Context Engineering: The New Frontier of AI

This article is written in English. Our training modules are available in French.

In 2024, AI leaders started saying something surprising: "Prompt engineering is dead. Context engineering is what matters now." What does that mean, and why should you care?

What Is Context Engineering?

Context engineering is the discipline of designing, curating, and optimizing all the information you provide to an AI model—not just the prompt itself.

The Shift in Thinking

Prompt Engineering (2022-2023):
"How do I write the perfect prompt?"

Context Engineering (2024-2025):
"How do I provide the right information, at the right time, in the right format?"

It's a fundamental shift from crafting instructions to architecting information systems.

Why the Shift Happened

1. Models Got Smarter

Early LLMs needed very specific prompt formats. Modern models (GPT-4, Claude 3.5, Gemini) understand instructions naturally. The bottleneck moved from "understanding the prompt" to "having the right information."

2. Applications Got Complex

Simple chatbots only needed good prompts. Production AI systems need:

• →Document retrieval (RAG)
• →Conversation memory
• →Tool definitions
• →User preferences
• →Real-time data

Managing all this is context engineering.

3. Context Quality Became the Differentiator

Research shows that a weaker model with excellent context often outperforms a stronger model with poor context:

GPT-3.5 + Perfect Context > GPT-4 + Poor Context

The 8 Components of Context

When you send a message to an AI, here's what makes up the full context:

| Component | Description | Example | |-----------|-------------|---------| | System Prompt | Behavioral instructions | "You are a legal assistant..." | | User Input | The current question | "What are the contract terms?" | | Conversation History | Recent exchanges | Last 5-10 messages | | Long-term Memory | Persistent knowledge | User preferences, past decisions | | Retrieved Documents | RAG results | Relevant policy sections | | Tool Definitions | Available functions | Calculator, search, database | | Output Format | Expected structure | JSON schema, markdown | | Few-shot Examples | Demonstrations | Sample inputs/outputs |

Context engineering = optimizing how all 8 work together.

What the Experts Say

"The most important skill for building with LLMs is not prompt engineering, it's context engineering." — Andrej Karpathy, ex-AI Director at Tesla

"Most agent failures are not model failures, they're context failures." — Tobi Lütke, CEO of Shopify

"The bottleneck for LLM applications has shifted from model capabilities to context quality." — Anthropic (creators of Claude)

This isn't hype—it's the consensus among AI leaders in 2025.

Context Engineering vs Prompt Engineering

| Aspect | Prompt Engineering | Context Engineering | |--------|-------------------|---------------------| | Focus | Single instruction | Entire information ecosystem | | Scope | One message | Full application architecture | | Approach | Static text | Dynamic information flows | | Optimization | Word choice | System design | | Scale | One interaction | Thousands of users |

Prompt engineering is a subset of context engineering, not a replacement.

The Impact of Good Context

Research and real-world implementations show dramatic differences:

| Metric | Poor Context | Optimized Context | |--------|-------------|-------------------| | Response accuracy | 60-70% | 90-95% | | Hallucination rate | 15-20% | 2-5% | | User satisfaction | Moderate | High | | Cost efficiency | Baseline | 30-50% reduction |

Bad context can degrade performance by 30-70%, even with the best models.

Real-World Example

Insurance Claims Processing

Before Context Engineering:

Prompt: "Process this insurance claim"
Context: Raw claim document (10,000 tokens)
Result: 65% accuracy, many errors

After Context Engineering:

System: Claims processing rules + decision criteria
Retrieved: Relevant policy sections only (2,000 tokens)
Memory: Customer history + previous claims
Tools: Policy lookup, calculation functions
Format: Structured JSON output

Result: 94% accuracy, audit trail included

Same model. Same task. Completely different results.

The 4 Pillars Framework

Leading AI engineers use a framework with 4 strategies:

1. WRITE

Persist information outside the context window for later retrieval.

2. SELECT

Choose only the most relevant information to include.

3. COMPRESS

Summarize or condense information to fit more in less space.

4. ISOLATE

Separate concerns to prevent context pollution.

Each pillar addresses a different challenge in context management.

Key Takeaways

1. →Context engineering = architecting all information provided to AI
2. →It's more important than prompt engineering for production systems
3. →Models are smart enough—the bottleneck is information quality
4. →8 components compete for context window space
5. →Good context engineering can improve accuracy by 30-50%

Ready to Master Context Engineering?

This article introduced the what and why of context engineering. But implementing it requires deep understanding of frameworks, techniques, and trade-offs.

In our Module 9 — Context Engineering, you'll learn:

• →The complete WRITE, SELECT, COMPRESS, ISOLATE framework
• →Dynamic context management for production systems
• →Memory architectures for long-running agents
• →Optimization strategies for cost and latency
• →Real-world implementation patterns

→ Explore Module 9: Context Engineering