Overview

Large Language Models are powerful. Precise prompting makes them useful. Python developers are at the center of the AI revolution. But integrating Large Language Models (LLMs) into real applications requires more than calling an API. It demands structured prompting, evaluation, cost control, and architectural discipline. Prompt Engineering for Pythonistas is a practical guide to designing, testing, and deploying LLM-powered features in modern Python applications-without hype and without fragile demos. What You'll Learn in This Book The foundations of prompt engineering for developers How LLMs actually process instructions Writing structured prompts for reliable outputs Few-shot, zero-shot, and chain-of-thought prompting Managing context windows and token limits Building Python pipelines around LLM APIs Evaluating and improving model responses systematically Designing production-ready AI features The focus is on engineering rigor, not guesswork. Who This Book Is ForThis guide is ideal for: Python developers integrating AI features Backend engineers building LLM-powered services AI engineers and applied ML practitioners Startup teams shipping AI products Developers moving from experimentation to production Basic Python experience is recommended. Why Prompt Engineering Is an Engineering DisciplinePrompting is not trial and error. When done properly, it becomes: A structured design process A reproducible experimentation workflow A measurable optimization task A critical layer of AI system reliability This book teaches how to treat prompts as part of your architecture, not as ad hoc strings.

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