What Is Context Engineering (and Why It Is Replacing Prompt Engineering)

2026-07-10 · Source: AutoGPT · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Advanced, long

Summary

Context engineering, popularized in June 2025 by Shopify CEO Tobi Lutke and amplified by Andrej Karpathy, is the practice of designing all information a language model sees before generating a response, extending beyond just the prompt. This encompasses system instructions, conversation history, retrieved documents, tool definitions, memory, and dynamically loaded context. A 2026 report indicates 82% of IT and data leaders find prompt engineering alone insufficient, with 95% planning context engineering training. While prompt engineering optimizes only the instruction layer, context engineering manages all six layers of the LLM's context window. This shift is driven by the rise of multi-step AI agents, increasingly capable models, and larger context windows that paradoxically require more precise context management to avoid "lost in the middle" issues. Key components include structured system instructions, multi-tier memory (static, dynamic, learned), Retrieval-Augmented Generation (RAG), intelligent tool orchestration, and dynamic context window management techniques like compaction and just-in-time loading, exemplified by Anthropic's Agent Skills system.

Key takeaway

For AI Engineers building or managing multi-step AI agents, you must shift beyond prompt engineering to comprehensive context engineering. Your focus should be on dynamically managing all six layers of the LLM's context window, including memory, retrieval, and tool orchestration, to prevent "lost in the middle" failures and optimize agent performance. Implement just-in-time context loading, like Anthropic's Agent Skills, to ensure your agents receive only the most relevant information, precisely when needed, avoiding token waste and improving decision-making.

Key insights

Context engineering, managing all LLM inputs, is replacing prompt engineering for complex, multi-step AI agent workflows.

Principles

• LLM context window functions as RAM.
• Right context beats more context.
• Manage context as a finite resource.

Method

Implement just-in-time context loading by scanning lightweight skill metadata, then dynamically loading full instructions and referenced files only when relevant to the current task.

In practice

• Use CLAUDE.md for static project conventions.
• Implement claude-mem for cross-session memory.
• Study Anthropic Agent Skills for dynamic loading.

Topics

• Context Engineering
• AI Agents
• LLM Context Management
• Retrieval-Augmented Generation
• Tool Orchestration
• Prompt Engineering

Best for: AI Engineer, Machine Learning Engineer, MLOps Engineer

Related on AIssential

• Context Engineering Explained in 3 Levels of Difficulty — Context engineering manages LLM context windows as dynamic resources to prevent information loss and performance degradation.
• Implement Anthropic's Context Engineering Framework with open source models — Dynamic context engineering mitigates "context rot" in LLM agents by managing information flow and computational load.
• Why More Context Makes Your Agent Dumber and What to Do About It — Nupur Sharma, Qodo — LLM agents struggle with large contexts, planning loops, and task overload; structured multi-agent systems and context optimization are key.
• Context Is the New Code — Patrick Debois, Tessl — Effective AI agent performance hinges on meticulously engineered context, necessitating a dedicated Context Development Life Cycle.
• A Guide to Context Engineering for LLMs — More context can degrade LLM performance due to architectural limitations and uneven attention distribution.
• Your Prompts Aren’t the Problem—Your Context Is — Effective AI performance hinges on managing the model's context window with relevant, timely information, not just prompt wording.

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Editorial summary, takeaway, and curation by AIssential. Original article published by AutoGPT.