{ "item_title" : "Prompt Engineering for Pythonistas", "item_author" : [" Thompson Carter "], "item_description" : "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 BookThe foundations of prompt engineering for developersHow LLMs actually process instructionsWriting structured prompts for reliable outputsFew-shot, zero-shot, and chain-of-thought promptingManaging context windows and token limitsBuilding Python pipelines around LLM APIsEvaluating and improving model responses systematicallyDesigning production-ready AI featuresThe focus is on engineering rigor, not guesswork.Who This Book Is ForThis guide is ideal for: Python developers integrating AI featuresBackend engineers building LLM-powered servicesAI engineers and applied ML practitionersStartup teams shipping AI productsDevelopers moving from experimentation to productionBasic Python experience is recommended.Why Prompt Engineering Is an Engineering DisciplinePrompting is not trial and error. When done properly, it becomes: A structured design processA reproducible experimentation workflowA measurable optimization taskA critical layer of AI system reliabilityThis book teaches how to treat prompts as part of your architecture, not as ad hoc strings.", "item_img_path" : "https://covers1.booksamillion.com/covers/bam/9/79/824/219/9798242196728_b.jpg", "price_data" : { "retail_price" : "19.99", "online_price" : "19.99", "our_price" : "19.99", "club_price" : "19.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }

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