Ruben Hassid Profile picture
Mar 24, 2024 12 tweets 5 min read Read on X
OpenAI shared the most complete library of guides to prompt chatgpt.

47 links of videos & academic papers.

I read them all, and made a top 10.

#1 → prompt engineering Image
Starting with the basics:
→ prompting techniques

All the techniques you need to know:
> zero-shot
> few-shot
> self-consistency sampling
> chain of thoughts (CoT)
> tree of thoughts (ToT)

First, what is the Chain of Thought? Image
#2 → chain of thoughts

It's a prompting technique that forces the LLM to think before giving a good answer.

Start by creating:
→ a step-by-step reasoning process.

Breaking down a problem into bite-size steps is easier for humans... & LLMs.

#3 → tree of thoughts: Image
What is the Tree of Thoughts?

It helps you brainstorm with the LLM.
> it creates a tree-like structure of ideas.
> each idea is a step to solve a problem.

You're the one selecting the right path:
→ the LLM simply provides options.

Now for the most famous video: Image
#4 → Andrej Kharpathy shared this famous Youtube video a year ago about:

> how to build GPT form scratch
> reading & exploring the data
> tokenizations

Here's the link:


And a month ago, he did a new one explaining how to build a tokenizer:
Image
#5 → the tokenizer is a necessary component of an LLMs.

It's like a puzzle maker. It takes a big piece of language & breaks it down into smaller puzzle pieces (token).

I'm fascinated by Andrej Kharpathy teaching us everything for free:
Image
#6 → jailbreak LLMs

1. Find a rule chatgpt needs to follow:
→ never use the word "computer"

But if you ask the right questions:
chatgpt say the forbidden "computer".

Just like "DAN" became famous, it's a reminder any LLM can be jailbreak.

For another prompting technique: Image
#7 → multi-agent debate

You create multiple agents & make them discuss with each other.

→ LLMs debate their answers over a few rounds to arrive at a common answer.

It helps for:
> mathematics.
> reasoning processes
> reducing hallucinations

#8 → reAct + CoT:
The benchmark said combining ReAct & CoT is the best way to prompt LLMs.

ReAct is a fact-driven method:
> ask the LLM to reason & act.

CoT sometimes makes up information that isn't true.

The best approach for answering questions is to combine their strengths. Image
#9 → prompt perfect

OpenAI shared a (paid) tool that helps you rewrite a perfect prompt for you.

All you need to do is:
> write your prompt
> send it
> click on "optimize"

And the chatbot craft a new prompt for you that you can edit & send again.
#10 → Open AI evals
Evals are designed to evaluate LLMs.

It's crucial for anyone working with LLMs.

It helps you understand how updates in model versions can impact your project.

Here's where to find it: github(dot)com/openai/evals

Last thing before you scroll away: Image
I run daily tests on LLMs like chatgpt, gemini & claude everyday to master them.

Check my profile @rubenhssd for more.

If you'd like to support me, a like or a simple RT goes a long way :)

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More from @RubenHssd

Aug 27
BREAKING: New Stanford study tracking 25 million US workers finds AI is systematically eliminating entry-level jobs.

Here are 6 disturbing facts from one of the largest AI employment study ever conducted:

(hint: young workers are getting obliterated) Image
Fact 1: Employment for early-career workers (ages 22-25) has declined substantially in occupations most exposed to AI.

Software developers aged 22-25 saw nearly 20% employment decline since late 2022, while older workers in the same occupations continued to grow. Image
Fact 2: Overall employment continues to grow robustly, but employment growth for young workers has been stagnant since late 2022.

In the highest AI-exposed occupations, young workers declined 6% while older workers in those same occupations grew 9%. Image
Read 12 tweets
Aug 25
For the first time, Google has measured how much energy AI really uses in production.

Spoiler: the gap vs. all previous estimates is huge... 🧵 Image
Despite AI transforming healthcare, education, and research, we've been flying blind on its environmental footprint.

Every estimate was based on lab benchmarks, not real-world production systems serving billions of users.

Google decided to measure what actually happens. Image
The results from measuring Gemini in production:

• 0.24 watt-hours per text prompt
• Equivalent to watching TV for 9 seconds
• 5 drops of water consumed
• 0.03 grams of CO2 emissions

Substantially lower than public estimates. Image
Read 14 tweets
Aug 12
Meta just won the world's biggest brain competition by building an AI that can READ YOUR MIND while you watch movies.

1st place out of 263 teams.

This is the most insane paper I've ever read: 🧵

(hint: mind reading is here)
For context, the Algonauts competition challenged teams to build AI that predicts brain activity from videos.

263 teams competed.

Meta crushed it with the biggest 1st-2nd place gap ever.

Let me break down how: Image
TRIBE (TRImodal Brain Encoder) is the first AI trained to predict brain responses across multiple senses simultaneously.

Most brain studies focus on one thing; vision OR hearing OR language.

TRIBE does all three at once, just like your actual brain. Image
Read 17 tweets
Aug 5
China built a computer with 2 billion neurons mimicking a monkey's brain.

If Moore's Law is still valid, we will have human-level brain computers with 86 billion neurons by 2033.

We are closer to duplicating humans.

Thread Image
China's progress is insane:

2020: Darwin Mouse (120 million neurons)
2025: Darwin Monkey (2 billion neurons)
2027: 4 billion neurons
2030: 16 billion neurons
2033: 86 billion neurons ← Human brain level

China went from mouse to monkey in 5 years. Image
What does a human brain computer actually mean?

Every thought, memory, and decision you make could theoretically be replicated in silicon.

We're talking about artificial consciousness that thinks like you do.
Read 11 tweets
Aug 3
NVIDIA just dropped paper exposing a $57 billion AI industry mistake.

While Big Tech keeps pushing expensive LLMs like ChatGPT & Claude...

Small language models handle 70% of AI agent work at 1/30th the cost.

Here's why this changes everything:

(hint: less is more) Image
→ The $57 billion mistake ↓

The AI industry invested massively in centralized LLM infrastructure in 2024.

But the actual market for LLM API services is only $5.6 billion.

That's a 10x gap between investment and revenue no one wants to admit. Image
→ Most companies are betting everything on one operational model that may be fundamentally flawed.

They assume centralized, generalist LLMs will remain the cornerstone without substantial alterations.

The problem? This assumption is about to get very expensive. Image
Read 19 tweets
Jul 30
BREAKING: Scientists just analyzed 740,000 hours of human speech across YouTube and podcasts.

Turns out, ChatGPT is rewiring how humans speak to each other.

Here's what they discovered:

(hint: the first AI to successfully colonize our brains) Image
This shook me up first:

The changes showed up in SPONTANEOUS conversations, not scripts or prepared thoughts.

Random people chatting on podcasts started using ChatGPT's favorite words without realizing it.

The way scientists proved this was ingenious ↓ Image
They fed thousands of human texts to ChatGPT for "editing" and tracked every single change.

ChatGPT uses certain words up to 300x more than humans naturally would.

300 times. Not 3x or 30x, but three hundred.
Read 16 tweets

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