From Prompt Engineering to Context Engineering

Why building effective AI systems now depends less on clever prompts and more on how we design, curate, and manage context.

From Prompt Engineering to Context Engineering

Designing AI systems that work in the real world

Over the last two years, most AI conversations I have had with clients, teams, and leaders have revolved around one idea: prompt engineering. How to phrase instructions, how to guide the model, how to get better answers.

That phase was necessary. It helped many organisations take their first meaningful steps with large language models.

But as AI moves from experimentation into products, workflows, and operational systems, a different challenge is emerging. The limitation is no longer the model or the prompt. It is the context we give the model to operate within.

This shift from prompt engineering to context engineering is not theoretical. It is already shaping how effective AI systems are built today.

Why prompt engineering stops working at scale

Prompt engineering assumes a simple interaction: a system prompt, a user message, and a response. That works well for single-turn queries or lightweight conversations.

Real business use cases look very different.

AI systems are now expected to:

At that point, the problem is no longer how well the prompt is written. The problem is whether the model is seeing the right information at the right time.

This is where most AI initiatives begin to feel fragile or unpredictable.

What context engineering actually means

Context engineering is the practice of deliberately designing what information enters the model’s context window for each step of a task.

It is not about giving the model everything you have. It is about curation.

Based on Anthropic’s work on effective context engineering for AI agents, the key idea is simple: the model should only see what is useful for the decision it is about to make.

That context can include:

The important part is that this selection happens repeatedly. Context is not static. It is assembled, filtered, and refined at each step.

From conversations to environments

One of the biggest mental shifts is understanding that advanced AI systems are not conversations. They are environments.

In a well designed system, there is a pool of possible context sources. Documents, tools, memory files, and instructions exist outside the model. Before each model call, a curated subset is selected and passed in.

On the one hand you have prompt engineering: a narrow, one-off interaction. On another you have context engineering: a system that continuously decides what the model should see and what it should not.

Practical context engineering decisions

In practice, context engineering comes down to a series of concrete design choices.

First, decide what information should be persistent. Things like business rules, safety constraints, or role definitions usually belong in the system prompt or long-term memory.

Second, decide what should be retrieved dynamically. Documents, policies, or customer data should be fetched only when relevant to the task.

Third, decide how history is handled. Full transcripts are rarely useful. Summaries that capture decisions, constraints, and outcomes are often far more effective.

Finally, decide when tools are available. Exposing every tool all the time adds noise and confusion. Tools should be introduced only when they are needed.

None of these decisions involve clever wording. They involve system thinking.

Why this matters for organisations

Many of the issues teams experience with AI are symptoms of poor context engineering.

Inconsistent outputs.
Hallucinations despite correct data.
Models ignoring constraints.
Agents that forget what they just learned.

These are not prompt problems. They are context problems.