This Week's Term: Context Engineering.
This Week's Term: Context Engineering - the practice of deliberately structuring, curating, and providing the right information to AI systems to maximize output quality, accuracy, and relevance.
While "prompt engineering" focuses on how you ask, context engineering focuses on what information you provide. Think of it as the difference between asking a good question versus giving someone the right background before they answer.
The business stakes are significant. Poor context leads to generic outputs, hallucinations, and wasted time iterating. Good context engineering means AI systems understand your domain, follow your standards, and produce usable outputs on the first try.
Consider a simple example: asking AI to draft a customer email. Without context, you get generic corporate speak. With context engineering - your brand voice, past successful emails, customer history, specific guidelines - you get something you can actually send.
This connects directly to several concepts we've covered in previous issues. Claude Skills (Issue #5) are essentially packaged context engineering - you define the context once, and it's automatically available when needed. The "Think Big" principle we're exploring this issue requires context engineering at scale: how do you give AI enough context to understand bold visions, not just optimize incremental improvements?
The emergence of tools specifically designed for context engineering signals its growing importance. ChatGPT and Claude Projects let you attach documents, conversation history, and custom instructions that persist across sessions. Their memory function serves similar purposes.
Context Engineering matters most in three scenarios:
Specialized Domains: When working in fields with specific terminology, standards, or approaches, context engineering is the difference between useful and useless outputs. Medical professionals need AI that understands their specialty's conventions. Engineers need systems that follow their team's architectural patterns.
Brand Consistency: Companies building AI-powered customer experiences need context that maintains voice, follows guidelines, and aligns with values across thousands of interactions. You can't manually review everything, so the context must be right from the start.
Complex Workflows: When AI handles multi-step processes, context engineering ensures continuity. The system remembers decisions made in step one when executing step five. Deliberate context engineering makes the experience coherent.
The future belongs to organizations that treat context engineering as seriously as they treated database design in the previous era. Because in AI systems, context is the new database - the foundation that determines what's possible.
For a 16-minute primer on Context Engineering, watch the video below from Shaw Talebi: