@bigaiguy: SHOCKING: An Indian developer just hit #1 on GitHub with a prompting framework that outperforms every major benchmark. …

🚨SHOCKING: An Indian developer just hit #1 on GitHub with a prompting framework that outperforms every major benchmark.

No VC money. No research lab. Just a laptop and 14 months of testing.

Here are the 11 prompt patterns from his repo that I’ve been using for 3 weeks: https://t.co/nXEpNf1tgn

SHOCKING: An Indian developer just hit #1 on GitHub with a prompting framework that outperforms every major benchmark.

No VC money. No research lab. Just a laptop and 14 months of testing.

Here are the 11 prompt patterns from his repo that I’ve been using for 3 weeks:

Pattern 1: The Anchor Pattern.

Start every complex prompt with a single sentence that defines the exact output format.

“Respond only with a numbered list. No preamble. No explanation after the list.”

The AI locks onto this anchor before processing anything else you write.

Pattern 2: The Constraint Stack.

Don’t add constraints as afterthoughts at the end of your prompt.

Stack them immediately after your core ask.

Ask → Constraints → Context

Reversed order cuts hallucinations by 28% in his testing across 4,000 prompts.

Pattern 3: The Persona Boundary.

Most people assign a role and stop there.

“You are a senior data scientist.”

His version adds a hard boundary:

“You are a senior data scientist. You do not speculate. If data is missing, you say so explicitly.”

The boundary is what activates the persona.

Pattern 4: The Failure Injection.

Before asking for analysis, give the AI an example of the wrong answer.

“Here is the kind of response I do not want: [example].”

This single addition reduced generic outputs by over 50% in his benchmark tests.

Negative examples outperform positive examples. Every time.

Pattern 5: The Confidence Gate.

Add this line to any factual or research task:

“Do not include any claim you cannot support with specific reasoning.”

The AI stops padding answers with plausible-sounding filler.

Output length drops. Output accuracy climbs.

Pattern 6: The Step Separator.

For multi-step tasks, don’t list steps as bullets.

Write them as hard stops:

“Complete step 1. Stop. Wait for my confirmation. Then proceed to step 2.”

Agents that run all steps without stopping drift 60% more than agents that checkpoint.

Pattern 7: The Compression Command.

After any long AI output, run this prompt:

“Compress the above into the 5 sentences that contain the most decision-relevant information.”

You get the signal without the noise.

He used this to process 300 research papers in a single weekend.

Pattern 8: The Assumption Audit.

Before any strategic or analytical task, add:

“List every assumption you are making before you begin.”

The AI surfaces hidden reasoning that would otherwise be buried inside a confident-sounding answer.

This one changes how you read every AI output after you use it.

Pattern 9: The Reframe Test.

After getting any AI recommendation, run this:

“Now argue the opposite position with equal conviction.”

If the AI can argue both sides with equal strength, you don’t have a real answer yet.

It’s the fastest way to find weak analysis.

Pattern 10: The Specificity Ladder.

When outputs feel generic, don’t rewrite your prompt.

Just add:

“Make every claim 3x more specific than your first instinct.”

Generic: “improve your marketing.” Specific: “run a 3-email sequence targeting users who clicked but did not convert in the last 14 days.”

Same prompt. Completely different output.