4980 related articles

A systematic breakdown of the AI LLM learning roadmap covering prompt engineering, AI Agent development, RAG knowledge bases, model fine-tuning, and hands-on projects for beginners.

A thorough explanation of the essence of AI large language models: from conceptual hierarchy and Transformer mechanics to probabilistic nature, helping test engineers understand LLM strengths and weaknesses.

From word vectors and embeddings to RNNs, BERT, Transformers, and ChatGPT — a complete guide to the technical evolution of large language models and the AI 2.0 era.

LLMs explained through the lens of functions: input is x, output is y, training solves for parameters, inference computes results. Trillion parameters, next-token prediction — no advanced math needed.

Demystify large language models using middle-school math: LLMs are complex functions, training solves for parameters, and inference predicts next-token probabilities.

A beginner-friendly guide clarifying AI, machine learning, deep learning, and LLMs — tracing the evolution from Deep Blue to AlphaGo, ChatGPT, and DeepSeek.

New to AI test development? This article breaks down the differences between machine learning and traditional programming, the origins of AI hallucinations, and the core principles of NLP/NLU/NLG to help test engineers build a solid AI knowledge framework.
GODMODE Project Deep Dive: AI Jailbrea…
GODMODE (G0DM0D3) has 9,300+ GitHub stars fueling debate on AI jailbreaking vs. safety alignment. A deep technical dive into LLM guardrails, prompt injection, and AI security governance.

New to AI? This guide clarifies AI, machine learning, deep learning, and LLMs, traces milestones from Deep Blue to DeepSeek, and maps out China's LLM landscape.

A complete beginner's guide to AI large language models: principles, the Transformer architecture, strengths, weaknesses, and practical tips for testers.

A beginner's guide to AI large models: clarify the relationships between AI, ML, deep learning, and LLMs, trace the journey from Deep Blue to ChatGPT and DeepSeek, and explore China's model landscape.

90% of AI beginners struggle with large language models due to misdirection, poor Prompt logic, and lack of real-world deployment skills. This guide covers the complete learning path from zero to practice.
Can AI Prove Mathematical Conjectures?…
A PDF claiming GPT-5.6 Sol Ultra proved the Cycle Double Cover Conjecture sparked debate on Hacker News. We unpack the truth and the limits of LLMs in math proofs.

IEEE launches an official LLM training course, signaling large language models are entering standardized professional education. What this means for the AI talent gap and your career.

Confused about breaking into AI LLMs? This guide breaks down the two core career tracks — Engineering & Deployment vs. Algorithm Research — covering RAG, Agents, and more.

A systematic AI LLM learning roadmap from scratch, covering Python basics, Prompt Engineering, RAG, Agent development, and enterprise-level projects.

A comprehensive 748-episode AI LLM tutorial covering Transformer architecture, Prompt Engineering, RAG, Agent, fine-tuning, and enterprise projects like AI customer service and knowledge bases.

A systematic three-phase AI LLM career transition roadmap: from Transformer fundamentals to RAG, Agent & LangChain development, to LoRA fine-tuning. Build enterprise-ready skills in two months.

Analysis of a 748-episode, 198-hour AI LLM development tutorial covering API integration, prompt engineering, RAG, AI Agents, fine-tuning, multimodal development, and deployment.

In-depth analysis of Bilibili's 748-episode AI LLM tutorial covering RAG, Agent, and fine-tuning. Includes content structure breakdown and practical study tips for beginners.