A Simple Guide to Retrieval Augmented Generation

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.

Generative AI with Large Language Models offered by deeplearning.AI in collaboration with AWS is 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 These are some notes from the three week course. You can download the entire set of notes from Gumroad (link in first comment)

Generative AI with Large Language Models offered by deeplearning.AI in collaboration with AWS is 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 These are some notes from the three week course. You can download the entire set of notes from Gumroad (link in first comment)

When it comes to evaluating the performance of Retrieval-Augmented Generation (RAG) systems, it essential to have a solid grasp of several key metrics, which are traditionally used in information retrieval. In this article, share a quick overview of some important metrics that help in better understand how well a RAG system is functioning. Note that these are metrics related to Information Retrieval Tasks. There are other, more commonly used, RAG specific metrics. 1. Mean Reciprocal Rank (MRR): Use MRR when you want to assess how quickly a system can find the first relevant document in a list of results. It’s particularly useful for measuring the rank of the first relevant document, but you have to always keep in mind that it doesn’t account for multiple relevant results, which can be critical in certain applications. 2. Mean Average Precision (MAP): MAP is one of the go-to metrics when you need to combine precision and recall at different levels of retrieval. It’s effective for understanding system performance across multiple queries by averaging the precision at various points in the ranked list. 3. nDCG (Normalized Discounted Cumulative Gain): When evaluating the quality of document ranking, rely on nDCG because it assigns higher scores to relevant documents that appear earlier in the results. This metric is particularly valuable in scenarios where documents have varying levels of relevance, as it prioritises those that are more important. 4. Precision & Recall: These classic metrics, though traditionally used in classification tasks, still offer important insights when evaluating the retrieval component of RAG systems. Precision helps understand how many retrieved documents are relevant, while recall shows how many of the relevant documents were actually retrieved. 5. F1 Score: Turn to the F1-score when you need to balance precision and recall. It provides a single metric that helps evaluate the trade-offs between the two. While it’s more commonly applied in classification contexts, it can also be useful for understanding retrieval performance in RAG systems. Each of these metrics offers a different perspective on the performance of a RAG system, and together, they help build a more comprehensive understanding of its effectiveness. While no single metric captures all aspects of performance, using these metrics in combination guides in optimising and improving RAG systems. --- Interested in learning RAG? Seven chapters of A Simple Guide to Retrieval Augmented Generation are now available at manning.com. Link to buy early access : https://mng.bz/jXJ9 Public Source Code now has over 120 stars : https://lnkd.in/gRVSp7mC ---

When it comes to evaluating the performance of Retrieval-Augmented Generation (RAG) systems, it essential to have a solid grasp of several key metrics, which are traditionally used in information retrieval. In this article, share a quick overview of some important metrics that help in better understand how well a RAG system is functioning. Note that these are metrics related to Information Retrieval Tasks. There are other, more commonly used, RAG specific metrics. 1. Mean Reciprocal Rank (MRR): Use MRR when you want to assess how quickly a system can find the first relevant document in a list of results. It’s particularly useful for measuring the rank of the first relevant document, but you have to always keep in mind that it doesn’t account for multiple relevant results, which can be critical in certain applications. 2. Mean Average Precision (MAP): MAP is one of the go-to metrics when you need to combine precision and recall at different levels of retrieval. It’s effective for understanding system performance across multiple queries by averaging the precision at various points in the ranked list. 3. nDCG (Normalized Discounted Cumulative Gain): When evaluating the quality of document ranking, rely on nDCG because it assigns higher scores to relevant documents that appear earlier in the results. This metric is particularly valuable in scenarios where documents have varying levels of relevance, as it prioritises those that are more important. 4. Precision & Recall: These classic metrics, though traditionally used in classification tasks, still offer important insights when evaluating the retrieval component of RAG systems. Precision helps understand how many retrieved documents are relevant, while recall shows how many of the relevant documents were actually retrieved. 5. F1 Score: Turn to the F1-score when you need to balance precision and recall. It provides a single metric that helps evaluate the trade-offs between the two. While it’s more commonly applied in classification contexts, it can also be useful for understanding retrieval performance in RAG systems. Each of these metrics offers a different perspective on the performance of a RAG system, and together, they help build a more comprehensive understanding of its effectiveness. While no single metric captures all aspects of performance, using these metrics in combination guides in optimising and improving RAG systems. --- Interested in learning RAG? Seven chapters of A Simple Guide to Retrieval Augmented Generation are now available at manning.com. Link to buy early access : https://mng.bz/jXJ9 Public Source Code now has over 120 stars : https://lnkd.in/gRVSp7mC ---

In the rapidly advancing field of Retrieval Augmented Generation (RAG), it can be easy to feel overwhelmed by the sheer volume of technical jargon. To bridge this gap and make RAG more accessible, this comprehensive taxonomy provides a well-organized list of over 200 key terms, breaking down the ecosystem into 8 intuitive categories. Based on A Simple Guide to Retrieval Augmented Generation, this taxonomy offers an accessible entry point for anyone looking to deepen their understanding of RAG, without being bogged down by unnecessary complexity. The terms are grouped into themes that cover the entire landscape of RAG, from core components to applied RAG patterns and the emerging RAGOps stack: ?? RAG Basics – Learn about LLM limitations, knowledge bases, and the principles of retrieval and generation. ?? Core Components – Understand indexing, chunking, metadata, embeddings, and retrieval strategies. ?? Evaluation – Explore key metrics like precision, recall, MRR, and frameworks like RAGAS and ARES. ?? Pipeline Design – Discover the design of na?ve, advanced, and modular RAG systems. ?? Operations Stack – Learn about the layers that power RAG systems, including security, caching, and monitoring. ?? Emerging Patterns – Uncover innovations such as Knowledge Graph-powered RAG, multimodal retrieval, and agentic RAG. ?? Technology Providers – A comprehensive list of service providers offering tools for RAG development and deployment. ?? Applied RAG – Explore use cases, challenges, and real-world applications in content generation, customer support, and more. If you're someone who follows the trends in AI and want to understand Retrieval Augmented Generation, reading this Taxonomy will go a long way. Link to download is in the comments. --- This taxonomy is based on A Simple Guide to Retrieval Augmented Generation. If you like, please get a copy (Link in comments) ---

It is absolutely thrilling to see A Simple Guide to Retrieval Augmented Generation by Abhinav Kimothi featuring in the Manning Publications Co. bestsellers. Thank you all those who've been a part of this journey. If you still haven't gotten your copy, get one now - https://mng.bz/jXJ9