Llumo Blog

Agentic AI : The Future of Autonomous Artificial Intelligence and Scientific Discovery

Prompt Engineering Services: A Technical Overview

Why LLM Prompts Are the Future: Let's Engineer Some Change!

Generate better original content with AI: Best AI Prompt

From Models to Agents: Google’s Vision and How LLUMO AI Is Making It Real

From Paper to Algorithm: The Future of Legal Research in 2025

AI Tools- Job Taker or Opportunity Generator?

Prompt and RAG: A Powerful Duo for Maximizing Intelligent LLM Performance

How to Monitor and Optimize AI Model Performance: Essential Tools and Best Practices

LLM evaluation metrics: Track AI performance

Master AI Model Evaluation in Legal-Tech: How LLUMO AI Delivers Reliable, Ethical Results

7 Surprising Ways the ChatGPT Advance Voice Mode Works for You

Why we built LLUMO?

Agent2Agent: The Communication Protocol Uniting Distributed AI Agents for Seamless Collaboration

Unlocking Potential: Supercharge Your Career with LLM model

How Top AI Engineers Debug LLM Hallucination with Structured Logging

Simplified Walkthrough - OpenAI Playground

Driving the Future of Conversational Intelligence: How LLUMO AI Empowers CI Companies to Succeed in 2025

Bypass accuracy issues in ChatGPT - Chat OpenAI Alternative

Overcoming the Roadblocks to Agentic AI: How LLUMO AI Is Powering Autonomous Systems

RAG vs Fine-Tuning: Which Approach Delivers Better Results for LLMs?

The AI Wave is Already Back - Prompt Engineering and Why it's Crucial

Discover 10 Exceptional Use Cases of RAG Systems in Real Life

AI Prompts or ChatML? Leveraging LLMs for Optimal Results

How to Build and Scale custom evaluation metrics for High-Stakes LLM Use Cases

Debugging Agentic Systems: A New Framework for Evaluating and Optimizing Multi-Agent LLM Workflows in Production

Mastering Multi-Agent Orchestration: Moving Beyond Evaluation to True Agent Intelligence with LLUMO AI

Evaluate 360° LLMs Quality

Hallucination 40%
answer_relevancy 59%
contextual_relevancy 52%
factual_correctness 28%
toxicity 21%
bias 40%
Response Coherence 50%
Empathy 46%
Adaptability 34%
Multi-turn Memory 30%
confidence 40%
context 59%
clarity 52%
cost 28%
accuracy 21%