Retrieval-Augmented Generation (RAG)

Retrieval-Augmented Generation (RAG) is a technique that enhances language models by integrating them with a retrieval mechanism—usually a vector database or search engine. Rather than relying solely on pre-trained knowledge, the model fetches relevant documents or passages and generates responses based on that real-time context.

A typical RAG pipeline includes:

1. Query formulation: User input is converted into a vector or keyword-based query.
2. Document retrieval: The system searches an external knowledge base or document store.
3. Context assembly: Retrieved snippets are passed along with the original prompt to the language model.
4. Response generation: The model synthesizes a grounded, relevant output.

RAG is widely used in:

• Enterprise chatbots and assistants.
• Knowledge management systems.
• AI search tools and customer support.
• Legal, healthcare, and academic AI applications.

Benefits include:

• Improved factual grounding.
• Customization with proprietary data.
• Reduced hallucination and liability risk.
• Context-aware generation.

However, RAG also introduces new attack surfaces:

• Poisoned documents: Feeding manipulated or malicious content into the model.
• Vector retrieval abuse: Triggering biased or misleading results via crafted queries.
• Context leakage: Including sensitive or internal data in retrieved context.
• Prompt injection via retrieved text: If retrieved documents contain manipulative instructions.

How PointGuard AI Addresses This:
PointGuard AI secures RAG systems by inspecting retrieval sources and analyzing how retrieved context influences outputs. It prevents document-level prompt injection, leakage, and hallucination amplification—helping organizations deploy RAG systems that are not only smart, but also safe.

Resources:

Databricks: Augment your LLMs using RAG

AWS: What is RAG (Retrieval-Augmented Generation)?