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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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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.
Ollama Tutorial: Run LLMs Locally on Your Machine
Ollama runs open language models on your own computer with one install. This tutorial walks through the command line, the REST API, conversation history, the OpenAI-compatible endpoint and a custom model built from a Modelfile, all run on an ordinary laptop, with the settings and limits worth knowing.
Synthetic Data Generation with LLMs: A Practical Guide
Language models can produce training examples on demand, which is tempting when real data is scarce. This guide generates customer messages for five banking intents with a local model, compares them with real ones, and trains the same classifier on synthetic, real and mixed data, tested on 200 real messages.
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.
Guardrails for LLMs: Keeping AI Apps Safe and On-Topic
Guardrails are the checks around a language model that keep an application safe and on task. This guide explains input, output and action guardrails, then hides four prompt injections in product reviews and measures how many get past a plain prompt, a delimited prompt and a layered defence.
Open-Source LLMs vs Closed Models: A 2026 Comparison
The choice between an open-weight model and a closed one is now less about raw quality and more about control, licence terms and cost structure. This comparison explains what open actually means for a language model, reads the licences that matter, and sets out when each kind of model is the better fit.