Definition
Context engineering
Structuring the inputs and memory an LLM receives. Ross Barnes's position is that the difference between reliable and hallucinatory output is almost always context, not model choice.
From the episode “From $80M To $1B: The Scaling Lesson That Now Applies To Every AI Workflow”How founders on the podcast use it
Structuring the information you feed an LLM so it produces useful output, a burden Clyde shifts off the user.
$250K and 3 Months to Build What Used to Cost $2 Million and 18 MonthsThe practice of structuring memory, history, and references so an AI system behaves consistently across sessions — the reason the same prompt can produce radically different results without it.
What It Do: 6 Parameters That Separate Cinematic AI From Total SlopHear it on the podcast3 episodes
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Related terms in AI Fundamentals
Computer visionThe branch of AI that lets computers interpret images and video, like recognizing objects, faces, or text.ConfabulationA human psychology term for the way memory reconstructs rather than records — people create plausible narratives around incomplete memories with no intent to deceive. Geoff draws the parallel to AI hallucinations: both are a system constructing a coherent story from incomplete information. The observation cuts both ways: AI hallucinations aren't as alien as they seem (human memory does the same thing), and human testimony is far less reliable than assumed (well-established in criminal justice research). Working closely with AI has become a useful mirror on human cognition.Constitutional LayerThe rules, values, and ethical principles baked into each agent at instantiation. The foundational constraint set that shapes all downstream behavior before any prompt is processed.Context ArchetypeA named configuration of rules governing how an AI generation system behaves in a specific mode — for example, 'creative exploration' vs. 'compliance review.' The same underlying model produces reliably different outputs depending on which archetype is active. In Knapsack's Intelligent Product Engine, archetypes are one of three components (alongside real-time artifact monitoring and a rules engine) that make AI generation predictable and enterprise-appropriate rather than open-ended and unpredictable.Context GroundingThe practice of providing an AI coding agent with accurate documentation of the existing codebase before asking it to generate or modify code. Without grounding, agents operate on generic code patterns and produce architecturally inconsistent output that may be technically correct in isolation but incompatible with the existing system. BrainGrid generates five grounding documents from the actual repository — architecture overview, key workflows, data structures, directory structure, API patterns — so the agent extends what's there rather than inventing new patterns.Context HallucinationA hallucination that occurs not from false information, but from failure to account for relationships between documents, where one document modifies, contradicts, or conditionally applies to another.Context windowHow much input, such as text, documents, or conversation history, an AI model can take into account at one time.Contrastive learningA training technique where a model learns by comparing similar and dissimilar examples, figuring out what makes things alike or different.