The Developer's Guide to LLM Internals

Master the technical concepts behind language models and how to leverage them

Beneath the surface of every ChatGPT response and AI assistant lies a sophisticated architecture that few truly understand. Sure, anyone can prompt an LLM. But only those who understand the underlying mechanisms—tokenization, embedding spaces, transformer attention, and retrieval augmentation—can push these technologies beyond their basic limitations.

This course deep dives into these concepts transforming theoretical concepts into practical knowledge you can implement immediately.

From the mathematics of vector embeddings to the intricacies of context handling, you'll master the concepts that top AI companies screen for in technical interviews. Each module builds your expertise progressively, moving from basic model concepts to advanced RAG implementations with vector databases.

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    LLM Concepts Deep Dive

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Included in the All Access Pass

  • $50.00

    From Java to AI: The Python-Free Guide to Large Language Models

    For a more from-the-basics introduction to LLM concepts, check out this course. This is an optional prerequisite for this one, but not strictly required
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Course curriculum

    1. Understanding the Concept of Model

      FREE PREVIEW

    2. Language Model Tasks and Auto Encoding

    3. Auto Regression and Text Prediction

    4. Text completion

    5. Audience Questions

    1. Pre-training

    2. Instruct tuning

    3. Fine Tuning

    4. Audience Questions

    5. Introduction to Fine-Tuning AI Models

    1. Introduction to Tokens and Embeddings

    2. Tokenization Explained

    3. Visualizing Tokenization

    4. How token boundaries are formed

    5. How word frequencies are identified

    6. Embeddings and Their Importance

    7. Exploring Embeddings and the N-dimensional space

    8. Embedding Math. Mind-Blowing Examples

    9. Tokenization and Embeddings

    10. Audience Questions

    1. Quiz Time: Test Your Knowledge

    2. The Concept of Text Similarity

    3. Audience Questions

    1. From Tokens To Text

    2. Introduction to Transformer Architecture

    3. Understanding Attention in LLMs

    4. Addressing Questions on Transformer Architecture

    1. Introduction to Context Length in LLMs

    2. Challenges with Context Limit and Statelessness

About this course

  • $50.00
  • 36 lessons
  • 3 hours of video content

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