#prompt engineering
Prompt Engineering: 24 AI articles covering prompt engineering news, analysis, and research
Articles
AI Promises Productivity, But for Managers It's Mostly aβ9
Companies are giving employees AI tools to boost productivity, but managers say evaluating AI-assisted work is harder and more time-consuming than before.
How to Break the AI Coding Agent Fix Loopβ10
Why AI coding agents get stuck in fix loops and a five-step methodβstop, revert, restate, isolate, verifyβto break the cycle and ship working code.
One Bad Prompt Took Down a Company's Salesforce: RSA's Jimβ9
RSA's Jim Taylor explains why AI agents break identity security, how 4,000 shadow agents hide in enterprises, and how RSA Agent ID governs them.
How to Use Lovable Responsibly: A Practical Guide for AIβ9
Learn how to use Lovable responsibly in 2026 with practical tips on security, privacy, testing, and safe AI-assisted app development.
How One Prompt Change Can Ripple Through 50 Others: Building aβ8
Learn how a Python dependency graph distinguishes reachable vs. candidate prompt sets to target retesting after component changes in AI systems.
8 Tips for Writing Effective Agent Instructionsβ10
Master agent instructions with these 8 expert tips for 2026. Learn to design clear, maintainable playbooks that optimize AI performance and workflow efficiency.
How to Get Reliable Structured Data Out of an LLMβ10
Reliable structured data from LLMs requires enforced schemas, validation, and smart retry loops to prevent JSON parsing failures.
Large Language Models in Mental Health: A Systematic Review ofβ9
This systematic review examines LLM applications in mental health, covering detection, therapy support, multimodal methods, and ethical deployment challenges.
Auxiliary Uncertainty Signals for LLM-Assisted Systematic Reviewβ9
Auxiliary BERT+GCN signals improve LLM screening in systematic reviews, with full-context delivery boosting F1 and MAYBE-only routing as the most cost-efficient...
Loop Engineering for RAG: Small Loops Within Steps, Big Loopsβ9
Loop engineering in RAG: detecting failures, adjusting, and retrying steps to turn brittle one-shot pipelines into resilient systems that recover and deliver.