What Is an Example of Context in AI?

Context in AI is the surrounding information - conversation history, user attributes, domain knowledge, metadata - that helps an AI system interpret inputs and produce relevant outputs. Without context, the same question gets the same generic answer regardless of who asks it or why. For a broader look at what makes AI systems context-aware, see what is context AI.

A straightforward example: when you ask an AI assistant "What is our revenue?", context determines whether it returns company-wide ARR, a regional quarterly figure, or a product-line breakdown. The question is identical each time. The context - your role, your department, the conversation so far, the data definitions in your organization - shapes the answer.

Linguistic Context: Resolving Ambiguity

The most basic form of context in AI is linguistic context - the surrounding words that disambiguate meaning. The word "bank" means a financial institution in "I deposited money at the bank" and a riverbed in "we sat on the river bank." NLP systems use the words around an ambiguous term to select the correct interpretation.

Pronoun resolution works the same way. In "The analyst sent the report to the VP, and she approved it within an hour," the system must determine that "she" refers to the VP, not the analyst. It does this by examining syntactic structure and semantic plausibility.

In enterprise settings, linguistic context extends to business terminology. A business glossary serves as a form of linguistic context for AI - it tells the system that "customer" in the CRM means "active paying account" while "customer" in the support system includes free-tier users. Without this definitional layer, the AI treats both as identical and produces wrong counts.

Conversational Context: Multi-Turn Memory

Conversational context is what allows an AI to handle follow-up questions without making the user repeat themselves. Consider an enterprise data query:

• User: "Show me Q3 revenue."
• AI: Returns a company-wide Q3 revenue chart.
• User: "Break it down by region."
• AI: Segments the same Q3 revenue data by region - without the user restating "Q3 revenue."
• User: "Compare to last year."
• AI: Adds a year-over-year comparison to the regional Q3 breakdown.

Each follow-up builds on the conversational context from prior turns. Without it, "Break it down by region" is meaningless - the system does not know what "it" refers to. Without "Compare to last year," the system does not know which metric and which regions to compare.

Domain Context: Industry-Specific Knowledge

Domain context is the specialized knowledge that tells an AI how to interpret inputs within a specific field. A medical AI that encounters "chest pain" in a 65-year-old patient with hypertension and diabetes treats it very differently from the same symptom in a 25-year-old athlete after a workout. The domain context - patient history, risk factors, clinical guidelines - shapes the assessment.

The same principle applies to enterprise data. When an AI queries a data catalog and encounters the term "customer," domain context tells it:

• In the CRM system, "customer" means an active paying account with a signed contract
• In the support system, "customer" includes free-tier users who submitted a ticket
• In the marketing database, "customer" means anyone who completed a form, including prospects

Without domain context from a business glossary, an AI asked "How many customers do we have?" will pick whichever table it finds first. The number could be off by an order of magnitude depending on which system's definition it uses.

Metadata as Context for Enterprise AI

In enterprise settings, the most impactful form of context is metadata: column descriptions, data lineage, freshness timestamps, ownership information, and quality scores. This is the context that determines whether an AI agent produces grounded answers or hallucinates definitions.

Consider an AI agent asked to build a quarterly business review. Without metadata context, it pulls numbers from the first matching tables it finds. Some numbers come from a staging environment. One metric uses a deprecated calculation method. The "headcount" figure includes contractors in one source and excludes them in another.

With metadata context, the same agent checks each table's lineage, verifies it is production-grade, confirms the calculation method matches the business glossary, and flags the headcount inconsistency for human review. The model is the same in both runs; only the available context changed.

This is why RAG architectures combined with governed metadata are a common way to make enterprise AI more reliable. Research supports the general point that retrieval can stand in for scale: DeepMind's RETRO model performed comparably to GPT-3 on the Pile benchmark with about 25 times fewer parameters by retrieving from a large text database (Borgeaud et al., 2021). Retrieval only helps, though, when what it retrieves is accurate and correctly defined.

What Happens When Context Is Missing

Missing context in AI produces failures that range from awkward to expensive.

Sentiment misread. A customer writes "That's sick!" in a product review. Without context about the user's demographic and the product category (streetwear), the AI flags it as a negative review. The marketing team removes a five-star rating.

Wrong metric, confident delivery. An executive asks an AI copilot "What is our revenue this quarter?" The model returns gross revenue from the accounting system instead of ARR from the billing system - the metric the executive actually uses. The number is $2M higher than expected. The executive quotes it in a board meeting. The CFO corrects the record publicly.

Cross-system confusion. An AI agent is asked to compare customer counts between the CRM and the support system. Without context about how each system defines "customer," it reports a 40% discrepancy. A junior analyst spends two days investigating a data quality issue that does not exist - the definitions are simply different.

Each of these failures traces to the same root cause: the AI lacked the context understanding needed to interpret the data correctly. A larger model would not have fixed any of them; clearer context would.

How Dawiso Provides Context to AI

Dawiso's Context Layer makes catalog metadata available to AI agents through the Dawiso MCP server. Any MCP-compatible agent can search catalog objects, read object details (attributes, linked glossary concepts and the people related to an object), trace data lineage and preview data samples. Write tools are available too, gated by the user's role. Instead of building a custom context pipeline for each AI tool, teams can connect their agents to one governed catalog.

When an AI copilot encounters the query "What is our churn rate?", it can look up the glossary term in Dawiso and read the organization's definition, for example monthly logo churn for paid SaaS accounts, excluding free-tier users. It can see who is responsible for the term and follow lineage back to the billing system tables it is calculated from. With that context, the AI can give a grounded, specific answer instead of a textbook definition or a guessed number.

The result is AI that can work with what data means in your organization as well as where it lives. That context is what moves enterprise AI from a convincing demo toward a tool people can rely on.

Sources

• Gartner, Gartner Says More Than 80% of Enterprises Will Have Used Generative AI APIs or Deployed Generative AI-Enabled Applications by 2026, press release, October 11, 2023.
• Borgeaud et al., Improving language models by retrieving from trillions of tokens, arXiv:2112.04426, 2021 (RETRO).
• Dawiso Help, Dawiso Model Context Protocol (MCP).
• Dawiso Help, Business glossary.