NLP Applications — Generative AI
Generative AI refers to systems that create new content — text, code, images, audio — rather than only classifying or extracting information from existing content. In the context of NLP, generative AI is built directly on the LLMs and Transformer decoder architecture covered earlier in this unit.
How Text Generation Works — Autoregressive Generation
A generative language model produces text one token at a time, each time predicting the probability distribution over the next possible token given everything generated so far — precisely the language modeling objective P(w_i | w_1, ..., w_{i-1}) first defined in Unit 2, just now computed by a Transformer decoder instead of n-gram counts.
Prompt: "Natural Language Processing is"
Step 1: predict next token -> "a" (highest probability continuation)
Step 2: predict next token -> "field"
Step 3: predict next token -> "of"
Step 4: predict next token -> "AI"
...continues until a stop condition is reached...
Result: "Natural Language Processing is a field of AI that enables computers to understand human language."Decoding Strategies — How the "Next Token" Is Actually Chosen
Simply always picking the single highest-probability token (greedy decoding) tends to produce repetitive, bland text. Practical systems use smarter strategies:
| Strategy | How it works |
|---|---|
| Greedy decoding | Always pick the single most probable next token |
| Beam search | Track several candidate sequences in parallel, keep the overall highest-probability one |
| Top-k sampling | Randomly sample from only the k most probable next tokens |
| Top-p (nucleus) sampling | Randomly sample from the smallest set of tokens whose cumulative probability exceeds p |
| Temperature | Controls randomness — low temperature = more deterministic/focused, high temperature = more creative/random |
from transformers import pipeline
generator = pipeline("text-generation", model="gpt2")
output = generator(
"Natural Language Processing is",
max_length=30,
num_return_sequences=1,
temperature=0.7,
top_p=0.9
)
print(output[0]['generated_text'])Generative AI Applications Built on NLP
| Application | What it generates |
|---|---|
| Conversational assistants | Free-form dialogue responses (extends the chatbot pipeline from the previous lesson) |
| Code generation | Source code from natural-language instructions |
| Content writing / copywriting | Blog posts, marketing copy, emails |
| Abstractive summarization (Unit 3, revisited) | New, condensed sentences rather than extracted ones |
| Text-to-image prompting | Natural-language descriptions turned into structured prompts for image models |
| Data augmentation | Generating synthetic training examples for other NLP models (e.g. more labeled sentiment examples for Unit 3's classifiers) |
Retrieval-Augmented Generation (RAG) — Addressing Hallucination
Recall from the previous lesson that a key LLM limitation is hallucination — generating fluent but factually wrong text. RAG systems address this by first retrieving relevant documents (using TF-IDF/embedding similarity search — techniques from Unit 3 and this unit) and feeding them into the LLM's prompt as grounding context before generation.
Ethical Considerations in Generative AI
| Concern | Description |
|---|---|
| Misinformation | Fluent generated text can spread convincing false information |
| Bias amplification | Models can reproduce and amplify biases present in training data |
| Copyright and attribution | Training data and generated output raise ownership/originality questions |
| Deepfake text / impersonation | Generated text can convincingly imitate a specific person's writing style |
| Job/workflow displacement | Automation of writing, coding, and support tasks previously done by humans |
Course Wrap-Up — How It All Connects
Every concept in this course — from tokenizing a sentence in Unit 1 to prompting an LLM in Unit 4 — is one link in this same pipeline. Understanding the classical foundations (regex, n-grams, TF-IDF, Naïve Bayes) is exactly what lets you debug, fine-tune, and reason about why modern generative AI systems behave the way they do.