دانلود کتاب مهندسی بافت (Context Engineering) با استفاده از خط‌لوله‌های پرامپتِ خودبهینه‌ساز در DSPy برای ساخت عامل‌های هوش مصنوعی قابل‌اطمینان

Working with large language models (LLMs) can be tricky — they need enough information to do the job, but too much information costs a lot and leads to context rot (how performance degrades as input length increases). AIs work best when you give them the right information, in the right format, at the right time. As Tobi Lütke, founder and CEO of Shopify, puts it: “I really like the term context engineering over prompt engineering. It describes the core skill better: the art of providing all the context for the task to be plausibly solvable by the LLM.” As models have gotten better at reasoning, the specific prompts have become relatively less important than the inputs available for the model to reason through. Solving context engineering has become the central challenge of modern AI development. The death of prompt engineering has been greatly exaggerated, but Andrej Karpathy, former Director of AI at Tesla, has cast his vote: “+1 for context engineering over ‘prompt engineering.'” Get the context right, use AI to rewrite its own instructions, and your application becomes reliable, scalable, and useful. Get it wrong and you burn tokens, lose accuracy, or suffer from constant hallucinations. DSPy is the definitive context engineering framework, handling all the plumbing, scaling, and optimization of your AI programs across model providers, leaving you free to focus on what matters most — the flow, specification, and evaluation of the system. With tens of thousands of GitHub stars and millions of monthly downloads, it is a cult classic among AI engineers (including Lütke) who braved its unfamiliar syntax. DSPy lets you define what your system should do — the inputs and outputs — in modular components paired with datasets and evaluation metrics. Unlike historically overhyped AI frameworks, DSPy rewards its practitioners with world-class prompt optimizers that reliably deliver massive performance gains: -From 24% to 51% success on question answering with search -A 10% improvement in document retrieval quality -From 66% to 87% accuracy on classification tasks -Improvements from 10% to 100%+ in my own consulting work as an AI engineer At its core, DSPy gives you three things every AI team needs: -Portable programs that run across different models and providers -Maintainable modules that are composable, reusable, and easily modified -Self-improving prompts that evolve through evaluation, not guesswork. Context engineering is already shaping the future of AI, and DSPy is how you put it into practice. Free yourself from endless tinkering with brittle prompt strings, so you can focus on solving real engineering problems instead.