The Most Important AI Engineer Skill 🔥 | AI Evals Explained தமிழில்
AI Coach John (Tamil)
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The Most Important AI Engineer Skill 🔥 | AI Evals Explained தமிழில்
8 139 просмотров · 4 дня назад
AI Coach John (Tamil)
182 тыс. подписчиков
8 139 просмотров · 4 дня назад
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🔥 LLM Evals Explained in Tamil | How to Test & Improve AI Systems in 2026 🤯
Building an AI application is not just about connecting an LLM and getting a response. The real challenge is: *How do you know whether your AI system is actually working correctly?*
As AI applications evolve from simple LLM chatbots to **RAG systems, AI Agents, and multi-step agentic workflows**, evaluating their quality, reliability, accuracy, and behavior is becoming one of the most important skills for AI Engineers.
In this Tamil explanation, we'll break down what *LLM Evaluation (LLM Evals)* means, why traditional software testing is not enough for AI systems, how AI responses can be evaluated, and how Evals fit into modern AI engineering workflows.
🚀 In this video, you'll learn:
• What are LLM Evals?
• Why evaluating LLM applications is difficult
• Traditional Software Testing vs LLM Evaluation
• How to measure the quality of AI responses
• Accuracy, relevance, faithfulness & correctness explained
• What is a good evaluation dataset?
• Reference-based vs reference-free evaluation
• LLM-as-a-Judge explained
• How to evaluate RAG applications
• How to evaluate AI Agents and tool-calling workflows
• Evaluating reasoning and multi-step AI workflows
• Human Evaluation vs Automated Evaluation
• Golden datasets and test cases
• Regression testing for LLM applications
• How Evals help improve AI reliability
• The role of observability and tracing in AI evaluation
• How LangSmith and other evaluation tools fit into the workflow
• Why AI Evals are becoming an important AI Engineer skill in 2026
• How Evals can become part of the AI development lifecycle
• What AI Engineers should learn to build reliable production AI systems
🔥 The key idea:
An AI application that works once is not necessarily a reliable AI application.
Build → Evaluate → Trace → Improve → Repeat.
As AI systems become more autonomous, **testing the AI itself becomes just as important as building the AI.
Whether you're learning LLMs, RAG, LangChain, LangGraph, AI Agents, or Agentic AI, understanding Evals can help you move from simply building AI demos to building **reliable production-ready AI systems.
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