Tag: RAG implementation

Citation and Attribution in RAG Outputs: How to Build Trustworthy LLM Responses

by Phillip Ramos

Citations in RAG systems turn AI guesses into verifiable facts. Learn how to implement accurate, trustworthy citations using real-world frameworks, data prep tips, and the latest 2025 research to meet regulatory demands and build user trust.

Recent-posts

Combining Pruning and Quantization for Maximum LLM Speedups

Mar, 3 2026

Secure Prompting for Vibe Coding: How to Ask for Safer Code

Oct, 2 2025

Compressed LLM Evaluation: Essential Protocols for 2026

Feb, 5 2026

Prompt Length vs Output Quality: Why Shorter Prompts Often Win in LLMs

May, 3 2026

Token Probability Calibration in Large Language Models: How to Fix Overconfidence in AI Responses

Jan, 16 2026