This series of posts introduces the concept of a "trust boundary" in LLM applications, highlighting that any data crossing into or out of the model is untrusted. It details three primary areas where untrusted input can enter: user input, retrieved content (like in RAG), and model output. The articles provide code examples in Python and Java demonstrating how to defend against prompt injection and other vulnerabilities using a SAFE pattern, which involves clearly delimiting data within the system prompt to distinguish it from instructions. AI
IMPACT Enhances the security and reliability of LLM applications by providing concrete patterns for handling untrusted data.
RANK_REASON The articles provide practical code examples and patterns for securing LLM applications, focusing on input validation and prompt injection defense.
- Agentic Workflows in Java
- Agentic Workflows in Python
- Anthropic
- Building Reliable LLM Applications in Java
- Building Reliable LLM Applications in Python
- claude-opus-4-8
- Java
- Making RAG Accurate in Java
- Making RAG Accurate in Python
- Pii
- Python
- retrieval-augmented generation
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