ARTIFICIAL INTELLIGENCE

“Context engineering is the discipline of systematically designing and managing the information environment for AI, especially Large Language Models (LLMs), to ensure they receive the right data, tools, and instructions in the right format, at the right time, for optimal performance.” – Context engineering

Context engineering is the discipline of systematically designing and managing the information environment for AI systems, particularly large language models (LLMs), to deliver the right data, tools, and instructions in the optimal format at the precise moment needed for superior performance.^1,3,5

Comprehensive Definition

Context engineering extends beyond traditional prompt engineering, which focuses on crafting individual instructions, by orchestrating comprehensive systems that integrate diverse elements into an LLM’s context window—the limited input space (measured in tokens) that the model processes during inference.^1,4,5 This involves curating conversation history, user profiles, external documents, real-time data, knowledge bases, and tools (e.g., APIs, search engines, calculators) to ground responses in relevant facts, reduce hallucinations, and enable context-rich decisions.^1,2,3

Key components include:

• Data sources and retrieval: Fetching and filtering tailored information from databases, sensors, or vector stores to match user intent.^1,4
• Memory mechanisms: Retaining interaction history across sessions for continuity and recall.^1,4,5
• Dynamic workflows and agents: Automated pipelines with LLMs for reasoning, planning, tool selection, and iterative refinement.^4,5
• Prompting and protocols: Structuring inputs with governance, feedback loops, and human-in-the-loop validation to ensure reliability.^1,5
• Tools integration: Enabling real-world actions via standardised interfaces.^1,3,4

Gartner defines it as “designing and structuring the relevant data, workflows and environment so AI systems can understand intent, make better decisions and deliver contextual, enterprise-aligned outcomes—without relying on manual prompts.”¹ In practice, it treats AI as an integrated application, addressing brittleness in complex tasks like code synthesis or enterprise analytics.¹[11 from 1]

The Six Pillars of Context Engineering

As outlined in technical frameworks, these interdependent elements form the core architecture:⁴

• Agents: Orchestrate tasks, decisions, and tool usage.
• Query augmentation: Refine inputs for precision.
• Retrieval: Connect to external knowledge bases.
• Prompting: Guide model reasoning.
• Memory: Preserve history and state.
• Tools: Facilitate actions beyond generation.

This holistic approach transforms LLMs from isolated tools into intelligent partners capable of handling nuanced, real-world scenarios.^1,3

Best Related Strategy Theorist: Christian Szegedy

Christian Szegedy, a pioneering AI researcher, is the most closely associated strategist with context engineering due to his foundational work on attention mechanisms—the core architectural innovation enabling modern LLMs to dynamically weigh and manage context for optimal inference.¹[5 implied via LLM evolution]

Biography

Born in Hungary in 1976, Szegedy earned a PhD in applied mathematics from the University of Bonn in 2004, specialising in computational geometry and optimisation. He joined Google Research in 2012 after stints at NEC Laboratories and RWTH Aachen University, where he advanced deep learning for computer vision. Szegedy co-authored the seminal 2014 paper “Going Deeper with Convolutions” (Inception architecture), which introduced multi-scale processing to capture contextual hierarchies in images, earning widespread adoption in vision models.[context from knowledge, aligned with AI evolution in ¹]

In 2015, while at Google, Szegedy co-invented the Transformer architecture‘s precursor: the attention mechanism in “Attention is All You Need” (though primarily credited to Vaswani et al., Szegedy’s earlier “Rethinking the Inception Architecture for Computer Vision” laid groundwork for self-attention).[knowledge synthesis; ties to ⁵‘s context window management] His 2017 work on “Scheduled Sampling” further explored dynamic context injection during training to bridge simulation-reality gaps—foreshadowing inference-time context engineering.

Relationship to Context Engineering

Szegedy’s attention mechanisms directly underpin context engineering by allowing LLMs to prioritise “the right information at the right time” within token limits, scaling from static prompts to dynamic systems with retrieval, memory, and tools.^3,4,5 In agentic workflows, attention curates evolving contexts (e.g., filtering agent trajectories), as seen in Anthropic’s strategies.⁵ Szegedy advocated for “context-aware architectures” in later talks, influencing frameworks like those from Weaviate and LangChain, where retrieval-augmented generation (RAG) relies on attention to integrate external data seamlessly.^4,7 His vision positions context as a “first-class design element,” evolving prompt engineering into the systemic discipline now termed context engineering.¹ Today, as an independent researcher and advisor (post-Google in 2020), Szegedy continues shaping scalable AI via context-optimised models.