Method#Artificial Intelligence#Machine Learning
RAG Implementation
Retrieval-Augmented Generation (RAG) is a method that augments generative models with external retrieval of documents to ground responses in factual knowledge. It combines a retriever and a generator to improve accuracy and context-awareness. This method guides architecture, data pipelines, and evaluation for knowledge-intensive applications.
This block bundles baseline information, context, and relations as a neutral reference in the model.
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Definition · Framing · Trade-offs · Examples
What is this view?
This page provides a neutral starting point with core facts, structure context, and immediate relations—independent of learning or decision paths.
Baseline data
Context
Organizational leveli
Team
Organizational maturityi
Intermediate
Impact areai
Technical
Decision
Decision typei
Architectural
Value stream stagei
Build
Assessment
Complexityi
High
Maturityi
Emerging
Cognitive loadi
High
Context in the model
Structural placement
Where this block lives in the structure.
No structure path available.
Relations
Connected blocks
Directly linked content elements.
Content · Related to
(2)
Dependency · Implements
(1)
Dependency · Requires
(1)