-Begriffen, Noise, Effort und Details
noise_value, _ = self.calculate_noise(text)
effort_value = self.calculate_effort(text)
details_value, _ = self.calculate_details(text)
profiling_index = (pseudo_matches + noise_value * effort_value + details_value)
return profiling_index
def calculate_adjusted_noise(self, text: str) -> float:
"""
Berechnet den angepassten Noise-Wert, der Details und die Gesamtzahl der Wörter berücksichtigt.
"""
noise_value, _ = self.calculate_noise(text)
total_words = len(text.split())
details_value, _ = self.calculate_details(text)
adjusted_noise = noise_value * (1 - details_value / max(total_words, 1))
return adjusted_noise
def calculate_anti_dump_score(self, text: str) -> float:
"""
Berechnet den Anti-Dump-Score (inverse), der die Effizienz und Substanz des Textes misst.
"""
effort_value = self.calculate_effort(text)
context_value = self.calculate_context(text)
details_value, _ = self.calculate_details(text)
noise_value = self.calculate_noise(text)[0]
anti_dump_score = (effort_value + context_value + details_value) / (noise_value + 1)
return anti_dump_score
- Support Systems
- Filter poorly formulated tickets.
- Automatic prompt: "Please provide more details."
- Education
- Scan essays: filler words vs. concrete facts.
- HR & Recruitment
- Applications with high Noise scores -> polite rejection.
- Online Forums
- Highlight poor questions, encourage user improvements.
- Preprocessing
- Analyze text, determine metrics
- ADI Calculation
- Use the global formula with weights.
- Decision
- ADI > 1: Reject request.
- 0 \leq ADI \leq 1: Medium priority.
- ADI < 0: Prioritize, high quality.
- Feedback Loop
- User or team feedback adjusts weightings.
(For demonstration only – not production-ready.)
def calculate_adi(noise, effort, context, details, bonus, penalty, weights):
w_N, w_E, w_C, w_D, w_B, w_P = weights
try:
numerator = w_N * noise - (w_E * effort + w_B * bonus)
denominator = w_C * context + w_D * details + w_P * penalty
return numerator / max(denominator, 0.1) # Avoid div/0
except ZeroDivisionError:
return float('inf')
# Example Test
input_text = "Pls fix my code. Urgent!!!"
weights = (1.0, 2.0, 1.5, 1.5, 0.5, 1.0) # (w_N, w_E, w_C, w_D, w_B, w_P)
# Assumed values (for demo):
noise_val = 0.5
effort_val = 0
context_val = 0
details_val = 0
bonus_val = 0
penalty_val = 1.0
adi_value = calculate_adi(
noise_val, effort_val, context_val, details_val,
bonus_val, penalty_val, weights
)
if adi_value > 1:
print("Reject. Please revise.")
elif 0 <= adi_value <= 1:
print("Mediocre input.")
else:
print("Very good input!")The Anti-Dump Algorithm provides a robust mathematical foundation for evaluating input quality, is easily extendable, and can be adapted to various domains:
- DumpIndex or ADI > 1: Low effort, irrelevant information.
- ADI < 0: Well-structured, clearly formulated requests.
This tool saves time and resources while fostering better questions and more constructive discussions. Simply put, ADI can optimize your tools and educate your users with AI technology!
Fun Fact: While not Nobel-worthy, every LLM confronted with this method showed improved interactions and educated users. This is my gift to anyone tired of nonsense—let’s make this a standard! 😅 I’d appreciate a ⭐ or even a ☕ if you find it helpful. 😄
- This document and example code are licensed under a Public license
- Thanks to all dumpy ai-website users! Realy , real thanks stupid dumps! From funfact to real solution!
Stay Dump-Free!
