Definition: Context engineering for AI refers to the systematic practice of designing and structuring the information, instructions, and framing provided to AI systems to optimize their performance and output quality. It encompasses techniques like prompt engineering, providing relevant background information, setting appropriate constraints, and crafting the input environment to guide AI behavior toward desired outcomes.

Origins: I don’t have definitive information about who first coined the specific term “context engineering” in AI. The concept evolved from prompt engineering practices that emerged with early language models, but the precise attribution of this terminology isn’t clearly documented in my knowledge.

Practical Application: Most people should apply context engineering by being deliberate about how they frame requests to AI systems. This means providing clear, specific instructions; including relevant background information; giving examples of desired outputs; setting appropriate constraints and boundaries; and iteratively refining their approach based on results. Start with clear objectives, provide necessary context without overwhelming detail, use consistent formatting, and test different approaches to find what works best for your specific use case.

Pros and Cons:

Pros:

• Dramatically improves output quality and relevance
• Reduces the need for multiple iterations and corrections
• Enables more predictable and reliable AI behavior
• Allows leveraging AI capabilities more effectively
• Can save significant time once mastered
• Enables complex task decomposition and multi-step reasoning

Cons:

• Requires time investment to learn and master
• Can become overly complex, leading to diminishing returns
• May create brittleness where small changes break the system
• Risk of over-engineering simple tasks
• Can introduce bias through poorly designed context
• Effectiveness varies across different AI models and versions
• May require constant adjustment as AI systems evolve

The key is finding the right balance—being thorough enough to get quality results without over-complicating straightforward tasks. Context engineering is most valuable for complex, repeated, or high-stakes interactions with AI systems, while simpler queries often benefit from more straightforward approaches.

Definition (2 sentences)
Context engineering is the craft of explicitly assembling the system messages, retrieved knowledge, conversation history, tools, schemas, and live data that enter an AI model’s context window. By treating that window like programmable “RAM,” it lets builders make the model reason consistently across many turns instead of hoping one clever prompt will suffice. (DataCamp, LangChain Blog)

Who coined it?
The earliest widely-circulated use of the term in an AI setting came from Shopify CEO Tobi Lütke’s 19 June 2025 tweet praising “context engineering” as the real skill behind reliable LLM apps; Andrej Karpathy boosted the tweet the same day, and Simon Willison plus LangChain’s Harrison Chase amplified it in blogs that week, locking the phrase into industry vocabulary. (OfficeChai, Simon Willison’s Weblog)

How to apply it in everyday work

1. Map the job-to-be-done. List the facts, rules, prior messages, and external data sources an AI assistant must consult for each task.
2. Automate retrieval & compression. Use RAG or embeddings to pull only the most relevant chunks, then trim or summarize them so you stay within token limits.
3. Layer context deliberately. Order inputs as system instructions → role-specific policies → current user request → retrieved snippets → tool definitions → expected output schema.
4. Instrument & iterate. Log every context bundle and model response; when outputs fail, diagnose whether the fault was missing, noisy, or mis-formatted context rather than the model itself. (DataCamp, LangChain Blog)

Pros

• Reliability at scale – well-engineered context keeps the 1 000th call as good as the first.
• Model-agnostic – improves performance without retraining or fine-tuning.
• Explainability – explicit context bundles make debugging and audits simpler.

Cons

• Engineering overhead – building retrieval, memory, and ordering pipelines is harder than writing prompts.
• Latency & cost trade-offs – larger contexts mean more tokens and slower responses unless carefully pruned.
• Maintenance burden – context rules, retrieval indexes, and tool specs must evolve with the product or they drift and break quality. (LangChain Blog)

Total: ≈270 words (well under 400).

Context engineering is the practice of designing and structuring the input provided to an AI to guide its reasoning and generate more accurate, relevant, and reliable outputs. It involves strategically providing specific instructions, examples, constraints, and relevant data to shape the AI’s behavior without altering its underlying code.

Origins of the Term

The term “context engineering” doesn’t have a single, formally recognized originator. Instead, it evolved naturally from the practice of prompt engineering as developers and users of Large Language Models (LLMs) discovered that providing a richer, more structured context was far more effective than simply refining a short prompt. It arose from the collective, practical need to gain more control and better performance from powerful but sometimes unpredictable AI systems.

How to Apply It

Most people can apply context engineering by treating their interaction with an AI not as a simple question-and-answer session, but as a briefing. You can improve your results by:

• Assigning a Persona: Start by telling the AI who it should be (e.g., “You are an expert financial analyst”).
• Providing Examples: Give one or two examples of the exact output format you want (this is called “few-shot prompting”).
• Setting Constraints: Clearly state what the AI shouldn’t do (e.g., “Do not use jargon,” “Keep the answer under 100 words”).
• Including Relevant Data: Paste in text, data, or background information for the AI to use as its source of truth.

By building this context around your core request, you guide the AI toward the desired outcome.

Pros and Cons

Pros 👍

• Increased Accuracy: Significantly improves the quality and relevance of AI-generated content.
• Better Control: Allows users to steer the AI’s tone, style, and format effectively.
• Cost-Effective: It’s much cheaper and faster than fine-tuning or retraining a model.
• Accessible: It doesn’t require advanced technical skills; anyone can learn to do it.

Cons 👎

• Time-Consuming: Crafting the perfect context can require significant trial and error.
• Model Dependent: A context that works well on one AI model may not work on another.
• Doesn’t Fix Core Flaws: It can’t overcome a model’s fundamental knowledge gaps or inherent biases.
• Complexity Risk: Overly long or confusing contexts can sometimes degrade the AI’s performance.