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#38 Back to Basics — RAG, Transformers, ML Optimization, and LLM Evaluation.
Artificial Intelligence   Latest   Machine Learning

#38 Back to Basics — RAG, Transformers, ML Optimization, and LLM Evaluation.

Towards AI Editorial Team
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August 29, 2024
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Last Updated on September 2, 2024 by Editorial Team

Author(s): Towards AI Editorial Team

Originally published on Towards AI.

Good morning, AI enthusiasts! This week, the community and I are answering some recurring questions about RAG, coding assistants, transformers, machine learning, and more. You will also find fun collaboration opportunities and memes.

Enjoy the read!

What’s AI Weekly

Many clients asked us (Towards AI), “But why would I use RAG if Gemini can process millions of tokens as input?” So, is RAG dead? That’s what I investigated in this week’s iteration of What’s AI. I explore the differences between RAG and sending all data in the input and explain why we believe RAG will remain relevant for the foreseeable future. This post should help you determine whether RAG is suitable for your application. Read the complete issue here!

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

This issue is brought to you thanks to GrowthSchool:

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Learn AI Together Community section!

Featured Community post from the Discord

Aman_91095 has been working on the GenAI Career Assistant, built using LangChain and Streamlit, a project designed to experiment with AI-powered job search tools. It helps with the job search process, helps you find job listings that fit your profile, generates cover letters customized for specific applications, and provides useful information about potential employers. Check it out here and support a fellow community member. If you have any feedback or questions, share them in the thread!

AI poll of the week!

The results show a very clear reliance on ChatGPT. Are general-purpose models enough for most use cases? Are specialized models only required for proprietary applications? Let’s discuss this 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. Ananya.exe is looking for a partner to collaborate on a finance-based project (which involves knowledge of multi-AI agents, RAG pipelines, information retrieval, NLP tasks, end-to-end development and deployment, etc.). If you know finance and can work with the above technical specifications, reach out in the thread!

2. Gere030199 is seeking a marketing co-founder for their Discord bot project. They need someone experienced in creating engaging content. If this sounds interesting, connect in the thread!

Meme of the week!

Meme shared by creitingameplays

TAI Curated section

Article of the week

Streamline Your LLM Evaluation: A Step-by-Step Guide to RAG Metrics with Streamlit by Maxime Jabarian

This piece presents a new Streamlit app intended for RAG evaluation. It offers an easy-to-use platform that shows chatbot performance using clear metrics and graphs. By integrating a comprehensive set of evaluation metrics beyond simple accuracy, the app ensures that users can easily understand and interpret the strengths and weaknesses of their LLM models in a clear and visually engaging manner.

Our must-read articles

1. How to use SVM in Power Systems Analysis? by Optimization team

Machine Learning has become a buzzword lately, with recruiters frequently advertising “data scientist” positions when they’re really seeking experts in optimization. This post emphasizes that many machine learning methods are fundamentally based on optimization. In other words, optimization laid the groundwork for the development of machine learning — much like the chicken laying the egg!

2. Attention is all you need: How Transformer Architecture in NLP started by Surya Maddula

This article discusses the evolution of transformer architecture in NLP, starting with the “Attention is all you need” paper. It also explores the problem of contextualized word embeddings and how transformer architecture addresses it by introducing the encoder-decoder model for translation. It also presents a few fine-tuning examples and transformer-based language models.

3. Querying SQL Database Using LLM Agents — Is It a Good Idea? by Sachin Khandewal

This blog explains different ways to query SQL Databases using Groq to access the LLMs. It also explains how to leverage LLM Agents to build an SQL Agent using an advanced DSPy framework and highlights its limitations.

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); } else { console.log( `Cannot replace the keywords in article with id ${articles[x].id}` ); } } } else { console.log('No articles found.'); } } let key; //not part of script, added for (key in keywordsAndLinks) { //key is the object in keywords and links object i.e ds, ml, ai for (let i = 0; i < keywordsAndLinks[key].keywords.length; i++) { //keywordsAndLinks[key].keywords is the array of keywords for key (ds, ml, ai) //keywordsAndLinks[key].keywords[i] is the keyword and keywordsAndLinks[key].link is the link //keyword and link is sent to searchreplace where it is then replaced using regular expression and replace function articleFilter( keywordsAndLinks[key].keywords[i], keywordsAndLinks[key].link ); } } function cleanLinks() { // (making smal functions is for DRY) this function gets the links and only keeps the first 2 and from the rest removes the anchor tag and replaces it with its text function removeLinks(links) { if (links.length > 1) { for (let i = 2; i < links.length; i++) { links[i].outerHTML = links[i].textContent; } } } //arrays which will contain all the achor tags found with the class (ds-link, ml-link, ailink) in each article inserted using search and replace let dslinks; let mllinks; let ailinks; let nllinks; let deslinks; let tdlinks; let iaslinks; let llinks; let pbplinks; let mlclinks; const content = document.querySelectorAll('article'); //all articles content.forEach((c) => { //to skip the articles with specific ids if (!articleIdsToSkip.includes(c.id)) { //getting all the anchor tags in each article one by one dslinks = document.querySelectorAll(`#${c.id} .entry-content a.ds-link`); mllinks = document.querySelectorAll(`#${c.id} .entry-content a.ml-link`); ailinks = document.querySelectorAll(`#${c.id} .entry-content a.ai-link`); nllinks = document.querySelectorAll(`#${c.id} .entry-content a.ntrl-link`); deslinks = document.querySelectorAll(`#${c.id} .entry-content a.des-link`); tdlinks = document.querySelectorAll(`#${c.id} .entry-content a.td-link`); iaslinks = document.querySelectorAll(`#${c.id} .entry-content a.ias-link`); 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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