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Full Process of Fine-tuning Large Models It is recommended to strictly follow the above process during fine-tuning and avoid skipping steps, which may lead to ineffective labor. For example, if the dataset is not fully constructed, and it is eventually found that the poor effect of the fine-tuned model is a problem of the quality of the dataset, then the preliminary efforts will be wasted, and the matter...

OlaChat AI Digital Intelligence Assistant 10,000-word in-depth analysis to bring you to the past and present of Text-to-SQL technology. Thesis: Next-Generation Database Interfaces: a Survey of LLM-based Text-to-SQL Generating accurate SQL from natural language problems (text-to-SQL) is a long...

01.Background In RAG application development, the first step is to chunk the document, efficient document chunking can effectively improve the accuracy of the subsequent recall content. Efficient document chunking can effectively improve the accuracy of the subsequent recalled content. How to efficiently chunk is a hot topic of discussion, there are such as fixed-size chunking, random-size chunking, sliding window...

Over the past year, we've worked with teams building Large Language Model (LLM) agents across multiple industries. Consistently, we have found that the most successful implementations did not use complex frameworks or specialized libraries, but rather were built with simple, composable patterns. In this post, we'll share our experience working with customers and since...

AI Summary Overview An in-depth look at AI cue engineering, with a roundtable format in which several experts from Anthropic share their understanding and practical experience of cue engineering from a variety of perspectives, including research, consumer, and enterprise. The article details the definition of cue engineering, its importance, and how...

Scaling Test-Time Compute has become one of the hottest topics in AI circles since OpenAI released the o1 model. Simply put, instead of piling up computational power in the pre-training or post-training phases, it is better to do it in the inference phase (i.e., when the large language model generates the output...

  Looking back to 2024, the big models are changing day by day, and hundreds of intelligent bodies are competing. As an important part of AI applications, RAG is also a "swarm of heroes and lords". At the beginning of the year ModularRAG continued to heat up, GraphRAG shine, open source tools in full swing in the middle of the year, the knowledge graph re-innovation opportunity, the end of the year graphical reasoning ...

In recent years, with the rapid development of Generative AI (GAI) and Large Language Model (LLM), their security and reliability issues have attracted much attention. A recent study has discovered a simple but efficient attack method called Best-of-N jailbreak (BoN for short). By inputting ...

Long Text Vector Modeling The ability to encode ten pages of text into a single vector sounds powerful, but is it really practical? Many people think... Not necessarily. Is it okay to use it directly? Should it be chunked? How to divide the most efficient? This article will take you in-depth discussion of different chunking strategies for long text vector models, analyzing the pros and cons...

  I. The Root Cause of Testing Prompts: LLM is highly sensitive to prompts, and subtle changes in wording can lead to significantly different outputs Untested prompts can produce: Factually incorrect information Irrelevant replies Unnecessary wasted API costs II. Systematic Optimization of Prompts ...

Overview This guide will walk you through creating a simple Retrieval Augmentation Generation (RAG) system using pure Python. We will use an embedding model and a large language model (LLM) to retrieve relevant documents and generate responses based on user queries. https://github.com/adithya-s-k/A...

Introduction Retrieval-enhanced generation (RAG) is a powerful technique that combines the benefits of large language models with the ability to retrieve relevant information from a knowledge base. This approach improves the quality and accuracy of generated responses by basing them on specific retrieved information.a This notebook aims ...

INTRODUCTION BM25 Retrieval Augmented Generation (BM25 RAG) is an advanced technique that combines the BM25 (Best Matching 25) algorithm for information retrieval with a large language model for text generation. By using a validated probabilistic retrieval model, this method improves the accuracy and relevance of the generated responses....

INTRODUCTION Data chunking is a key step in Retrieval Augmented Generation (RAG) systems. It breaks large documents into smaller, manageable pieces for efficient indexing, retrieval, and processing. This README provides an overview of the various chunking methods available in the RAG pipeline. https://github.com/adithya-...

One of the biggest breakthroughs in the field of AI this year should be in the field of programming, AI programming tools like Cursor and v0 dev have not only drastically lowered the threshold of programming for the average person, but also allowed professional programmers to dramatically increase their development efficiency. But all the news we hear is about high school students who can't program,...

Introduction Evaluation is a key component in the development and optimization of Retrieval Augmentation Generation (RAG) systems. Evaluation involves measuring the performance, accuracy, and quality of all aspects of the RAG process, from retrieval effectiveness to the relevance and authenticity of generated responses. Importance of RAG Evaluation An effective RAG system...