AI Lesson 78 – Text Generation Models | 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 78: Text Generation

Text Generation is one of the most fascinating capabilities of modern Artificial Intelligence. It allows machines to generate human-like text that feels natural, meaningful, and context-aware.

Chatbots, email auto-replies, story writers, code assistants, and content generators all rely on text generation models.

Real-World Connection

Whenever an AI completes your sentence, writes an email draft, answers a question, or generates documentation, text generation is happening in real time.

Platforms like customer support bots, virtual assistants, and AI writing tools heavily depend on text generation models.

What Is Text Generation?

Text generation is the task of predicting and generating the next sequence of words based on given input text. The model learns language structure, grammar, and meaning from large datasets.

Instead of selecting from fixed responses, the model creates new text dynamically.

How Text Generation Models Work

Modern text generation uses transformer-based language models. These models read input text, understand context, and predict the most likely next word repeatedly until the response is complete.

This process is called autoregressive generation.

Simple Text Generation Example

Let’s see how a pretrained language model generates text. 


from transformers import pipeline

generator = pipeline("text-generation", model="gpt2")

prompt = "Artificial Intelligence is changing the way"
result = generator(prompt, max_length=40)

print(result[0]["generated_text"])
Artificial Intelligence is changing the way we work, communicate, and solve complex problems across industries.

Understanding the Output

The model starts with the prompt and predicts one word at a time. Each predicted word becomes part of the input for the next prediction.

This continues until the model reaches the maximum length or a stopping condition.

Controlling Generated Text

Text generation models allow control over creativity and length using parameters like:

  • max_length — maximum number of tokens
  • temperature — randomness of output
  • top_k and top_p — probability filtering

Real-World Use Cases

  • Chatbots and virtual assistants
  • Content writing and summarization
  • Code generation
  • Game dialogues and storytelling

Challenges in Text Generation

  • Hallucinations and incorrect facts
  • Bias in generated content
  • Maintaining long-term context

Responsible use and safety mechanisms are critical when deploying text generation systems.

Practice Questions

Practice 1: What NLP task generates new text based on input?



Practice 2: What is the process of predicting one word at a time called?



Practice 3: Which architecture is commonly used for text generation?



Quick Quiz

Quiz 1: Which parameter controls creativity in text generation?





Quiz 2: What task allows AI to write stories and responses?





Quiz 3: What is a common challenge in text generation?





Coming up next: NLP Evaluation Metrics — how we measure the quality of generated text.

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