This comprehensive guide introduces Context Engineering as a critical methodology for building high-performance AI agents, moving beyond traditional Prompt Engineering. It details the components of a production harness, memory architectures, and multi-agent patterns, illustrating its effectiveness through benchmarks.
The article proposes a crucial shift from prompt engineering to context engineering, arguing that many AI failures stem from missing relevant information rather than poor phrasing. Context engineering involves deliberately providing the AI with crucial data, such as system instructions, project documentation, and source files, before it acts.
O artigo discute a ênfase de Karpathy em "engenharia de contexto" em vez de "engenharia de prompt" para LLMs, destacando que a performance da IA depende crucialmente do contexto fornecido. Ele aponta o problema de LLMs consumirem muitos tokens repetidamente para entender o contexto de um código, levando o autor a desenvolver uma ferramenta para automatizar esse processo.
Context engineering is the discipline of systematically designing the information environment surrounding a prompt in LLM systems. This skill, expected to replace prompt engineering by 2026, focuses on what the model needs to know to perform well, rather than just what it should do.
The article compares two approaches to improve the reliability of small AI models in agentic workflows: Forge Guardrails, which uses runtime guardrails, and Context Kit, which employs context engineering. Both solutions aim to boost the performance of small models, with Forge reporting a jump from 53% to 99% and the Context Kit raising Gemma 4 from 75% to 92% parity with Claude Opus. The text explores how these different methodologies collide and how a hypothetical combination could work to solve the problem of small models failing in multi-step loops.
This article introduces the Context Pyramid, a framework for Product Managers to diagnose and solve AI agent context problems. It emphasizes that effective context depends not on the size of the window, but on the quality, structure, and relevance of the information provided to the agent.
Este artigo defende que a 'Context Engineering' é a nova 'Prompt Engineering', tratando a IA como um membro da equipe com acesso a documentação e processos, em vez de uma caixa de busca. Baseado em experiência prática, ele descreve a construção de sistemas de IA em produção para operações de pré-vendas.
This guide provides developers with insights into effective context engineering for AI agents. It explores methods and best practices to optimize the performance and reliability of AI systems through well-structured contextual inputs.
