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#35 Advanced prompting techniques are a myth…it’s all about good communication!
Artificial Intelligence   Latest   Machine Learning

#35 Advanced prompting techniques are a myth…it’s all about good communication!

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August 8, 2024
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Author(s): Towards AI Editorial Team

Originally published on Towards AI.

Good morning, AI enthusiasts! This week, don’t skip to your favorite sections (we know you guys do that); we have some fun bonuses for you, especially if you are in the ‘learning stage’. For the rest, of course, there are great conceptual articles, practical project tutorials, and a handy tool from the community.

What’s AI Weekly

I recently wrote a piece along with two friends for our weekly High Learning Rate newsletter (which you should follow), and since I had many thoughts and opinions on the subject, I decided to share more in the What’s AI newsletter as well. I think, despite all the hype around “advanced” prompting techniques, it’s really just about telling the model what you want in plain language. Read the short opinion piece here!

— Louis-François Bouchard, Towards AI Co-founder & Head of Community

This issue is brought to you by… us!

Building LLMs for Production is currently at 30% off!

Take advantage of the current deal offered by Amazon (depending on location) to get our recent book, “Building LLMs for Production,” with 30% off right now!

Here’s a quote from Jerry Liu, Co-founder and CEO of LlamaIndex, describing the book:

“This is the most comprehensive textbook to date on building LLM applications — all essential topics in an AI Engineer’s toolkit.”

P.S. If you already have it, please consider leaving a review! If you do, reach out to our co-founder Louis (louis@towardsai.net) with a screenshot, and he’ll provide you with free alpha access to our upcoming courses!

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Learn AI Together Community section!

Featured Community post from the Discord

Arwmoffat just released Manifest, a tool that lets you write a Python function and have an LLM execute it. Manifest relies on runtime metadata, such as a function’s name, docstring, arguments, and type hints. It uses this metadata to compose a prompt and sends it to an LLM. The LLM “executes” the prompt and returns a JSON-based format that can be parsed into the appropriate object. Check it out on GitHub and support a fellow community member. If you have feedback or questions, reach out in the thread!

AI poll of the week!

We did the same poll a couple of years ago, and the results show an interesting trend after two years. Check it out in the thread!

Collaboration Opportunities

The Learn AI Together Discord community is flooding with collaboration opportunities. If you are excited to dive into applied AI, want a study partner, or even want to find a partner for your passion project, join the collaboration channel! Keep an eye on this section, too — we share cool opportunities every week!

1. Jbird248 is looking for developers, preferably good with UX/UI, to join an open-sourced AI project. If you are interested in front-end, back-end, or AI, reach out to him in the thread!

2. Ritikashakya is looking to team up with someone interested in contests and eager to learn. Teams can be up to two people, so if you’re curious, connect in the thread!

3. Muhib7486 is new to ML/AI and is looking for a study and/or accountability partner. If you are also a beginner diving into ML, contact him in the thread!

Meme of the week!

Meme shared by ghost_in_the_machine

TAI Curated section

Article of the week

Understanding and Explaining Neural Networks: A Mathematical and Python Implementation Guide by Shenggang Li

This post will simplify the complexities of Neural Networks (NNs) by explaining the steps in model training: creating the model, defining loss functions, and optimizing them with gradient descent. You will learn how NNs use the chain rule and backpropagation for complex loss functions. The author starts with the basics of logistic regression to show how forward (prediction) and backward (training) processes work. Then, moves to a more complex NN with one hidden layer, explaining its forward and backward training processes in detail.

Our must-read articles

1. Lightweight YOLO Detection with Object Tracking from Scratch by Tan Pengshi Alvin

This article aims to achieve both object detection using the YOLO framework and object tracking using a custom framework built entirely from scratch. The author will introduce both the codes and architectures of the models in detail. For simplicity and proof-of-concept, the data applied for the object detection and tracking model will be completely simulated as jiggling multi-colored particles using OpenCV.

2. Optimization of Language Models for Efficient Inference and Performance Using Mixed Architectures by Antonello Sale

Did you know that innovative architecture designs, hardware advancements, and optimization schemes can take language models to the next level? By leveraging hardware acceleration, parallel computing frameworks, and sophisticated inference procedures, we can achieve a delicate balance between speed and accuracy. This article will get into the methodologies and techniques that power these improvements and provide a vision of what lies ahead for high-performance, efficient language models.

3. Feedback Loops in Generative AI: How AI May Shoot Itself in the Foot by Anthony Demeusy

Generative AI can enhance creativity, but beware of feedback loops! They may amplify biases and lead to unintended consequences. Continuous monitoring and ethical guidelines are crucial to ensure responsible AI use. Check this article to know all about feedback loops.

4. How I Build an Agent with Long-Term, Personalized Memory by Gao Dalie

To solve the problem of AI models’ lack of long-term memory and personalization capabilities, the author introduces Mem0. It is suitable for AI applications that require long-term memory and context retention, such as chatbots and smart assistants. This article provides an easy-to-understand explanation of Mem0 overview, what makes Mem0 unique, How Mem0 is different from Rag, and even how to build an actual application.

If you are interested in publishing with Towards AI, check our guidelines and sign up. We will publish your work to our network if it meets our editorial policies and standards.

Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.

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} strongTag.remove(); }); }); } removeStrongFromHeadings(); "use strict"; window.onload = () => { /* //This is an object for each category of subjects and in that there are kewords and link to the keywods let keywordsAndLinks = { //you can add more categories and define their keywords and add a link ds: { keywords: [ //you can add more keywords here they are detected and replaced with achor tag automatically 'data science', 'Data science', 'Data Science', 'data Science', 'DATA SCIENCE', ], //we will replace the linktext with the keyword later on in the code //you can easily change links for each category here //(include class="ml-link" and linktext) link: 'linktext', }, ml: { keywords: [ //Add more keywords 'machine learning', 'Machine learning', 'Machine Learning', 'machine Learning', 'MACHINE LEARNING', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, ai: { keywords: [ 'artificial intelligence', 'Artificial intelligence', 'Artificial Intelligence', 'artificial Intelligence', 'ARTIFICIAL INTELLIGENCE', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, nl: { keywords: [ 'NLP', 'nlp', 'natural language processing', 'Natural Language Processing', 'NATURAL LANGUAGE PROCESSING', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, des: { keywords: [ 'data engineering services', 'Data Engineering Services', 'DATA ENGINEERING SERVICES', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, td: { keywords: [ 'training data', 'Training Data', 'training Data', 'TRAINING DATA', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, ias: { keywords: [ 'image annotation services', 'Image annotation services', 'image Annotation services', 'image annotation Services', 'Image Annotation Services', 'IMAGE ANNOTATION SERVICES', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, l: { keywords: [ 'labeling', 'labelling', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, pbp: { keywords: [ 'previous blog posts', 'previous blog post', 'latest', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, mlc: { keywords: [ 'machine learning course', 'machine learning class', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, }; //Articles to skip let articleIdsToSkip = ['post-2651', 'post-3414', 'post-3540']; //keyword with its related achortag is recieved here along with article id function searchAndReplace(keyword, anchorTag, articleId) { //selects the h3 h4 and p tags that are inside of the article let content = document.querySelector(`#${articleId} .entry-content`); //replaces the "linktext" in achor tag with the keyword that will be searched and replaced let newLink = anchorTag.replace('linktext', keyword); //regular expression to search keyword var re = new RegExp('(' + keyword + ')', 'g'); //this replaces the keywords in h3 h4 and p tags content with achor tag content.innerHTML = content.innerHTML.replace(re, newLink); } function articleFilter(keyword, anchorTag) { //gets all the articles var articles = document.querySelectorAll('article'); //if its zero or less then there are no articles if (articles.length > 0) { for (let x = 0; x < articles.length; x++) { //articles to skip is an array in which there are ids of articles which should not get effected //if the current article's id is also in that array then do not call search and replace with its data if (!articleIdsToSkip.includes(articles[x].id)) { //search and replace is called on articles which should get effected searchAndReplace(keyword, anchorTag, articles[x].id, key); 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mlclinks = document.querySelectorAll(`#${c.id} .entry-content a.mlc-link`); llinks = document.querySelectorAll(`#${c.id} .entry-content a.l-link`); pbplinks = document.querySelectorAll(`#${c.id} .entry-content a.pbp-link`); //sending the anchor tags list of each article one by one to remove extra anchor tags removeLinks(dslinks); removeLinks(mllinks); removeLinks(ailinks); removeLinks(nllinks); removeLinks(deslinks); removeLinks(tdlinks); removeLinks(iaslinks); removeLinks(mlclinks); removeLinks(llinks); removeLinks(pbplinks); } }); } //To remove extra achor tags of each category (ds, ml, ai) and only have 2 of each category per article cleanLinks(); */ //Recommended Articles var ctaLinks = [ /* ' ' + '

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