Deepak K

Deepak Kumar is a Project Manager at ScholarEase and Editor for AIML Insights. He writes and edits content on AI, machine learning, data science, statistical analysis, data engineering, and practical technology workflows.

How LLMs Work: Tokens in the context of large language models training

Large Language Models, Blog /
Diagram showing how LLMs work with tokens context and inference

How LLMs Work: Tokens, Context, and Inference Large language models (LLMs) work by turning text into tokens, reading those tokens within a limited context window, and predicting what token should come next. That prediction process is called inference. In simple terms, an LLM does not retrieve meaning the way a person does. It processes patterns […]

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10 Best Prompt Templates for Summarization and Research With Examples

Prompt Engineering, Blog /
Best Prompt templates for summarization and research: Best prompt engineering templates for summarization and research paper summary templates.

Best Prompt Templates for Summarization and Research The best prompt templates for summarization and research help AI turn messy information into something usable. A strong template does not just ask for a summary. It defines the goal, audience, depth, format, and limits. That is what makes the output more reliable for study notes, literature reviews,

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RAG vs Fine-Tuning: Which One Should You Use in AI?

RAG, Blog /
RAG vs fine-tuning comparison diagram for AI systems

RAG vs Fine-Tuning: Which One Should You Use? RAG and fine-tuning solve different AI problems. RAG improves answers by retrieving relevant external information before generation, while fine-tuning changes the model’s behavior through additional training. In simple terms, choose RAG when your system needs access to changing or private knowledge, and choose fine-tuning when you need

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Open Source LLMs vs Closed Models: Key Differences Explained

Blog, Large Language Models /
Open source LLMs vs closed models: open-weight model versus hosted closed API

Open Source LLMs vs Closed Models Open source LLMs and closed models solve different problems. In general, open-weight models give you more control, customization, and deployment flexibility, while closed models usually offer easier access, strong managed infrastructure, and faster path-to-production through hosted APIs. In 2026, that trade-off matters more than ever because both camps are

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What Is a Large Language Model? Explained Simply for Beginners

Large Language Models, Blog /
what is a large language model: LLM pipeline for beginner understanding

What Is a Large Language Model? Explained Simply The field of artificial intelligence is moving forward at an unprecedented pace, driven by software systems that talk, reason, and draft documents like humans. If you are entering this technical space for the first time, securing the official full name and definition of llm in ai ml

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