Understanding RAG Systems and How They Enhance LLMs
Apr 29, 2024 | Blog Post
Retrieval augmented generation (RAG) is a method that combines and enhances generative artificial intelligence (AI) applications development. It enhances Large Language Models (LLMs) by integrating external information and knowledge in the form of databases, improving performance of language generation tasks and response quality by providing current, acurrate and applicable information.
LLMs are a set of neural networks language models that are trained with a significant amount of text data which enable them to simulate human-like text response on broad range of subjects. Some of the notable examples are GPT-4, GPT-3, BERT, and T5. These LLMs have shown great advancement in their abilities to perform a variety of natural processing tasks which include text generation, question answering, and language understanding.
High level process for implementing a RAG system:
Data preparation
Indexing relevant data
Information retrieval process (ensures generated responses are relevant and accurate, enhancing existing system’s performance)
LLM inference
Key architectural elements of a RAG architecture:
Vector database
MLflow LLM deployment
Modeling Serving
Beginner RAG systems came with several major challenges including low accuracy in retrieving relevant information, incomplete or unclear responses, repetition, hallucinations, and redundancy in responses.
Advanced RAG systems made several changes to drastically improve functions and performance. An example is the fine-grained indexing technique which allows the new system to be very detailed and specific in data retrieval to improve accuracy and reliability as well as the overall quality of text outputs. The use of dynamic techniques with Sentence-Window Retrieval and Auto-Merging Retrieval also enhanced advanced RAG models by expanding to broader contexts while adding multiple input sources which further improve the response.
Additionally, modern RAG systems can be very flexible and adapt to various applications through module substitution or reconfiguration. Specific domain enhancements can further refine the specificity and enhance accuracy of model outputs.They can also be tailored to the specific needs of organizations or government agencies with large proprietary datasets.
Advanced RAG models with hybrid techniques and modular enhancements provide:
Augmented retrieval quality
Optimized data indexing
Model embedding techniques
Post-retrieval processes, and
Cost-effective solution by removing the need for frequent model retraining
Future of RAG and implications include:
Integration of retrieval processes and pipelines across enterprise applications can pose knowledge and feasibility challenges and require innovative solutions.
Data privacy and cybersecurity are major concerns in an increasingly digitalized and interconnected technology environment.
Some notable LLMs are OpenAI’s GPT series of models (e.g., GPT-3.5, GPT-4, used in ChatGPT and Microsoft Copilot), Google’s PaLM and Gemini, xAI’s Grok, Meta’s LLaMA family of models, Antropic’s Claude models, Mistral AI’s models, and Databricks’ DBRX. (Wikipedia)
Key Terms:
RAG/retrieval augmented generation – is a design approach that enhances Large Language Models (LLMs) by integrating external information and knowledge in the form of databases, improving response quality by providing current, accurate and applicable outputs.
LLM/Large language model – is a deep learning model that is pre-trained on vast amounts of data. LLMs are a set of artificial neural networks that consist of an encoder and a decoder transformer with self-attention capabilities. The encode and decoder extract meanings from a sequence of text and understand the relationships between words and phrases in it. (Wikipedia)
AI hallucinations - are incorrect or misleading outputs from AI models. Causes include poor data quality and inefficient data used in training, incorrect/biased assumptions, or biases in the data or algorithms used to train the model.
Guu, K., Lee, K., Tung, Z., Pasupat, P., & Chang, M. W. (July, 2020). Retrieval augmented language model pre-training. JMLR. Volume 119.https://proceedings.mlr.press/v119/guu20a.html?ref=https://githubhelp.com
Additional Reading:
How the use of Retrieval Augmented Generation (RAG) will Benefit Federal Healthcare
How Retrieval-Augmented Generation (RAG) Helps Reduce AI Hallucinations.
Comprehending Retrieval-Augmented Generation: The What and How
