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After years of prompt engineering dominating the applied AI conversation, a critical evolution is underway. The focus is shifting from finding the perfect words to a more fundamental question: What configuration of context will most reliably produce our desired model behavior?
This is the essence of context engineering: the art and science of curating what information enters an LLM's limited attention budget at each step of inference.
Core Principle
Good context engineering means finding the smallest possible set of high-signal tokens that maximize the likelihood of your desired outcome.
From Prompts to Context: The Evolution
In the early days, prompt engineering focused primarily on crafting effective system prompts for one-shot tasks. But as we build agents that operate over multiple turns and longer time horizons, we need strategies for managing the entire context state.
Prompt Engineering
A discrete task of writing optimal instructions
Single-shot focus
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→
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Context Engineering
An iterative process of curating holistic state
Multi-turn optimization
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Why Context Engineering Matters
Research has uncovered a critical phenomenon: context rot. As tokens in the context window increase, the model's ability to accurately recall information decreases. Like humans with limited working memory, LLMs have a finite "attention budget."
The Technical Reality: LLMs use transformer architecture where every token attends to every other token, creating n² pairwise relationships. As context grows, this attention gets stretched thin.
The Anatomy of Effective Context
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System Prompts
Find the "right altitude" between brittle hardcoded logic and vague high-level guidance. Use clear sections with XML tags or markdown headers.
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🔧
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Tools
Design self-contained tools with minimal overlap. Return token-efficient information. If a human can't decide which tool to use, neither can the agent.
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📚
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Examples
Curate diverse, canonical examples instead of edge case lists. For LLMs, examples are the "pictures" worth a thousand words.
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Just-in-Time Context Retrieval
Rather than pre-loading all relevant data, modern agents maintain lightweight identifiers (file paths, queries, links) and dynamically load data at runtime. This mirrors human cognition: we don't memorize corpora, we use systems like file structures to retrieve information on demand.
Hybrid Retrieval Strategy
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Pre-loaded
Essential config files
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🔍
Just-in-Time
Dynamic exploration
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Techniques for Long-Horizon Tasks
For tasks spanning minutes to hours, three techniques address context window limitations:
| Technique |
How It Works |
Best For |
| Compaction |
Summarize context and reinitiate with compressed summary |
Extensive back-and-forth |
| Note-Taking |
Persist notes outside context, pull back when needed |
Iterative development |
| Sub-Agents |
Specialized agents return condensed summaries to lead agent |
Complex research tasks |
Best Practice
"Do the simplest thing that works" remains the best advice. As models improve, agents can operate with more autonomy and less prescriptive engineering.
The Takeaway
Context engineering represents a fundamental shift in how we build with LLMs. The guiding principle is consistent: treat context as a precious, finite resource and find the smallest set of high-signal tokens that maximize your desired outcome.
Even as model capabilities scale, this principle will remain central to building reliable, effective agents.
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