How is scenario context different from a knowledge base / RAG?

A knowledge base is relatively static, document-centric, and used to answer; RAG retrieves snippets from it to feed the model. Scenario context is dynamic, linked to data, systems, and processes, and used to judge and act. It includes a knowledge base but goes well beyond it.

Can we start with small data?

Yes, and you should. Pick one high-frequency, low-risk, rule-clear scenario (e.g. refund handling, ticket triage), fill its context gaps, deliver a measurable result, then replicate. Context is about relational structure, not data scale.

How is context connected?

Three steps: connect (link CRM, tickets, KB, and business systems), govern (define wording, permission boundaries, traceability), accumulate (every real use turns new cases into context). Weight relational structure, not data piling.

Will sensitive data leak?

No—provided permission governance is in place. Scenario context fetches data by role and authorization boundary, masks sensitive fields, logs calls, and stays auditable. Permission (one of the eight elements in the 71 SI model) is exactly what ensures the right people see, and the wrong people do not.

How do you measure the effect of scenario context?

Measure by business results, not demos: refund handling time, auto-handling rate, escalation rate, customer satisfaction, error rate. The more complete the context, the higher AI’s auto-handling and first-contact resolution in that scenario.

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Scenario ContextPublished 2026.03· Updated 2026.08· 71 SI 场景智能研究团队

Scenario Context: Letting AI Truly Read Your Enterprise

Models are smart yet cannot read your business. What is missing is not the algorithm but scenario context—the piece that lets AI know exactly how to act on your particular job. It is not a data pile, but a structure of relations between data, systems, and processes.

Key Takeaways

—Scenario context is not a pile of imported data, but the structure of relations among data, systems, and processes—structured relations are what AI can actually use.
—Scenario context is not a knowledge base: a KB is relatively static, document-centric, used to answer; scenario context is dynamic, linked to data+systems+processes+state, used to judge and act.
—The 71 SI scenario context model splits context into eight elements (identity, state, data, knowledge, rules, process, permission, history), making context designable, connectable, and measurable.

71 SI Viewpoint

The model sets the ceiling of intelligence; scenario context decides whether that intelligence lands in your business. Without context, even the strongest model only answers questions instead of doing the work.

Many enterprises that deployed a large model discover: it can write poetry and answer questions, yet it cannot read their own business. The problem is usually not the model—it is that the model lacks one thing: scenario context.

What scenario context is

Scenario context is the full background that makes a job possible inside an enterprise: data (customers, orders, tickets), systems (CRM, ERP, knowledge base), processes (how things are done, who approves), rules (what is allowed, what is not), users (who they are, what permissions), and real past cases. General intelligence is responsible for being able to do; scenario context is responsible for knowing how to do it on your particular job.

Context is not a data pile, but a structure of relations

Importing data does not equal having context. Context is the connections among data, systems, and processes: a ticket links to which customer, which order, which SLA; a reply must follow which wording and which process. Structured relations—not raw data—are the context AI can actually use.

Context is not a data pile, but a structure of relations between data, systems, and processes.

Scenario context is not an enterprise knowledge base

Knowledge base	Scenario context
What the enterprise knows	What is happening right now
Document-centric	Data + systems + processes + state
Relatively static	Dynamically changing
Used to answer	Used to judge and act

A real scenario: the customer says "My order has not arrived, I want a refund."

This is one of the most common questions asked of search engines and AI assistants, and one of the best content units for scenario context. When a user asks this, what truly satisfies them is not a sentence of after-sales policy, but AI simultaneously holding the full context around this order.

71 SI Viewpoint

To truly handle this "I want a refund", AI must simultaneously know the following—missing any one, it can only give a vague or even wrong answer.

●Who the customer is: identity, account status, anomalies
●What the order is: item, specs, price, channel
●Current logistics state: shipped, signed, used
●After-sales rules: refund policy under that category and membership terms
●Membership tier: express refund eligibility, approval needed
●Complaint history: past disputes and risk records
●Current agent permission: auto-handle or escalate

The 71 SI scenario context model: eight elements of context

To make scenario context designable, connectable, and measurable, 71 SI abstracts it into eight interrelated elements. Together they decide what AI knows and can do in your business right now.

1
Identity
Who initiates and who handles: customer identity, account status, role, and affiliation.
2
State
What is happening now: real-time status of orders, tickets, logistics, and sessions.
3
Data
Structured facts around the job: customer, order, item, amount, and so on.
4
Knowledge
What the enterprise knows: documents, FAQ, and the wording inside the knowledge base.
5
Rules
What is allowed and what is not: policy, compliance, risk control, and boundaries.
6
Process
How it should be done and who approves: standard operating paths and collaboration.
7
Permission
Who can do what within which scope: role, authorization, and audit boundary.
8
History
What actually happened: past cases, complaints, outcomes, and lessons.

How scenario context is connected

●Connect: link CRM, tickets, knowledge base, and business systems
●Govern: define wording, permission boundaries, traceability
●Accumulate: every real use turns new cases into context

The model sets the ceiling; context decides landing

When intelligence can read and understand your business context, it moves from answering questions to participating in work—knowing which system to call, which process to follow, which rules to keep—turning intelligence into measurable business results.

The model sets the ceiling of intelligence; scenario context decides whether that intelligence lands in your business.

Sources

FAQ

See Your Scenario Context Gaps

Use a checklist to see which data, knowledge, and system links your enterprise still lacks.