Knowledge Base

Compared with retraining a model, the knowledge base approach does not require annotated data or GPU compute; it only requires uploading documents and configuring a retrieval strategy, so it is low-cost and quick to show results.

The first step in building a knowledge base is data conversion, for example using OCR to recognize the text of images in documents and convert it into searchable text.

The gate can catch missing fields, but it cannot catch that 'the five dimensions were the wrong five to begin with,' nor can it stop the knowledge base from running along a wrong map; erroneous content is saved as if true and still counts when measured with the kernel's own ruler, so quality cannot be scored by the kernel itself.

Luminous's knowledge base supports filling in documents, web pages, Q&A pairs and business details, and the AI Agent answers based on this information.

Automated customer service systems usually run on a 'knowledge base + rules engine' or a lightweight dialogue model, with the design goal of completing high-frequency standardized requests with minimal interaction, tending to use static fields instead of manual verification.

Lovable's knowledge base is a persistence mechanism for system prompts that solidifies project-level information into context, differing from the retrieval-chunk approach of RAG.

The professional approach is to handle policy validity and real-time status separately: policy validity belongs to general rules stored in the knowledge base, while real-time status must be retrieved through tool calls.

Many AI customer service systems only call the knowledge base but do not read real-time data, and therefore cannot answer which step a customer's transaction is stuck at.

Periodically exporting product information as structured files such as CSV and JSON and syncing them to the knowledge base suffers from information delays and cannot guarantee real-time inventory accuracy

Understanding how to design prompts and organize a knowledge base often has greater long-term value than familiarity with the interface operations themselves.

Prompts and the knowledge base are the two core elements that determine the output quality of an agent.

Knowledge bases and persistent memory serve different purposes: knowledge bases (corresponding to data sources) provide reference material for the large model, while persistent memory (corresponding to conversation data) stores records of user–AI interactions and user behavior patterns.

In AI+ industry deployment, the most valuable asset is the local knowledge base, namely business experience, work logs, and problem-solving approaches.

A knowledge base is static content retrieval that answers what is written in a document, whereas long-term memory focuses on what has been done, what has changed, and what knowledge has been accumulated.

For enterprise-level scenarios, knowledge base optimization often improves the practical usability of an agent more than prompt tuning does.

If the knowledge base itself is of poor quality or has insufficient coverage, RAG cannot guarantee output quality.

The knowledge base determines the accuracy and professionalism of an agent's responses

By building a professional knowledge base that integrates an enterprise's internal proprietary data, agents can be equipped with professional capabilities in specific domains.

Key implementation points for building a knowledge base include document preprocessing and vector storage, retrieval strategy optimization (hybrid retrieval, re-ranking), context management and Token optimization, and privacy protection and data security.

语义检索技术通过嵌入模型将Skill描述文本转化为高维向量存入向量数据库，通过余弦相似度等算法检索最匹配的技能