How do I give AI enough context without putting everything in the prompt?

Summary

• Large prompts waste tokens, slow down responses, and can confuse models by mixing signal with noise. Every piece of context added reduces the model's focus on what actually matters.
• AI agents and models need curated, ranked context that identifies the most relevant and authoritative information for each question, not a data dump.
• Separate governed definitions (metrics, entities, relationships) from inferred knowledge learned from your organization's actual work (queries, dashboards, notebooks, agent interactions).
• Rank context by relevance, authority, freshness, and usage patterns so the model receives only the most trustworthy and pertinent information for the question.
• Let the model request context on demand instead of pre-loading everything. This improves speed, reduces token waste, and lets the agent reason through what it needs to know.

How do I give AI enough context without putting everything in the prompt?
Giving AI enough context without bloating the prompt is a fundamental challenge in agent design. Too much context wastes tokens, slows inference, introduces noise that confuses the model, and dilutes the signal that matters. Too little and the model hallucinates or gives inaccurate answers.
Most organizations treat this as a binary choice: embed everything or provide nothing. Effective AI systems use a better approach: a curated, ranked ontology of semantic definitions; inferred knowledge learned from your actual work; and retrieval that pulls in only what's relevant and trustworthy for each query.

What is semantic context for AI?

Semantic context is curated business meaning between raw data and the AI model. It includes governed definitions of metrics (what "revenue" means), entities (which tables are authoritative for customers), and relationships (which definition applies for which question).
Raw data alone is useless to an agent. A model sees table names (fct_revenue_daily, customer_facts) but has no idea which is the source of truth, what three revenue columns mean, whether "active customer" means logged in or paid, or how to reconcile conflicting definitions. Semantic context bridges this gap by providing curated, high-confidence definitions the model can trust.

Key capabilities to evaluate

Capability	Why it matters
Unified semantics	Definitions should live in one place (your data governance layer) so agents, SQL, and apps use the same definitions.
Learned context	Queries, dashboards, notebooks, and agent interactions teach the system about relationships, usage patterns, and emerging concepts.
Relevance ranking	Rank context by freshness, usage, certification, and semantic similarity to the query, not by alphabetical order.
Authority signals	Distinguish certified definitions from inferred knowledge. Let the model know which sources are trustworthy.
Permission-aware	Never return context the user can't access. Definitions must respect role-based access control.
On-demand retrieval	Let agents request context as needed, not pre-load everything. This saves tokens and lets the model reason through what it needs.

How Databricks approaches contextual AI

Databricks Genie Ontology provides trusted business understanding for AI agents. It combines governed semantics from Unity Catalog (certified metrics, entities, relationships) with context automatically learned from your organization's data, analytics, and work patterns.
Genie Ontology learns continuously from queries, dashboards, notebooks, and agent interactions to infer concepts, usage patterns, and expertise. It ranks context by relevance, authority, and freshness. When an agent answers a question, it receives curated, focused context ranked by what's most relevant and trustworthy, not a data dump. The ontology enforces permissions so the model never receives context the user shouldn't see.
This lets agents deliver accurate, consistent answers faster at lower cost, because the model spends tokens on reasoning, not disambiguating conflicting definitions. Multiple agents and teams share the same governed business understanding, so all systems use consistent definitions.

FAQs

How do I avoid hallucinations when context is sparse?

Hallucinations happen when models lack trusted context to anchor reasoning. Genie Ontology ranks context by authority: certified definitions are marked high-confidence, inferred knowledge is tagged as such. The model distinguishes "source of truth" from "pattern I learned." You can configure agents to refuse to answer if context is missing.

What if I don't have formal definitions yet?

Start with continuous learning: let Genie Ontology infer concepts from your queries, dashboards, and work. As patterns emerge, certify the most important definitions in Unity Catalog. You don't need a perfect dictionary to begin. Over time, your context layer grows more curated.

How much of my prompt should be context?

Context should be a modest portion of your prompt. If it dominates, you're likely dumping raw data instead of curated definitions. Use semantic context ("revenue metric is calculated as..."), not raw schemas.

Can multiple teams use the same ontology?

Yes. Genie Ontology is shared across all agents, apps, and users. Each user sees only definitions they can access. This ensures consistent answers across teams.