LLM Security Red Lines: Prompt Injection Defense in Practice
Prompt Injection is the biggest security risk for LLM applications. This article explains how to defend.
AI Application Testing Strategy: How to Test AI-Generated Code
Testing AI-generated code differs from regular code. This article explains AI code testing methodology.
LLM Fine-tuning to Production: QLoRA and RLHF in Practice
QLoRA + RLHF is the mainstream approach for production LLM fine-tuning. Quick overview of practical workflow.
Fine-tuning LLMs Locally: Ollama + Unsloth in Practice
Open-source fine-tuning tools have matured. Fine-tuning a small model locally with consumer GPU is now feasible. This is practical experience: which tools to use, data preparation, common pitfalls, and when fine-tuning is worth it.
RAG Evaluation Guide: How to Know If Your RAG Is Good
RAG system evaluation is hard. RAGAS, Trulens, LLM-as-Judge—this article introduces practical RAG evaluation methods.
LLM Observability: Practical Methods for Monitoring Prompt and Response
After LLM service goes live, monitoring Prompt/Response patterns is key to finding issues. This article covers LLM observability implementation.