Tag: LLMs

Mar, 24 2026

Prompting as Programming: How Natural Language Became the Interface for LLMs

Emily Fies

Prompting has replaced coding for many tasks, turning natural language into the new programming interface for LLMs. Learn how system prompts, Chain of Thought, and generated knowledge are reshaping how we interact with AI.

Mar, 8 2026

NLP Pipelines vs End-to-End LLMs: When to Use Traditional Processing vs Prompting

Emily Fies

NLP pipelines offer speed and precision for structured tasks, while LLMs excel at complex reasoning. The best approach combines both: use pipelines for preprocessing and LLMs for nuanced understanding. This hybrid model cuts costs, improves accuracy, and meets regulatory needs.

Mar, 7 2026

Real-Time Multimodal Assistants Powered by Large Language Models: What They Can Do Today

Emily Fies

Real-time multimodal assistants use AI to process text, images, audio, and video together in under half a second. They're already improving customer service, healthcare, and education-but they're not perfect yet.

Jan, 23 2026

Why Transformers Power Modern Large Language Models: The Core Concepts You Need

Emily Fies

Transformers revolutionized AI by letting language models understand context instantly. Learn how self-attention, positional encoding, and multi-head attention power today’s top LLMs - and why they’re replacing older models.

Tag: LLMs

Prompting as Programming: How Natural Language Became the Interface for LLMs

NLP Pipelines vs End-to-End LLMs: When to Use Traditional Processing vs Prompting

Real-Time Multimodal Assistants Powered by Large Language Models: What They Can Do Today

Why Transformers Power Modern Large Language Models: The Core Concepts You Need

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Latest Courses

Security Basics for Non-Technical Builders Using Vibe Coding Platforms

Cost Management for Large Language Models: Pricing Models and Token Budgets

Benchmark Transfer After Fine-Tuning: How LLMs Keep Their General Skills When Learning New Tasks

Latency Budgets for Interactive Large Language Model Applications

NLP Pipelines vs End-to-End LLMs: When to Use Traditional Processing vs Prompting

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