Jared Bowns is Head of Data and AI at Elyxor, helping enterprises turn emerging AI into scalable, real-world business value.
Large language models (LLMs) have evolved from novelty to necessity in boardrooms around the world. Executives now speak fluently about GPT, copilots and retrieval-augmented generation (RAG). But as organizations shift from pilot experiments to production deployments, a key question emerges: Why do some AI systems feel intuitive, trustworthy and productive, while others stall out in proof-of-concept purgatory?
The answer lies in a subtle but foundational discipline: context engineering.
Prompt Engineering Was Never the Endgame
When ChatGPT first captured public attention, a new cottage industry emerged around prompt engineering, which is the art of crafting precise text instructions to guide model behavior. This was a helpful and necessary step in the evolution of human-to-AI interactions, but it quickly showed its limits in real-world business environments.
Prompt engineering treats the model as a black box and focuses narrowly on inputs. It is tactical, session-bound and overall fragile. While it excels in experimentation, it struggles under the weight of enterprise demands like continuity, compliance, role awareness and dynamic internal data.
Context engineering, by contrast, focuses not just on the input but on what the model understands at the moment of interaction. It involves the deliberate design of memory, state, grounding data, business logic and role definition. Together, these elements transform a general-purpose chatbot into a capable, trusted copilot.
What Is Context Engineering?
Context engineering is the discipline of designing the environment in which an AI system operates. It spans multiple layers of system and process integration, including:
• Role And Goal Alignment: Clearly defining the AI’s function (for example, “junior financial analyst” versus “claims assistant”) to keep outputs relevant and consistent.
• Retrieval Frameworks: Connecting the model to internal data sources such as contracts, policies, emails and knowledge bases using RAG or semantic search.
• Session State And Memory: Preserving continuity by tracking prior inputs, decisions and user history.
• Security And Compliance Constraints: Respecting user permissions, data classifications and regulatory rules.
• Workflow Integration: Embedding AI into real business processes rather than isolating it in a sandbox.
This context stack does not replace prompts. Instead, it supports and enhances them with structure, relevance and operational rigor.
How Context Engineering Creates Business Value
Without context engineering, even the most advanced models behave like articulate interns with amnesia. They may be eloquent, but they are inconsistent and disconnected from the way your business operates. With the right context, those same models become reliable copilots that:
• Accelerate decision-making by delivering the right information at the right time
• Reduce risk by grounding answers in authoritative sources
• Improve adoption through role-specific, workflow-aware experiences
• Scale institutional knowledge without overburdening human experts
Consider how context engineering can shape outcomes with large language models. Imagine a global insurer deploying a generative AI claims assistant. If the system were designed with only prompt engineering, it could easily hallucinate policy details and require constant human oversight. By contrast, layering in structured retrieval from historical claim data, defining clear role instructions and enabling memory across sessions could provide the guardrails needed to keep the system on track.
The difference isn’t a better model; it’s better context.
From Cool Demo To Critical Infrastructure
A strategic approach to context engineering is what elevates generative AI from a demo to a dependable system. It marks the transition from general-purpose assistants to business-specific intelligence layers that understand context, history and intent.
Leaders should be asking:
• Who is responsible for designing our AI’s context stack?
• Which business systems are connected to the AI layer?
• How are we capturing and evolving institutional knowledge into usable context?
• What roles, permissions and workflows must our AI respect?
The Rise Of The Context Engineer
As AI becomes more deeply woven into business operations, the new role of context engineer is emerging as a key part of enterprise AI projects. These professionals bridge technical expertise and domain knowledge to ensure AI systems perform reliably and align with real-world workflows.
The companies seeing the most value from AI aren’t relying on superior models; they’re creating the right conditions for those models to succeed. As foundation models become increasingly commoditized, I believe competitive advantage will come from how well an organization engineers context.
In both AI and business, perspective is shaped by one’s position, and context gives that perspective its meaning. It is the foundation of intelligent action.
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