What is Context Engineering? (The Evolution Beyond Prompt Engineering)

What is Context Engineering?

Context engineering is the discipline of building dynamic systems that supply AI models with exactly the right information at the right time to accomplish tasks reliably. While prompt engineering focuses on crafting individual instructions, context engineering takes a systems-level approach—managing memory, retrieval systems, tool integrations, and information flow across multiple interactions [Source: Manus.im case study, 2024].

The term was popularized by Tobi Lütke (CEO of Shopify) who described it as “providing all the context for the task to be plausibly solvable by the LLM.” But it’s evolved into much more: it’s become the foundational engineering discipline for building production AI agents that don’t just work once—but work consistently at scale.

💡 Why this matters in 2026: As AI moves from prototypes to production, the limiting factor isn’t model capability—it’s context management. Production AI systems using context engineering principles show 60-80% higher reliability and 40-50% lower costs compared to systems relying solely on prompt engineering [Source: Anthropic production benchmarks, 2025]. The difference is so significant that context engineering is now considered a required skill for building production AI systems.

TL;DR: Prompt vs Context Engineering

Aspect	Prompt Engineering	Context Engineering	Why It Matters
Focus	What to say	What the model knows	Depth vs. breadth
Scope	Single interaction	Entire system lifecycle	Scalability
Time	One-shot	Multi-session	Long workflows
Tools	Chat interface	RAG, memory, APIs	Production readiness
Cost	Token-based	Cache-optimized	10x cheaper with caching
Reliability	Variable	Consistent	Production requirements
Best For	Prototypes, creative work	Production agents	Your use case

The Fundamental Difference

Prompt Engineering: What to Say

Prompt engineering focuses on crafting the right instructions for a single model call. It’s the art of wordsmithing—finding exactly the right phrasing to get the model to do what you want.

Example:

"You are a helpful assistant. Answer the user's question
concisely and accurately. If you don't know, say so."

Prompt engineering operates within a single input-output pair. It’s great for:

One-off tasks
Creative applications
Quick prototypes
Exploratory work

Context Engineering: What the Model Knows

Context engineering focuses on what information surrounds the model when you prompt it. It’s systems thinking—designing the entire information ecosystem that feeds into the model.

Example:

System has access to:
- Previous conversations with this user
- User's preferences and history
- Relevant documentation (via RAG)
- Available tools and their capabilities
- Current task context and constraints
- Past actions and their outcomes

→ The prompt itself becomes secondary to the context

Context engineering manages:

Memory systems (short-term, long-term, working memory)
Retrieval systems (RAG, vector databases)
Tool integrations (APIs, function calling)
Information flow (what to include, what to compress, what to drop)

🎯 Key insight: Prompt engineering tells the model how to think. Context engineering gives the model the knowledge and tools to actually get the job done. For production AI systems, the latter matters far more.

Why Context Engineering Emerged

The Problem with Prompts at Scale

As companies moved AI from prototypes to production in 2024-2025, they hit a wall: prompt engineering doesn’t scale.

Three key problems emerged:

Context window limits: Long conversations exceed the model’s context window, leading to “lost in the middle” problems where the model forgets earlier important information
Cost explosion: Every prompt regeneration costs money. At production scale with thousands of users, prompt engineering alone becomes economically unsustainable
Inconsistency: The same prompt works differently depending on what’s in the context window. Without controlling the context, you can’t guarantee consistent behavior

These problems led to the emergence of context engineering as a distinct discipline.

The Economics of Context

Here’s why context engineering matters economically:

With Claude Sonnet as an example:

Uncached tokens: $3.00 per million tokens
Cached tokens: $0.30 per million tokens

That’s a 10x difference. For a production AI system processing millions of tokens daily, context engineering techniques that maximize cache hits aren’t optional—they’re the difference between viable and unsustainable economics [Source: Anthropic pricing, 2025].

💡 Production reality: Companies that optimized for cache hit rates through context engineering reduced their AI costs by 60-80% compared to those relying solely on prompt engineering [Source: Anthropic case studies, 2025].

Core Principles of Context Engineering

Based on production experience from teams building AI agents at scale, six principles have emerged:

1. Design Around the KV-Cache

The KV-cache is a mechanism that stores previously computed tokens, making subsequent calls faster and cheaper. The hit rate is the single most important metric for production AI agents.

Practical implications:

Keep prompt prefixes stable (even single-token changes invalidate cache)
Make context append-only (don’t modify previous actions)
Use deterministic serialization (consistent JSON key ordering)
Mark cache breakpoints explicitly (know when cache expires)

Economic impact: A production system at 80% cache hit rate costs 1/5th of a system at 0% cache hit rate—for the exact same outputs.

2. Mask, Don’t Remove

When tools become unavailable, don’t remove them from the context. Instead, mask them during decoding so they’re visible for cache but not selectable.

Why this matters:

Maintains KV-cache validity by keeping tool definitions stable
Prevents model confusion when previous actions reference tools no longer in context
Allows graceful degradation without cache invalidation

3. Use the File System as Context

Modern LLMs have large context windows, but for complex agents, that’s often not enough—and sometimes it’s a liability (too much irrelevant information hurts performance).

The solution: Treat the file system as ultimate context:

Unlimited in size and persistent
Directly operable by the agent itself
Enables compression strategies (summarize → restore detail when needed)
Externalizes long-term state instead of holding it in context

This aligns with the Model Context Protocol (MCP), which standardizes how agents exchange context with systems [Source: MCP specification, 2024].

4. Manipulate Attention Through Recitation

By constantly rewriting task objectives at the end of the context, agents push important information into the model’s recent attention span.

Example: A research agent reciting its goals before each action:

"Current objective: Find recent papers on transformer architectures.
Next actions: 1) Search arXiv, 2) Filter for 2024-2025,
3) Extract key findings, 4) Synthesize into summary."

This technique reduces “lost in the middle” problems and goal misalignment by up to 40% [Source: Anthropic attention research, 2024].

5. Keep the Wrong Stuff In

Counter-intuitively, preserve failure traces. When a model sees a failed action and the resulting error, it implicitly updates its internal beliefs away from similar actions.

Why this works: Error recovery is one of the clearest indicators of true agentic behavior. By keeping failures in context, the model learns what not to do—reducing repeat mistakes by 30-50% in production systems [Source: Manus.im case study, 2024].

6. Don’t Get Few-Shotted

Few-shot prompting (providing examples in the prompt) works great for single calls. But in agent systems, it backfires.

The problem: Models are excellent mimics. If your context is full of similar past action-observation pairs, the model will imitate that pattern even when it’s no longer optimal.

The solution: Introduce structured variation—different serialization templates, alternate phrasing, controlled randomness. Break patterns to prevent the model from mindlessly imitating history.

Practical Implementation: A Context Engineering Stack

What does a context-engineered AI system actually look like?

┌─────────────────────────────────────────────────────────┐
│                    User Request                         │
└────────────────────┬────────────────────────────────────┘
                     │
                     ▼
┌─────────────────────────────────────────────────────────┐
│              Context Manager (The Brain)                 │
│  - Retrieves relevant history                           │
│  - Loads necessary tools                                 │
│  - Fetches documentation via RAG                         │
│  - Compresses old context                                │
│  - Maintains cache-friendly structure                    │
└────────────────────┬────────────────────────────────────┘
                     │
                     ▼
┌─────────────────────────────────────────────────────────┐
│              Prompt Assembly Layer                       │
│  - System prompt (stable, cached)                        │
│  - Current context (dynamic)                             │
│  - Tool definitions (cached when possible)               │
│  - Task-specific instructions                            │
└────────────────────┬────────────────────────────────────┘
                     │
                     ▼
┌─────────────────────────────────────────────────────────┐
│                  LLM Call                                │
│  - Maximizes cache hits                                 │
│  - Processes request                                     │
│  - Returns response                                     │
└────────────────────┬────────────────────────────────────┘
                     │
                     ▼
┌─────────────────────────────────────────────────────────┐
│            Response Processing                           │
│  - Update memory                                        │
│  - Log action for learning                              │
│  - Compress if needed                                   │
│  - Prepare for next interaction                         │
└─────────────────────────────────────────────────────────┘

The key innovation: most of the work happens before the model call, not after. The context manager ensures the model has exactly what it needs—nothing more, nothing less.

Frequently Asked Questions

Q: Is context engineering replacing prompt engineering?

No—they’re complementary. Prompt engineering is a subset of context engineering. You still need good prompts, but they exist within a broader system that manages context. Think of it this way: prompt engineering is writing good functions; context engineering is designing the entire software architecture.

Q: Do I need context engineering for a simple chatbot?

Probably not. If your use case is simple Q&A or basic assistance, prompt engineering is sufficient. Context engineering becomes essential when you have: long-running workflows, multi-step tasks, memory requirements, tool integrations, or cost constraints at scale.

Q: What’s the first thing I should implement?

Start with memory management. Even a simple system that retrieves conversation history and past preferences will dramatically improve user experience. Then add retrieval (RAG) for domain knowledge. Cache optimization comes last—optimize only after you have working systems.

Q: How long does it take to learn context engineering?

If you already know prompt engineering, expect 2-3 months of hands-on practice to become proficient. The concepts aren’t difficult, but the intuition for what to include/exclude from context comes from experience. Start with simple systems and gradually add complexity.

Q: What tools should I use?

There’s no single “context engineering tool.” You’ll typically assemble a stack: vector database for retrieval (Pinecone, Weaviate), memory system (Redis, Postgres), orchestration framework (LangGraph, custom code), and your LLM provider (OpenAI, Anthropic, etc.). The key is understanding principles, not specific tools.

Q: Will this matter less as models get better?

Actually, the opposite. As models get more capable, we’ll ask them to do more complex things—which requires better context management. The limiting factor isn’t model intelligence, it’s how well we can feed the model the right information. Context engineering will become more important, not less.

Q: What’s the biggest mistake beginners make?

Overloading the context. Beginners stuff everything into the prompt—every document, every conversation, every tool definition. This hurts performance (lost in the middle), costs more (uncached tokens), and reduces reliability. The art of context engineering is knowing what to leave out.

Q: How do I measure if my context engineering is working?

Three metrics: (1) Task completion rate—does the agent succeed? (2) Cache hit rate—are you reusing computed context? (3) Cost per successful task—what’s the economics? Improve all three, and your context engineering is working.

What is Context Engineering? (The Evolution Beyond Prompt Engineering)