AI Personal Learning
and practical guidance

AI knowledge Page 2

A simple and effective RAG retrieval strategy: sparse + dense hybrid retrieval and re-ranking, and utilizing "cue caching" to generate overall document-relevant context for text chunks - Chief AI Sharing Circle

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...

The big model fine-tuning knowledge points that even a novice can understand - Chief AI Sharing Circle

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 x Milvus: How to Improve RAG Accuracy - Chief AI Sharing Circle

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 Summarizes Simple and Effective Ways to Build Efficient Intelligentsia - Chief AI Sharing Circle

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...

Mostly experts from Anthropic discuss Prompt Engineering - Chief AI Sharing Circle

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: Chain of Thought on Vector Models-Chief AI Sharing Circle

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 Annual RAG List, 100+ RAG Application Strategies - Chief AI Sharing Circle

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 ...

Comparative Review of 6 Open Source AI Web Crawler Frameworks in 2024: Functional Interpretation, Application Scenario Analysis-Chief AI Sharing Circle

Comparative Review of 6 Open Source AI Web Crawler Frameworks in 2024: Functionality Interpretation, Application Scenario Analysis

Before the formal discussion, it is necessary to clarify the concept of AI crawlers (also known as LLM crawlers), which can be roughly divided into two categories: roughly, it can be divided into two categories, one is the regular crawler tool, except that its results are directly used in the context of the LLM, this kind of strictly speaking, and the AI does not have anything to do with the other category ...

Chief AI Sharing Circle

Chief AI Sharing Circle specializes in AI learning, providing comprehensive AI learning content, AI tools and hands-on guidance. Our goal is to help users master AI technology and explore the unlimited potential of AI together through high-quality content and practical experience sharing. Whether you are an AI beginner or a senior expert, this is the ideal place for you to gain knowledge, improve your skills and realize innovation.

Contact Us
en_USEnglish