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Part IV: Large Language Models in Data Science
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*How can we leverage AI to enhance data science workflows?* The previous parts developed computational methods for statistical inference: simulation for approximating distributions, resampling for quantifying uncertainty, and MCMC for Bayesian posterior computation. These methods share a common thread—they use computation to solve problems that resist analytical treatment. Part IV extends this computational perspective to a new frontier: integrating large language models into data science pipelines.

LLMs represent a paradigm shift in how we process unstructured data. Trained on vast text corpora, these models encode rich representations of language, concepts, and reasoning patterns. For data scientists, they offer powerful capabilities: transforming raw text into numerical features, annotating data at scale, generating synthetic examples, and assisting with analysis. But with power comes responsibility—LLMs can hallucinate, leak private information, and amplify biases. Effective use requires understanding both capabilities and limitations.

This part is deliberately practical. While Parts II and III developed rigorous mathematical foundations for frequentist and Bayesian inference, Part IV focuses on integration patterns, best practices, and responsible deployment. The goal is not to train you as an ML engineer but to equip you as a data scientist who can thoughtfully incorporate LLMs into analytical workflows.

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**The Arc of Part IV**

**Chapter 6** develops the core concepts and practical skills for LLM integration in data science.

We begin with **foundations**: what LLMs are, how they're trained, and why they exhibit the capabilities they do. You'll understand the transformer architecture at a conceptual level, the role of pre-training and fine-tuning, and the trade-offs between model sizes and deployment options (API-based vs. local).

**Embeddings and feature extraction** transform text into dense vector representations that capture semantic meaning. These embeddings enable similarity search, clustering, classification, and other downstream tasks. We implement preprocessing pipelines, generate embeddings using both API-based and open-source models, and integrate these features into statistical models from earlier chapters.

**Data annotation and augmentation** addresses a persistent bottleneck: labeled data. LLMs can annotate text at scale—sentiment, topics, entities, relationships—with quality approaching human labelers for many tasks. We develop annotation pipelines, implement quality control, and discuss when LLM annotation is appropriate versus when human labels remain essential.

**Retrieval-augmented generation (RAG)** extends LLM capabilities to domain-specific knowledge. By retrieving relevant documents before generation, RAG systems ground responses in authoritative sources—critical for applications where accuracy matters. We build RAG pipelines, discussing chunking strategies, retrieval methods, and evaluation.

**Prompt engineering** develops systematic approaches to reliable outputs: clear instructions, few-shot examples, chain-of-thought reasoning, and output formatting. We treat prompts as code—version-controlled and reproducible.

**Responsible AI practices** address privacy, bias, transparency, and appropriate use. We distinguish between open models (deployable locally, inspectable) and closed models (API-only, opaque), discussing implications for different applications.

**What Part IV Is Not**

To set appropriate expectations:

*This is not a deep learning course.* We won't derive backpropagation, implement transformers from scratch, or train models on GPUs. Our focus is using pre-trained models effectively, not building them.

*This is not a prompt engineering bootcamp.* While we cover prompting techniques, the emphasis is on principled integration into data science workflows, not maximizing chatbot performance.

*This is not comprehensive NLP.* Traditional NLP topics appear only as they relate to practical data science applications. Students interested in NLP depth should take dedicated courses.

What Part IV *is*: a practical guide to thoughtfully integrating LLMs into the computational toolkit you've built throughout this course.

**Connections**

*Part I: Foundations* establishes the computational thinking that transfers to LLM integration. The mindset—using computation to solve intractable problems—applies whether we're simulating distributions or generating embeddings.

*Part II: Frequentist Inference* provides validation principles. Cross-validation from Chapter 4 applies directly to evaluating LLM performance. Bootstrap methods quantify uncertainty in LLM-derived features.

*Part III: Bayesian Inference* contributes uncertainty quantification. When LLMs generate predictions or annotations, we need to assess confidence. The habit of asking "How confident should I be?" transfers directly. Bayesian methods appear in calibration, active learning, and probabilistic retrieval.

*Capstone Project* synthesizes all parts. Students may incorporate LLM capabilities—embeddings, annotation, RAG—alongside the statistical methods from earlier chapters to address real-world problems.

**Prerequisites**

Part IV assumes familiarity with the statistical foundations from Parts I–III, particularly model validation (cross-validation), uncertainty quantification (confidence/credible intervals), and Python programming. No prior experience with deep learning or NLP is required—we develop the necessary concepts from a data science perspective.

By Part IV's end, you'll be able to integrate LLMs into data science workflows responsibly: generating embeddings, annotating data, building RAG systems, and assessing reliability—with appropriate attention to limitations and ethical considerations.