Enterprises have begun to discover what the generative AI hype can obscure: large language models are convincing but inconsistent unless fed the right data. Markets move on data and analysis; a misplaced figure, a stale disclosure, or a hallucinated data point can make the difference between sound judgment and costly error.
That’s why the true differentiator in enterprise-grade generative AI isn’t style, but substance – specifically, context engineering: the structuring, selection and delivery of the right data into an AI system’s context window at the right moment. Without it, models are more likely to hallucinate, miss critical signals or provide generic answers unfit for high-stakes decision-making.
One way to test an AI platform’s reliability is with the ‘needle in a haystack’ benchmark, which measures how well a model can retrieve a precise fact buried inside irrelevant text. Enterprises face this challenge at scale as they process thousands of documents – from regulatory updates and disclosures to analyst notes and market feeds.
Critical insights are often buried deep within this flood of text. Faced with an ever-growing haystack of data, the smarter approach isn’t more frantic searching, but a system that organises, filters and prioritises the hay before the search even begins.
Introducing context engineering
AI models tend to be generalists, drawing on vast but frozen training data. Context engineering addresses this by supplying a model with the right information in the right way at the right moment. Without engineered context, even the most advanced system may cite outdated facts, hallucinate plausible but false information, or provide generic answers that lack real substance. With this discipline, a general-purpose model can be made domain-specific – not because its architecture changes, but because its working memory is carefully curated and aligned to the task at hand.
Expanding a model’s context windows – the model’s limited working memory – has been one way to tackle the problem, but bigger is not always better. Longer windows are costly, spread attention too thin and are more likely to degrade the model’s ability to focus on what matters most. Thus, the context window remains limited prime real estate, which makes careful selection and engineering of inputs far more important than sheer size.
The practice of context engineering starts by transforming messy enterprise data – from scanned PDFs and tables to transcripts and free-form notes – into structured, machine-readable formats. Retrieval systems then surface the most relevant fragments, while information chunking and indexing help ensure outputs stay accurate and on purpose through built-in safeguards. Additionally, continuous evaluation loops test responses for accuracy, grounding and relevance. The process is ongoing, as context requirements shift with changing data and user needs.
Prompting, context and the new AI stack
The first wave of enterprise AI focused heavily on prompts. Teams learned to ask for outputs in certain ways, such as, “act as an analyst,” “summarise in plain English” or “explain in bullet points”. These cues help shape tone and presentation, but asking AI to summarise an earnings call is meaningless if the transcript supplied is outdated or irrelevant.
A good metaphor is a car. Prompts are the driver – they set intent and direction, telling the system where to go. But a driver without fuel will not get anywhere. Context is the fuel – the refined input that powers the engine, giving the system the energy to act. And fuel quality matters. High-grade, well-prepared context keeps the system running smoothly; stale, noisy, or irrelevant context leads to inefficiency and breakdowns.
This means the AI stack must evolve. Prompts are an important layer, but not sufficient on their own. Enterprises also need context engineering – pipelines that transform raw data and retrieval systems that decide which fragments to surface. Evaluation frameworks then score outputs, while governance structures ensure transparency. This is the machinery beneath the surface, and without it, enterprise AI might not be able to move from proofs-of-concept to reliable deployment.
The rise of agentic AI will make this even more critical. These systems will not just answer questions – they’ll execute workflows and reason across multiple steps. Their success will depend on the reliability of the context they retrieve at each stage. And governance pressures are likely to intensify the demand for transparency: users and regulators alike want to know not only what the model said, but why it said it. Provenance and auditability of inputs may soon matter as much as the fluency of outputs.
The power of context
Enabling enterprise AI to succeed requires more than prompts. Context engineering is a foundation that makes models reliable, consistent and useful at scale.
Prompts set the direction, but context fuels the journey, determining both how far AI can go and how much confidence decision-makers can place in its answers. Enterprises that master both may help define the next era of intelligent systems.
