The Rise of Context Engineering: Why It Is Replacing Prompt Engineering in AI
Each week through the Shape of Tomorrow newsletter and podcast, I examine the evolution of artificial intelligence. One shift has become increasingly clear: prompt engineering, once the dominant skillset for interacting with large language models, is being surpassed by a deeper and more powerful practice.
Context engineering.
Beyond Prompt Crafting: The New Discipline of Context Engineering
Prompt engineering has played a key role in how we engage with language models. It focused on wording instructions to coax better responses. As model capabilities have grown and their ability to process larger inputs has expanded, a limitation has become evident. Prompts alone cannot consistently generate high-quality output. Even well-written instructions fall short when the model lacks relevant background information.
Context engineering addresses this gap. It involves shaping what the model “knows” at any given moment. This includes instructions, examples, historical exchanges, documents, and media. Leaders in the field such as Andrej Karpathy and Tobi Lütke have emphasized that success increasingly depends on how well one curates and structures that context. It is not just about what is asked, but about everything surrounding that request.
Where Prompt Engineering Fails
Prompt engineering tends to assume that the model has a consistent baseline of understanding. In practice, this is rarely the case. Without structured input to define the situation, the model improvises, and not always in helpful ways.
Here are several core challenges:
Ambiguity vs. Specificity: Loose phrasing leads to poor results. Excessive precision limits flexibility.
Performance Variability: What succeeds with one model or task may falter with another.
Context Saturation: Detailed prompts often consume the space meant for essential background information.
Inconsistent Output: Subtle shifts in underlying context can alter responses unexpectedly.
Missing Expertise: Even strong prompts fail without deep, relevant information behind them.
Mastering the Context Window
The expanded capacity of today’s models introduces new complexity. Tools like GPT-4 can now process hundreds of pages in one exchange. This turns input selection into a critical task. Rather than asking, “How should I phrase my prompt?” the more relevant question is, “What information should the model be working from?”
Effective context engineering requires a combination of:
Technical understanding of model behavior and token limits
Strategic thinking to prioritize what matters
Information design to ensure relevance and clarity
Subject matter knowledge to select vital content
Analytical processes to iterate and refine approaches
Why This Shift Matters
AI is no longer a novelty, it is becoming a core component of daily operations. The real impact does not come from novelty prompts. It comes from systems that reflect your business, your challenges, and your ambitions.
Context engineering creates this alignment. It transforms language models into responsive and reliable contributors. It ensures consistency, preserves nuance, and builds toward goals rather than merely reacting to questions.
This discipline combines creative thinking, technical ability, and business fluency. For those serious about operationalizing AI, context engineering has already become the most valuable skill in the toolkit.
Let’s keep the conversation going. I’ll explore more on this in future issues, and I look forward to hearing your questions and reflections.