What is context engineering?
An AI model knows a great deal about the world and nothing about your business. Every useful answer depends on the context it's given: which data, which definitions, which rules, and which tools.
Prompt engineering is about how you ask. Context engineering is about what the model knows when it answers. For business work, the second usually matters more.
How does context engineering work?
- Definitions. What each metric means and how it's calculated, so the model uses the company's math.
- Data. The current, relevant records for the question, retrieved from governed sources.
- Rules. Policies and guardrails, like brand guidelines or what must go to a person for approval.
- Tools. The actions the model is allowed to take, and the permissions they run with.
- Limits. Only what the task needs, since extra context can make answers worse.
What is an example of context engineering?
Two answers to the same ROAS question
Asked for ROAS by channel, an assistant with no business context picks its own formula and its own date range. The answer looks confident and matches no report in the company.
Given the company's definition of ROAS, its channel taxonomy, and its fiscal calendar, the same assistant returns the figure finance would calculate. Only the context changed.
Context engineering vs. prompt engineering vs. fine-tuning
| Prompt engineering | Context engineering | Fine-tuning | |
|---|---|---|---|
| What it changes | How the request is worded | What the model knows at answer time | The model's weights |
| Updates when data changes | No | Yes, at every request | Only after retraining |
| Best for | Tone and format | Accurate, current business answers | Specialized style or tasks |
Why does context engineering matter?
Most wrong AI answers in business aren't the model's fault. The model had the wrong data, an outdated definition, or no definition at all. Getting the context right is the most direct way to make AI answers hold up.
How does Alli do this?
Alli Semantic Layer, part of Alli by PMG, is where marketing business logic is defined once: shared KPI definitions, harmonized naming across every platform and region, and a common data dictionary. Every team, tool, and AI agent in Alli works from those definitions, so AI stays grounded in the logic teams already agreed on.
Alli Data supplies the governed, current data underneath, and Alli4u skills package the instructions and tools an assistant needs for a specific job.
Explore Alli Semantic LayerContext engineering FAQ
How is context engineering different from prompt engineering?
Prompt engineering shapes how a request is worded. Context engineering decides what information, definitions, rules, and tools the model has when it answers, which usually matters more for business accuracy.
Is RAG a form of context engineering?
Yes. Retrieval-augmented generation fetches relevant documents or data and adds them to the model's context. Context engineering also covers definitions, rules, and tools.
Can too much context hurt AI answers?
Yes. Extra, irrelevant information can distract a model and make answers less accurate. Good context is limited to what the task needs.
How does a semantic layer help AI?
It gives AI the company's own metric definitions and naming, so the model calculates numbers the way the business does instead of guessing.
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