IT Career Guides & Free Resources
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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Top Generative AI Projects to Build for Your Portfolio
Most generative AI portfolios are chatbot demos that look alike. This guide proposes eight projects, from a structured extractor to an agent over a database, each built on a public dataset with a clear licence and each with a measurement to report, so the portfolio shows engineering rather than enthusiasm.
Prompt Injection and LLM Security: What You Need to Know
Prompt injection is the top risk in the OWASP Top 10 for LLM applications, and there is no known way to prevent it outright. What direct and indirect injection look like, why RAG and agents widen the attack surface, and the design controls that limit the damage.
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.
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.
Tokenization in LLMs: Why It Matters More Than You Think
Models do not read words. They read tokens, and tokens decide what a prompt costs, how much fits in the context window and why a model stumbles on spelling and long numbers. This guide explains subword tokenization and measures it: one sentence is 8 tokens in English and up to 99 in Telugu.
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.