Intro to Large Language Models

Thursday, Dec. 3, 12:30-3:30 p.m.

Online

This comprehensive, non-coding session provides an architectural walkthrough of Large Language Models (LLMs), bridging the gap between high-level AI concepts and practical model utilization. The session traces the historical evolution of Natural Language Processing (NLP), tracking how early statistical models (N-grams, Bag-of-Words) and sequential deep learning networks transitioned into modern self-attention paradigms. Participants examine the inner workings of the Transformer architecture, exploring tokenization, embeddings, multi-head self-attention, and the structural trade-offs between encoder-only, decoder-only, and encoder-decoder designs, while analyzing how context window limits dictate memory consumption and computational complexity.

Beyond core mechanics, the session evaluates the current LLM ecosystem through model benchmarks, comparing leading families (OpenAI, Anthropic, Google, Meta, and DeepSeek) across parameters like reasoning, cost efficiency, multimodal capabilities, and alignment. Finally, through guided visual demonstrations and concrete examples, participants discover how to access these models via proprietary APIs and open-source platforms (such as Hugging Face), identify key failure modes (hallucinations, bias, prompt injection), and learn some applied prompt engineering patterns to reliably direct model behavior.

Presented by ACENET

Event Listing 2026-12-03 12:30:00 2026-12-03 15:30:00 America/St_Johns Intro to Large Language Models This comprehensive, non-coding session provides an architectural walkthrough of Large Language Models (LLMs), bridging the gap between high-level AI concepts and practical model utilization. The session traces the historical evolution of Natural Language Processing (NLP), tracking how early statistical models (N-grams, Bag-of-Words) and sequential deep learning networks transitioned into modern self-attention paradigms. Participants examine the inner workings of the Transformer architecture, exploring tokenization, embeddings, multi-head self-attention, and the structural trade-offs between encoder-only, decoder-only, and encoder-decoder designs, while analyzing how context window limits dictate memory consumption and computational complexity. Beyond core mechanics, the session evaluates the current LLM ecosystem through model benchmarks, comparing leading families (OpenAI, Anthropic, Google, Meta, and DeepSeek) across parameters like reasoning, cost efficiency, multimodal capabilities, and alignment. Finally, through guided visual demonstrations and concrete examples, participants discover how to access these models via proprietary APIs and open-source platforms (such as Hugging Face), identify key failure modes (hallucinations, bias, prompt injection), and learn some applied prompt engineering patterns to reliably direct model behavior. Register here Online ACENET