AI Personal Learning
and practical guidance
Beanbag Marscode1

AI knowledge Page 5

如何为RAG应用选择最佳Embedding模型-首席AI分享圈

How to choose the best Embedding model for a RAG application

Vector Embedding is the core of current Retrieval Augmented Generation (RAG) applications. They capture semantic information of data objects (e.g., text, images, etc.) and represent them as arrays of numbers. In current generative AI applications, these vector Embedding are usually generated by Embedding models. How to apply for RAG ...

万字长文讲透 RAG 在DB-GPT实际落地场景中的优化-首席AI分享圈

A 10,000-word article on RAG optimization in DB-GPT real-world scenarios.

PREFACE Over the past two years, Retrieval-Augmented Generation (RAG, Retrieval-Augmented Generation) technology has gradually become a core component for enhancing intelligences. By combining the dual capabilities of retrieval and generation, RAG is able to introduce external knowledge, thus providing more applications of large models in complex scenarios...

2025年值得入坑的 AI Agent 五大框架-首席AI分享圈

Top 5 AI Agent Frameworks Worth Getting Into in 2025

Agent The most common translation I've seen so far is "intelligent body", but the direct translation is "agent". What does Agentic translate to? I feel that a word like "agentic" is more appropriate. So in order not to confuse the readers, I use English directly in this article. With the development of LLM, the ability of AI...

朴素、有效的RAG检索策略:稀疏+密集混合检索并重排,并利用“提示缓存”为文本块生成整体文档相关的上下文-首席AI分享圈

Simple, effective RAG retrieval strategy: sparse + dense hybrid search and rearrangement, and use "cue caching" to generate overall document-relevant context for text chunks.

In order for an AI model to be useful in a particular scenario, it usually needs access to background knowledge. For example, a customer support chatbot needs to understand the specific business it serves, while a legal analysis bot needs to have access to a large number of past cases. Developers often use Retrieval-Augmente...

小白也能看懂的大模型微调知识点-首席AI分享圈

Large model fine-tuning knowledge points that even a novice can understand

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...

Late Chunking×Milvus:如何提高RAG准确率-首席AI分享圈

Late Chunking x Milvus: How to Improve RAG Accuracy

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...

Anthropic总结构建高效智能体简单且有效的方法-首席AI分享圈

Anthropic summarizes simple and effective ways to build efficient intelligences

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...

多为来自Anthropic的专家关于Prompt Engineering的讨论-首席AI分享圈

Mostly experts from Anthropic discuss Prompt Engineering

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:向量模型上的思维链-首席AI分享圈

Scaling Test-Time Compute: Chain of Thought on Vector Models

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...

2024年度RAG清单,RAG应用策略100+-首席AI分享圈

2024 RAG Inventory, RAG Application Strategy 100+

  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 ...

卷起来了!长文本向量模型分块策略大比拼-首席AI分享圈

Rolled Up! Long Text Vector Model Chunking Strategies Competition

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...

如何有效测试 LLM 提示词 - 从理论到实践的完整指南-首席AI分享圈

How to Test LLM Cues Effectively - A Complete Guide from Theory to Practice

  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 ...

AI College of Engineering: 1. Tip Engineering

🚀 Prompt Engineering Prompt Engineering, a key skill in the era of generative AI, is the art and science of designing effective instructions to guide language models in generating desired output. As reported by DataCamp, this emerging discipline involves designing and optimizing prompts to generate desired output from AI models (...

AI工程学院:2.1从零开始实现 RAG-首席AI分享圈

AI Engineering Academy: 2.1 Implementing RAG from Scratch

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...

AI工程学院:2.2基本 RAG 实现-首席AI分享圈

AI Engineering Academy: 2.2 Basic RAG Implementation

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 ...

AI工程学院:2.3BM25 RAG (检索增强生成)-首席AI分享圈

AI Engineering Academy: 2.3BM25 RAG (Retrieval Augmentation Generation)

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....

en_USEnglish