Add CAG Section

[RandyPatterson]

Context-Augmented Generation (CAG) in AI

🔍 What is CAG?

Context-Augmented Generation (CAG) is a technique used in large language models (LLMs) to enhance the quality, relevance, and accuracy of generated outputs by incorporating external or situational context into the generation process.

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🧠 How It Works

CAG introduces additional context beyond the model’s pre-trained knowledge, such as:

• User-specific data (e.g., preferences, history)
• Domain-specific knowledge (e.g., legal, medical, technical)
• Real-time inputs (e.g., sensor data, current events)
• Structured documents (e.g., manuals, reports, databases)

This context is injected into the prompt or used to guide the generation process, allowing the model to produce more targeted, accurate, and useful responses.

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🆚 CAG vs. RAG (Retrieval-Augmented Generation)

Feature	CAG (Context-Augmented)	RAG (Retrieval-Augmented)
Context Source	Injected directly into the prompt	Retrieved documents used during generation
Use Case	Personalized or situational generation	Fact-based or knowledge-grounded generation
Latency	Lower (no retrieval step)	Higher (requires retrieval phase)
Example	Chatbot using user profile for replies	QA system pulling from a knowledge base

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🧩 Applications of CAG

• Customer support: Tailoring responses based on user history
• Healthcare: Generating summaries using patient records
• Legal tech: Drafting documents with case-specific context

Course Module Ideas

1. Intro to CAG
2. Show some examples like CoPilot or ChatGPT upload of documents
3. Alter the chat application to allow uploads of files to include in the context
4. Allow Documents (use MarkitDown MCP Server), Image (multimodal inpout) and Audo (Transcribe)