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Expert roadmaps, tutorials, salary guides and interview prep across Data Science, Generative AI, Python, Cloud & Analytics — written by GloryTecks trainers in Hyderabad.
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How Transformers Changed AI: Attention Explained
Attention lets every word in a sentence draw information from every other word in one step. That idea, from a 2017 paper, replaced the older sequence models and underlies current language models. This guide works the arithmetic by hand, then shows a real attention head in GPT-2 linking a pronoun to its noun.
Building AI Agents with LangGraph: A Hands-On Tutorial
Agent tutorials stop when the demo works. Production agents fail in other ways: tools time out, loops never end, and behaviour changes silently. This hands-on guide builds a small LangGraph agent and makes it reliable, with a retry policy on its tool node, a recursion limit and deterministic tests.
Building a Multi-Agent System: Patterns and Pitfalls
Splitting work across several cooperating AI agents is appealing and often disappointing. This guide sets out the main multi-agent patterns, what published experience says about when they beat a single agent, a small model of how errors compound across hand-offs, and design rules that follow from it.
Evaluating LLM Outputs: Metrics, Tools and Best Practices
Every change to a prompt or model needs evidence that it helped. This guide explains the main ways to evaluate LLM outputs, then checks a small local LLM judge against expert human verdicts from MT-Bench and finds it agreeing often, flipping often when answers swap places, and trustworthy only when consistent.
Prompt Engineering: Techniques That Actually Improve Output
Most prompt advice is a list of tips. This guide takes one job, pulling three fields out of customer emails, and rewrites the prompt seven times, scoring each version on the same twelve emails. An example and a schema fixed the format; definitions and asking for evidence before the verdict fixed the meaning.
Agentic AI Roadmap 2026: From Prompts to Autonomous Systems
Agentic AI is the most hyped corner of generative AI and the easiest to learn in the wrong order. This roadmap builds it in seven stages, from reliable prompts and structured output through retrieval and tools to workflows, agents and production safeguards, with something to build at each stage.