Context Engineering vs Prompt Engineering: The 2025 AI Shift

Remember when “prompt engineering” was the hottest skill in AI? When developers spent hours crafting the perfect phrasing, hoping to coax better responses from ChatGPT? Those days are rapidly fading. In 2025, a new discipline has emerged that’s fundamentally changing how we build AI-powered applications: context engineering.

As Andrej Karpathy puts it: “Context engineering is the delicate art and science of filling the context window with just the right information for the next step.”

This shift isn’t just semantics—it represents a fundamental evolution from clever wordsmithing to rigorous software architecture. For AI coding assistants like Claude Code and Cursor, mastering context engineering is the difference between an agent that understands your entire codebase and one that breaks everything it touches.

The Evolution: From Writing Prompts to Architecting Context

Prompt engineering was largely about clever wording. Maybe if you phrased a request differently, added “think step by step,” or included few-shot examples, the LLM would produce better output. It was an art form, often feeling more like persuasion than engineering.

Context engineering is different. It’s architecture for intelligence.

The key insight is this: Prompt engineering is about what you ask. Context engineering is about what the model already knows when you ask it.

Early generative AI was stateless—each interaction existed in isolation. A clever prompt was often sufficient. But autonomous AI agents are fundamentally different. They persist across multiple interactions, make sequential decisions, coordinate with other agents, and operate with varying levels of human oversight.

Why Prompt Engineering Breaks Down for Agents

Agentic AI systems suffer high failure rates under traditional prompt engineering. Here’s why:

Without shared context, agents misalign on priorities. One agent extracts data while another applies outdated rules because neither accesses the same operational state. They duplicate validation checks or ignore dependencies entirely.

Consider a typical multi-agent coding workflow:

Agent A analyzes the codebase structure

Agent B generates new code

Agent C writes tests

Agent D reviews for security issues

With prompt engineering alone, each agent starts from scratch. Agent B doesn’t know what Agent A discovered. Agent C can’t access Agent B’s implementation rationale. The result? Inconsistent code, redundant work, and subtle bugs that slip through because no single agent had the full picture.

Karpathy’s Operating System Analogy

Andrej Karpathy offers a powerful mental model: LLMs are like a new kind of operating system. The LLM itself is the CPU, and its context window is the RAM—the model’s working memory.

Just like RAM, the context window has limited capacity. And just as an operating system carefully curates what fits into RAM to maximize performance, context engineering plays the same role for LLM applications.

This analogy illuminates why context engineering matters so much:

• RAM is finite → Context windows have token limits (200K for Claude, 128K-200K for Cursor)
• RAM management is critical → Poor context management leads to performance degradation
• OS abstracts complexity → Good context engineering hides complexity from the model
• Priority scheduling exists → Not all context is equally important

The Four Pillars of Context Engineering

According to research from Anthropic and LangChain, effective context engineering for agents can be grouped into four strategies: Write, Select, Compress, and Isolate.

1. Write: Persisting Context Beyond the Window

Writing context means saving information outside the context window for later retrieval. This includes:

• Scratchpads for intermediate reasoning
• Memory stores for long-term facts
• Tool outputs persisted to files
• Conversation summaries for continuity

Claude Code exemplifies this with its /compact command, which automatically summarizes earlier conversation parts while retaining critical information. This “writes” compressed context that can be referenced later without consuming the full token budget.

2. Select: Retrieving the Right Information

Selecting context means pulling relevant information into the window when needed. Modern coding assistants use multiple selection strategies:

• RAG (Retrieval-Augmented Generation) for semantic search
• String-based search (grep, ripgrep) for exact matches
• File system indexing for code navigation
• Git history for understanding changes

Cursor’s approach combines RAG with traditional search tools, creating a two-pronged system that can find both semantically similar code and exact string matches.

3. Compress: Maximizing Information Density

Compression means retaining only the tokens necessary for the current task. Techniques include:

• Summarization of long conversations
• Chunking large files intelligently
• Filtering irrelevant code sections
• Truncation with smart boundaries

The goal isn’t just fitting more in—it’s ensuring the model focuses on what matters. As one practitioner notes: “Don’t dump a dozen files into the prompt ‘just in case.’ This creates noise and can confuse the model.”

4. Isolate: Parallel Context for Parallel Work

Isolation means splitting context across different agents or processes to enable parallel execution. Each agent gets a focused slice of context optimized for its specific task.

This is why multi-agent architectures are becoming dominant. Instead of one agent trying to hold everything in context, specialized agents work on isolated portions and coordinate through shared state.

Context Engineering in Practice: Claude Code vs Cursor

The competition between Claude Code and Cursor perfectly illustrates different approaches to context engineering.

Claude Code’s Approach

Claude Code emerged as what Karpathy calls “the first convincing demonstration of what an LLM Agent looks like.” Its context engineering strategy includes:

• 200K token context window — Larger than competitors, enabling true understanding of entire projects
• Automatic compaction — Intelligently summarizes conversations while preserving critical details
• Local-first architecture — Runs on your computer with access to your environment, data, and configuration
• Extended thinking — Can reason about complex architectural decisions before acting

The key insight from Anthropic: the agent runs on your computer, not in the cloud. This means it has natural access to your local context—files, environment variables, git history—without needing to upload everything.

Cursor’s Approach

Cursor takes a different path to context management:

• RAG + Search hybrid — Combines semantic search with traditional string matching
• Auto-managed windows — Limits chat sessions to ~20K tokens by default for speed
• Max Mode option — Allows up to 200K tokens when needed
• Aggressive caching — Indexes codebases for rapid retrieval

Cursor’s philosophy prioritizes latency over context size. By default, it keeps context small and fast, using retrieval to pull in relevant code on demand.

The ACE Framework: Proof That Context Beats Fine-Tuning

Perhaps the strongest validation for context engineering comes from the ACE (Augmented Context Engineering) framework, developed by researchers at Stanford University, SambaNova Systems, and UC Berkeley.

Their findings were striking:

• 10.6% improvement on agentic tasks through context engineering alone
• 8.6% gains on financial reasoning benchmarks
• 86.9% average latency reduction compared to fine-tuning approaches

The key insight: editing input context outperformed model fine-tuning. Instead of expensive retraining, teams can achieve better results by engineering what goes into the context window.

This has massive implications for AI coding assistants. Rather than fine-tuning models on specific codebases (expensive, slow, requires retraining), tools can achieve superior performance through intelligent context engineering (cheap, fast, dynamic).

Practical Context Engineering for Developers

If you’re building AI-powered development tools—or just trying to get better results from existing ones—here are actionable strategies:

For Tool Builders

Implement intelligent retrieval — Don’t rely on users to provide context. Use RAG, file indexing, and semantic search to automatically surface relevant code.

Design for compression — Build summarization into your architecture. Long conversations should automatically compact without losing critical information.

Support isolation — Enable parallel agent workflows where each agent gets focused context for its specific task.

Persist strategically — Write important discoveries to external storage (files, databases, memory stores) so they survive context window limits.

For Users of AI Coding Assistants

Provide focused context — Don’t dump entire codebases into prompts. Include only files directly relevant to your task.

Use project documentation — Tools like Claude Code respect CLAUDE.md files that describe project architecture. Create these to give agents persistent project knowledge.

Leverage retrieval features — Use @file references in Cursor or let Claude Code’s search find relevant code automatically.

Compact when needed — If conversations get long and responses degrade, use built-in compression commands like /compact.

The Future: Context-Aware AI Development

The shift from prompt engineering to context engineering signals a broader maturation of AI development. We’re moving from:

• Art to engineering — Reproducible systems over clever tricks
• Single-turn to multi-turn — Persistent agents over isolated queries
• Stateless to stateful — Accumulated knowledge over fresh starts
• Individual to collaborative — Multi-agent coordination over solo performance

For AI coding assistants, this evolution means increasingly sophisticated context management. Future tools will likely feature:

• Automatic context optimization — AI systems that manage their own context windows
• Cross-session memory — Agents that remember previous projects and preferences
• Team-level context sharing — Shared understanding across developer workflows
• Adaptive compression — Dynamic summarization based on task requirements

Conclusion

Prompt engineering isn’t dead—it remains important for crafting effective instructions. But it’s no longer sufficient for building reliable AI agents. Context engineering represents the next frontier: a discipline that treats the model’s working memory with the same rigor we apply to other system resources.

As Gartner notes, context engineering is rapidly replacing prompt engineering as the critical skill for enterprise AI success. For developers working with AI coding assistants, understanding these principles isn’t optional—it’s essential for getting the most out of tools like Claude Code and Cursor.

The question isn’t whether to adopt context engineering practices. It’s how quickly you can master them before the rest of the industry catches up.

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Key Takeaways:

Context engineering is architecture for intelligence — It’s about what the model knows, not just what you ask

Four pillars: Write, Select, Compress, Isolate — The fundamental strategies for managing context

Context beats fine-tuning — The ACE framework proved 10%+ improvements through context alone

Different tools, different approaches — Claude Code favors large context; Cursor favors fast retrieval

This is a learnable skill — Apply focused context, use documentation, leverage compression

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Ready to level up your AI-assisted development? Start by auditing how you provide context to your tools. The difference between a helpful AI agent and a frustrating one often comes down to the quality of context it receives.