Data Analysis with LLMs

Data Analysis with LLMs by Cornell professor Immanuel Trummer is the practical field manual every data practitioner needs to exploit the transformative capabilities of modern language models. Starting from first principles—what a prompt is, how tokenization works, why few-shot examples help—the book walks readers step by step through real Python mini-projects that classify text, extract structured information, cluster documents, translate natural language into SQL and Cypher queries, answer questions about images and videos, transcribe and translate audio, and build voice-driven database interfaces. It then tackles the hard economic problem every production team faces: how to get high-quality results without overpaying. Chapters on model selection, parameter tuning, prompt engineering, and fine-tuning demonstrate concrete cost-quality tradeoffs on a running sentiment-classification scenario. The final section broadens the toolkit to GPT alternatives (Anthropic, Cohere, Google, Hugging Face), the LangChain agent framework, and LlamaIndex for multimodal retrieval—giving readers everything they need to design sophisticated, maintainable AI pipelines. Whether you are a software developer, data scientist, or curious hobbyist, this book turns the magic of LLMs into systematic, replicable engineering practice.

What it argues

A causal model describing how design levers applied by a practitioner—prompt design, model selection, parameter configuration, fine-tuning, and architectural choices—drive psychological and behavioral outcomes (trust, adoption, iteration speed) and ultimately determine the cost, quality, and scalability of LLM-powered data analysis pipelines.