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#45 Is Prompting a Future-Proof Skill?
Artificial Intelligence   Latest   Machine Learning

#45 Is Prompting a Future-Proof Skill?

Towards AI Editorial Team
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October 17, 2024
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Last Updated on October 19, 2024 by Editorial Team

Author(s): Towards AI Editorial Team

Originally published on Towards AI.

Good morning, AI enthusiasts! Over the past few months, we have discussed the AI engineer’s toolkit for building reliable LLM products multiple times. We believe that combining RAG, prompting, and fine-tuning will be key to developing scalable solutions with generative AI. As LLMs evolve, the implementation of these techniques will also change, but we believe the fundamentals will remain relevant for a long time, and that’s why we wrote ‘Building LLMs for Production’ as a foundational resource for teaching the core principles of building production products with LLMs.

What’s AI Weekly

This week, in my other newsletter, we explore a timely question: Is prompting a skill we need to master or just a temporary necessity? With the evolution of LLMs, we see a shift in how we interact with these models (i.e., from GPT-3 to GPT-4o and now o1). Prompting is still here, but its complexity may be short-lived. Let’s understand how the landscape is changing and what it means for those of us who work (or interact) with AI daily. Read the full article on Substack!

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

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AI poll of the week!

We are really eager to know where you find the most value when it comes to learning AI (related to code, best practices, usage, etc.) Share it with us 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. Gere030199 is looking for a full-stack developer to help build the MVP as a co-founder. You will create oauth2 Google, set up Stripe and PayPal subscriptions, create a streaming section, and help with ideas and strategies. If this sounds interesting, reach out in the thread!

2. Visrix is looking for an audio-video specialist to help with UI/UX design, AWS configurations, and scaling as the platform grows. If you are interested in producing and optimizing audio and video content, connect in the thread!

3. Oppyalex is looking for someone interested in AI agents and using GenAI for software development to build plugins for an AI coding assistant called OppyDev. If you are passionate about software engineering and want to work on an interesting project in your spare time, contact in the thread!

Meme of the week!

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TAI Curated section

Article of the week

Highlighting and Annotating PDFs on UI Using Streamlit for Retrieval-Augmented Generation (RAG) by Anoop Maurya

This article outlines creating an AI-powered document assistant that can extract information from PDF documents using Retrieval-Augmented Generation (RAG) and Streamlit. It discusses the architecture of the system, detailing how RAG enhances the assistant’s ability to retrieve relevant information and generate accurate responses based on user queries. The article provides step-by-step instructions for implementing the solution, including data preprocessing, indexing, and integrating the Streamlit interface for user interaction. Practical examples demonstrate the effectiveness of this approach in transforming static documents into interactive, query-responsive tools, showcasing potential applications in various domains such as education and business.

Our must-read articles

1. RAG: The Power of Text Splitting for Improving Retrieval: A Developer’s Handbook by Md Monsur ali

This article discusses the significance of text splitting in enhancing the performance of Retrieval-Augmented Generation (RAG) systems. It explains how breaking down large text documents into smaller, manageable segments can improve the efficiency and accuracy of information retrieval. It covers various text-splitting techniques and their impact on retrieval performance, providing practical examples and guidelines for developers. By illustrating the benefits of effective text segmentation, the article aims to equip developers with strategies to optimize RAG implementations, ultimately leading to better AI-driven outcomes.

2. Developing A Virtual Psychologist With Gen-AI by Ori Cohen

This article explores the creation of a virtual psychologist using generative AI technologies. It discusses the potential of AI to provide mental health support and therapy through conversational agents. It outlines the design and development process of the virtual psychologist, including integrating natural language processing and machine learning techniques to facilitate empathetic and context-aware interactions. It also addresses ethical considerations, such as privacy and the importance of human oversight in mental health applications. Through practical examples, the article highlights the transformative potential of AI in making psychological support more accessible and personalized.

3. Advancing Genetic Algorithms and Their Applications by Shenggang Li

The article explains the foundational principles of genetic algorithms, including selection, crossover, and mutation processes, and how these concepts evolve with new techniques and optimizations. It highlights real-world applications of GAs in areas such as optimization problems, machine learning, and engineering design. Practical examples and case studies illustrate the effectiveness of genetic algorithms in solving complex problems. It emphasizes the ongoing research to enhance their performance and applicability in modern technology.

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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Upgrade to access all of Medium\./g, ''); // Removes 'This member-only story...' }); //Load ionic icons and cache them if ('localStorage' in window && window['localStorage'] !== null) { const cssLink = 'https://code.ionicframework.com/ionicons/2.0.1/css/ionicons.min.css'; const storedCss = localStorage.getItem('ionicons'); if (storedCss) { loadCSS(storedCss); } else { fetch(cssLink).then(response => response.text()).then(css => { localStorage.setItem('ionicons', css); loadCSS(css); }); } } function loadCSS(css) { const style = document.createElement('style'); style.innerHTML = css; document.head.appendChild(style); } //Remove elements from imported content automatically function removeStrongFromHeadings() { const elements = document.querySelectorAll('h1, h2, h3, h4, h5, h6, span'); elements.forEach(el => { const strongTags = el.querySelectorAll('strong'); strongTags.forEach(strongTag => { while (strongTag.firstChild) { strongTag.parentNode.insertBefore(strongTag.firstChild, strongTag); } 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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