Archive: 2026/09 - Page 3

Deterministic Prompts: How to Reduce LLM Output Variance

Discover how to reduce LLM output variance using deterministic prompts. Learn about temperature, top-p, and chain-of-thought techniques for consistent AI responses.

Instruction Hierarchies for Generative AI: Managing Conflicts Between Prompts and Policies

Discover how instruction hierarchies protect AI from prompt injection by prioritizing system rules over user inputs. Learn about OpenAI's breakthroughs and future trends.

Instruction-Optimized Transformer Variants for Alignment-Ready LLMs

Discover how instruction-optimized transformer variants enhance LLM alignment. Learn about DeMoRecon, DPO, and AlignEZ for better instruction following.

Privacy Controls for RAG: Row-Level Security and Redaction Before LLMs

Learn how to secure RAG systems with row-level security and data redaction. Prevent data leaks by filtering vector searches and masking PII before it reaches the LLM.

AI Code Is Guilty Until Proven Secure: A Policy Framework for Teams

Discover how to implement a 'guilty until proven secure' policy for AI-generated code. Learn to balance AI productivity with robust security controls.