Knowledge Base

AI evaluation targets are divided into three layers—large language model evaluation, knowledge base (RAG) evaluation, and agent (Agent) evaluation—forming a progressive relationship of increasing complexity and knowledge depth.

The core process of Retrieval-Augmented Generation (RAG) consists of three steps: chunking and vectorizing knowledge base documents for storage, retrieving the most semantically relevant segments, and injecting those segments as context into the LLM prompt.

Semantic retrieval technology converts Skill description text into high-dimensional vectors via an embedding model and stores them in a vector database, retrieving the best-matching skills through algorithms such as cosine similarity.

The knowledge base's data pipeline runs periodically: scripts scan specified sources, the model analyzes, extracts, and transforms the raw documents, the cleaned knowledge enters vector storage, and upper layers retrieve it on demand.

The knowledge base provides the model with reliable context and high-quality example code, and the sandbox provides an isolated environment to check whether the generated code runs and passes unit tests.

In Coze, plugins are the interface for calling external services within a workflow, and knowledge bases are used to provide the model with domain-private retrieval-augmented information.

The first part of the course breaks down the five core modules of an Agent: prompts, tool calling, memory and context management, knowledge base, and multi-Agent collaboration.

Exposing the knowledge base via the MCP protocol decouples knowledge updates from agent logic: after the code changes, it is enough to re-extract, with no need to modify the Agent.

Forcing in a knowledge base for small tasks may be pure over-engineering.

The effectiveness of a knowledge base depends on three factors: whether the document chunking is reasonable, whether retrieval hits the correct passages, and whether the model faithfully uses the retrieved content.

Knowledge base sources supported by the system include: website crawling (sitemap), PDF/DOCX/TXT/MD documents, image OCR, handwritten FAQs, internal team repositories and audio transcription text.

Coze's knowledge base targets unstructured text and performs semantic matching through vectorized retrieval; the database is structured table storage that supports create, read, update and delete.

Language-specific knowledge requires product recommendations and descriptions to match the language of the request and the store view; the knowledge base should be organized along the language dimension rather than simply sharing one set of content.

AI only works on good source material; without a reviewed knowledge base, you get neither good AI nor good outsourcing.

When outsourcing, three things — the knowledge base, exception handling, and insights — should always stay in-house.

Bad answers blamed on the model being inadequate mostly trace back to the documents themselves after investigation: outdated versions, duplicated content, and multiple files conflicting with each other.

It is recommended to extract key design decisions from comments and place them in independent documents or knowledge bases, so that AI can find them through clearer retrieval paths.

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.