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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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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.
Few-Shot vs Zero-Shot Prompting: A Practical Guide
Few-shot prompting means showing a model examples before asking it to do the task. The usual advice is that examples help. This guide tests that on 48 real customer messages with a small open model, and finds a case where more examples made results worse and one-line definitions worked better than any of them.
Fine-Tuning vs RAG: Which Should You Choose?
Fine-tuning and RAG are often presented as rivals. They change different things: retrieval gives a model facts to read, and fine-tuning changes how it behaves. This guide shows the difference with a test on a local model, summarises the research and gives an order to try things in.
How Large Language Models (LLMs) Work Under the Hood
A large language model does one thing: given some text, it scores every possible next token. Everything else, from chat to code, is that step repeated. This guide follows the step with real numbers from GPT-2 running on a laptop, then explains training, temperature and why fluent text is not always true.
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.