A Simple Guide to Retrieval Augmented Generation

I am absolutely chuffed to announce that all chapters of A Simple Guide to Retrieval Augmented Generation have finally been released. The book is a foundational guide for individuals looking to explore Retrieval Augmented Generation. This book is for technology professionals who want to get introduced to the concept of Retrieval Augmented Generation and build LLM-based apps. It will prove to be a handy book for beginners as well as experienced professionals. You'll also get an opportunity to code along in python but the book is intended for non-coders as well. From introducing the technique to building RAG pipelines in production, in 9 chapters the book covers the following - Chapter 1?- Large Language Models and the Need for Retrieval Augmented Generation Chapter 2?- RAG systems and their design Chapter 3?- Indexing Pipeline : Creating a knowledge base for RAG based applications Chapter 4 - Generation Pipeline: Real time interaction for contextual responses Chapter 5 - RAG Evaluation : Checking accuracy, relevance and faithfulness Chapter 6 - Evolving RAGOps Stack : Technologies that make RAG possible Chapter 7 - Progression of RAG systems : Naive to Advanced to Modular Chapter 8 - Rag variants: Multimodal, agentic, graph and other rags Chapter 9 - RAG Development Framework & areas of further exploration If you haven't already, please get your copy here - https://mng.bz/jXJ9 If you like to get hands-on, the GitHub repo of the book is public. You can star/clone/fork it here: https://lnkd.in/gRVSp7mC I'm Abhinav, if you have any questions or observations, please let me know your comments.

Generative AI, Large Language Models, Retrieval Augmented Generation, AI Agents are most likely going to be the buzz words in 2025. If you are planning to build contemporary AI applications, it is unlikely that you are going to use traditional search methods. To meet the evolving user needs of accuracy, personalisation, and relevance, modern search and retrieve features need to be guided by AI too. AI-Powered Search by Trey Grainger, Doug Turnbull and Max Irwin, published by Manning Publications Co. is an incredible guide to design and implement modern search systems. Key Highlights - Modern search systems like RAG need to be domain-aware, personalised, contextual, and even conversational. The book addresses these needs by integrating semantic search, knowledge graphs, and foundation models. - The accompanying code repository gives a necessary practical focus to the theory of AI driven search. - The book also dives into techniques for scalable indexing, query processing, and retrieval methods, ensuring systems handle large-scale data effectively. - It combines traditional search algorithms (TF-IDF, BM25) with deep learning approaches like transformer models, dense embeddings, and fine-tuned LLMs, offering a hybrid model for superior performance. - It also provides the necessary details about emerging paradigms like multimodal search, generative search, and agent-based search. If you're a data scientist, a software developer, a business leader or a product manager building search engines, chatbots, or intelligent assistants, this book equips you with the knowledge and code to deliver state-of-the-art solutions. You can get your copy here - https://mng.bz/8OXB Thanks Aira Ducic for introducing me to this wonderful resource.

Based on a survey of Indian VCs by Inc42 Media, 46% Of Indian VCs Are Pushing For Startups To Focus On Retrieval-Augmented Generation (RAG) Solutions One of the quickest ways to learn a concept is to study the common questions and their answers around the subject. This book contains a list of 80 questions divided in 8 groups for a quick, in-depth understanding of the technique. Here are a set of 40 questions on RAG foundations from Around RAG in 80 Questions based on A Simple Guide to Retrieval Augmented Generation Do let me know what you think? Are there any other questions you think are important to gain a foundational understanding of RAG? PS - If you're don't want to spend money and get the entire ebook, do send me a DM.

Based on my book A Simple Guide to Retrieval Augmented Generation (https://lnkd.in/gcDAGucF), I have put together 80 questions that will help in getting a quick understanding of the key concepts of RAG. It has 8 sections based on the first 8 chapters of the book. ??RAG Basics: Questions on limitations of LLMs and the introduction to RAG ??RAG System Design: Questions on the high level design of RAG systems ??Indexing Pipeline: Questions on creating a knowledge brain for RAG systems ??Generation Pipeline: Questions on real-time user interaction in RAG systems ??Evaluation:Questions on the evaluation of RAG systems ??Beyond Naive RAG: Questions on advanced RAG techniques ??Evolving RAGOps Stack: Questions on the technology stack for RAG ??RAG Variants: Questions on RAG patterns like multimodal, graph, agents, etc. Attached are ten questions on Chapter 1 - LLMs and the need for RAG. I call this project Around RAG in 80 Questions ?? In case you want to purchase the complete e-book with all the questions and answers, you can download it from Gumroad - https://lnkd.in/gY6xkjGZ I will continue to share the contents in subsequent posts so if you don't want to download the ebook right away, you can follow me and watch this space for more. (In case, you're looking for a discount, please send me a message) What do you think? What other questions should be a part of this?

Retrieval Augmented Generation, or RAG, stands as a pivotal technique shaping the landscape of applied generative AI. A novel concept introduced by Lewis et. al., in their seminal paper Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks, RAG has swiftly emerged as a cornerstone, enhancing reliability and trustworthiness in the outputs from Large Language Models (LLMs). A few months ago, Ali Ghodsi, co-founder and CEO of Databricks, revealed that their customers are actively embracing RAG, with 60% of their use cases involving LLMs being built upon this architecture. For developers, managers and business leaders, RAG has become as essential a concept to understand as Generative AI and Large Language Models. One of the quickest ways to learn a concept is to study the common questions and their answers around the subject. So, I put together 80 questions that will help in getting a quick understanding of the key concepts of RAG. It has 8 sections - ??RAG Basics: Questions on limitations of LLMs and the introduction to RAG ??RAG System Design: Questions on the high level design of RAG systems ??Indexing Pipeline: Questions on creating a knowledge brain for RAG systems ??Generation Pipeline: Questions on real-time user interaction in RAG systems ??Evaluation:Questions on the evaluation of RAG systems ??Beyond Naive RAG: Questions on advanced RAG techniques ??Evolving RAGOps Stack: Questions on the technology stack for RAG ??RAG Variants: Questions on RAG patterns like multimodal, graph, agents, etc. These questions are based on my book A Simple Guide to Retrieval Augmented Generation. Please consider purchasing a copy - https://lnkd.in/gcDAGucF I am attaching all the questions here. I call this project Around RAG in 80 Questions ?? In case you want to purchase the complete e-book with all the questions and answers, you can download it from Gumroad - https://lnkd.in/gY6xkjGZ I will continue to share the contents in subsequent posts so if you don't want to download the ebook right away, you can follow me and watch this space for more. (In case, you're looking for a discount, please send me a message) What do you think? What other questions should be a part of this?

Top 10 Questions based on the first chapter of A Simple Guide to Retrieval Augmented Generation by Abhinav Kimothi. Let us know your answers in the comments below! Best answers might just win some discounts!

And we've done it for another week! A Simple Guide to Retrieval Augmented Generation features in the Manning Publications Co.'s best sellers yet again! Thanks a lot for all the love and support! If you haven't gotten your copy, please get it here - https://mng.bz/jXJ9

Thank you for making A Simple Guide to Retrieval Augmented Generation by Abhinav Kimothi feature in the Manning Publications Co.'s bestsellers for yet another week. If you still haven't gotten your copy, get one now - https://mng.bz/jXJ9

In Abhinav Kimothi's article, dive deep into the evaluation metrics, frameworks, and benchmarks essential for assessing RAG pipelines. Understanding these tools is key to optimizing your systems for better performance. #LLM #GenAI

About a year ago, I took this course "Generative AI with LLMs" offered by deeplearning[dot]ai in collaboration with AWS. I found it one of the best courses to get an introduction to the LLM technology. The course follows a Generative AI Project lifecycle and introduces concepts right from use cases to model deployment, covering the stages of pre-training, fine-tuning, and alignment using RLHF in depth. It talks about several concepts ? What is an LLM? ? Use Cases for application of LLMs ? What are Transformers? How was text generation done before Transformers? ? How does a Transformer generate Text? ? What is a Prompt? ? Generative AI Project Life Cycle. ? How do you pre-train Large Language Models? ? Challenges with pre-training LLMs. ? What is the optimal configuration for pre-training LLMs? ? When is pre-training useful? ? What is Instruction Fine Tuning? ? What Catastrophic Forgetting? ? How to Evaluate a Fine Tuned model? ? What is Parameter Efficient Fine Tuning? ? Aligning with Human Values ? How does RLHF work? ? How to avoid Reward Hacking? ? Scaling Human Feedback : Self Supervision with Constitutional AI ? How to optimise and deploy LLMs for inferencing? ? Using LLMs in Applications ? LLM Application Architecture ? Responsible AI ? Generative AI Project Lifecycle Cheatsheet I took some notes that I am sharing here. Have you already taken this course? or other courses on LLMs? Which concepts do you find hard to grasp? Do you think this course is still relevant? Do let me know in the comments below.