AI Lesson 96 – Introduction to LLMs | Dataplexa

AI Course

I. AI Foundations
1. Introduction to AI 2. History of AI 3. AI vs ML vs DL 4. Search Algorithms 5. Intelligent Agents 6. Informed & Uninformed Search 7. Constraint Satisfaction 8. Adversarial Search 9. Logic in AI 10. Planning in AI 11. Knowledge Representation 12. Probabilistic AI 13. Bayesian Networks 14. Markov Models 15. AI Ethics 16. AI Applications 17. Intro to PyTorch 18. Intro to TensorFlow 19. AI Workflows 20. Data for AI
II. Machine Learning
21. Introduction to ML 22. ML Workflow 23. Supervised Learning 24. Unsupervised Learning 25. Reinforcement Learning 26. Linear Regression 27. Logistic Regression 28. SVM 29. Decision Trees 30. Random Forest 31. Gradient Boosting 32. XGBoost 33. K-Means 34. Hierarchical Clustering 35. Dimensionality Reduction 36. Feature Engineering 37. Feature Selection 38. Model Evaluation 39. Overfitting vs Underfitting 40. Cross Validation
III. Deep Learning
41. Intro to Deep Learning 42. Neural Networks 43. Activation Functions 44. Backpropagation 45. Loss & Optimizers 46. Regularization 47. CNN Basics 48. CNN Architectures 49. RNN, LSTM, GRU 50. Seq2Seq 51. Transformers 52. Attention 53. Positional Encoding 54. Training DL 55. Hyperparameter Tuning 56. Transfer Learning 57. Autoencoders 58. GAN Basics 59. GAN Applications 60. Model Serving
IV. Natural Language Processing
61. Intro to NLP 62. Text Processing 63. Tokenization 64. Stopwords & Lemmatization 65. BoW & TF-IDF 66. Word Embeddings 67. Sentence Embeddings 68. NLP Classification 69. Sequence Modeling 70. RNN in NLP 71. Transformers in NLP 72. BERT Basics 73. BERT Fine-tuning 74. RoBERTa & DistilBERT 75. Sentiment Analysis 76. NER 77. Machine Translation 78. Text Generation 79. NLP Evaluation 80. NLP Use Cases
V. Computer Vision
81. Intro to CV 82. Image Processing 83. Filters & Edges 84. Convolutions 85. Object Detection 86. Object Tracking 87. Image Segmentation 88. Face Recognition 89. Feature Detection 90. OCR 91. Image Augmentation 92. Generative Vision 93. Vision Transformers 94. Multimodal Models 95. CV Use Cases
VI. LLM Engineering
96. Intro to LLMs 97. Tokenization 98. LLM Architecture 99. Pretraining 100. Fine-tuning 101. RLHF 102. Prompt Engineering 103. Embedding Models 104. Vector Databases 105. LLM Agents 106. Guardrails & Safety 107. AI in Production 108. End-to-End Project

Lesson 96: Introduction to Large Language Models (LLMs)

Large Language Models, commonly called LLMs, are one of the most important breakthroughs in modern Artificial Intelligence. These models power chatbots, code assistants, search engines, and many intelligent applications used daily.

In this lesson, you will understand what LLMs are, how they work at a high level, and why they are different from traditional AI and machine learning models.

What Is a Large Language Model?

A Large Language Model is an AI system trained on massive amounts of text data to understand, generate, and reason using human language.

Unlike rule-based systems, LLMs learn patterns in language such as grammar, meaning, context, and relationships between words.

  • They predict the next word based on previous words
  • They understand context across long text
  • They can generate new, human-like responses

Why Are LLMs Called “Large”?

LLMs are called large because of three main factors:

  • They are trained on billions or trillions of words
  • They contain millions or billions of parameters
  • They require powerful hardware to train and run

The size allows these models to capture complex language patterns that smaller models cannot.

Real-World Examples of LLMs

You already interact with LLM-powered systems more often than you realize.

  • Chatbots and virtual assistants
  • Code generation and debugging tools
  • Search engines with conversational answers
  • Email drafting and summarization tools
  • Content moderation and analysis systems

How LLMs Work (Conceptual View)

At their core, LLMs work by predicting the most likely next word based on previous words. This simple idea scales into powerful language understanding when trained on huge datasets.

For example, given the sentence:

"Artificial intelligence is changing the"

The model predicts what word should come next based on learned patterns.

Simple Token Prediction Example


sentence = ["Artificial", "intelligence", "is", "changing", "the"]

next_word = model.predict(sentence)

print(next_word)

This code represents the fundamental idea behind LLMs. The model looks at previous tokens and predicts the next most probable token.

What Makes LLMs Powerful?

LLMs are powerful because they combine scale, architecture, and training techniques.

  • They understand context instead of isolated words
  • They generalize across tasks without retraining
  • They can reason, summarize, translate, and explain

A single LLM can perform multiple tasks that previously required separate models.

LLMs vs Traditional NLP Models

Before LLMs, NLP systems were built for specific tasks such as sentiment analysis or translation.

  • Traditional NLP models were task-specific
  • LLMs are task-agnostic and adaptable
  • LLMs require prompts instead of fixed pipelines

This shift is what makes LLMs a foundation model for many applications.

Limitations of LLMs

Despite their power, LLMs are not perfect.

  • They can generate incorrect or misleading information
  • They reflect biases present in training data
  • They do not truly understand meaning like humans

Understanding these limitations is important when deploying LLM-based systems.

Practice Questions

Practice 1: What does LLM stand for?



Practice 2: What is the core task LLMs are trained to perform?



Practice 3: What allows LLMs to generate meaningful responses?



Quick Quiz

Quiz 1: What type of data are LLMs primarily trained on?





Quiz 2: What makes LLMs different from older NLP models?





Quiz 3: Why must LLM outputs be reviewed carefully?





Coming up next: Tokenization in LLMs — how text is converted into tokens before models can process it.

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