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GlossaryWhat is Context Engineering?
Context Engineering decides which information an AI model receives before it answers. It is the difference between an AI that guesses and an AI that answers from your own, verified enterprise knowledge.
In short
Context Engineering is the practice of selecting and preparing the right enterprise context for an AI model before it answers: a small set of highly relevant, pre-verified facts, within the permissions of the user. Mindbreeze InSpire does this with a semantic pipeline, a relevance model, RAG and fact extraction before any data reaches the LLM. The result is lower token consumption, faster responses and a lower risk of hallucinations.
On this page
What exactly is Context Engineering?
A language model only knows what it was trained on and what you give it with the question. Context Engineering is about that second part: which documents, facts and data the model receives, in which form and in which order.
Prompt engineering is about how you phrase a question. Context Engineering is about what the model knows when it answers. For enterprise AI the second matters most: the best prompt cannot fix an answer based on an outdated contract or on a document the user should not see.
Why Context Engineering matters
Fewer hallucinations
The model answers from verified facts instead of filling gaps with plausible guesses.
Lower cost
A small set of relevant facts uses far fewer tokens than sending whole documents.
Faster answers
Less input means shorter response times.
Permissions respected
Only information the user may open ends up in the context.
Traceable answers
Every answer can be traced back to its sources.
Freedom of model
Good context works with the model you choose, from Copilot to your own agents.
How does Context Engineering work?
- Step 1
Connect
Bring structured and unstructured information together from M-Files, SharePoint, ERP, CRM and mail, with existing permissions preserved.
- Step 2
Understand
Semantic analysis, classification and fact extraction turn documents into facts and relations.
- Step 3
Select
A relevance model picks the few facts that matter for this question and this user.
- Step 4
Answer
Retrieval Augmented Generation gives that context to the LLM or AI agent, which answers with sources.
Context Engineering, prompt engineering and RAG
| Approach | Focus | Result |
|---|---|---|
| Prompt engineering | How you phrase the question | Better instructions, same knowledge |
| RAG | Retrieving relevant documents before an answer is generated | Answers based on your documents |
| Context Engineering | Selecting, preparing and securing all context the model receives | Only relevant, verified and permitted facts |
Context Engineering with Mindbreeze and M-Files
Mindbreeze InSpire performs a semantic pipeline, relevance model, RAG and fact extraction before any data reaches the LLM. AI agents then work with a small set of highly relevant, pre-verified facts, not with all the noise around.
The quality of that context starts with the quality of your documents. In M-Files, documents carry metadata, versions and permissions, so the AI knows which contract is the signed version and who may see it. SoftAdvice designs that data foundation and connects the sources.
Frequently asked questions about Context Engineering
What is Context Engineering?
Context Engineering is the practice of selecting and preparing the right enterprise context for an AI model before it answers: a small set of highly relevant, pre-verified facts, within the permissions of the user.
What is the difference with prompt engineering?
Prompt engineering improves how a question is phrased. Context Engineering improves what the model knows when it answers: the facts, sources and permissions behind the answer.
Is Context Engineering the same as RAG?
No. RAG, retrieving relevant documents before generating an answer, is one technique within Context Engineering. Context Engineering also covers which sources are connected, how facts are extracted and ranked, and which permissions apply.
Why does Context Engineering lower costs?
Language models are billed per token. Sending a small set of relevant facts instead of whole documents reduces token consumption and response times.
What do I need to get started?
Well-structured documents with metadata and permissions, connected sources and a first use case. The AI Readiness Scan or a Discovery shows where you stand today.
Our consultants are happy to think along with you.
Plan a conversationIs your data ready for AI that answers with context?
In Discovery we look at your documents and sources first, and tell you what AI needs to give reliable answers.
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