Glossary
What is a Context Layer? (The Missing Piece in AI Analytics)
A context layer is the governed store of business meaning — definitions, rules, exceptions, and relationships — that an LLM needs in order to answer analytical questions without guessing. It sits on top of a semantic layer and underneath any AI-driven interface.
Why 'just dump more into the context window' is not the answer
When teams first try to make LLMs answer business questions, the default instinct is to shove schema, sample rows, and a prompt into the context window. This works for toy questions and fails on anything real.
A context layer is structured, governed, and reusable. It is not a prompt. It is a system of record for what the business means, which the LLM consults the way a new analyst would consult the team's runbooks on day one.
What lives in a context layer
Canonical metric definitions and their exceptions.
Entity and relationship descriptions written in business language.
Business rules that describe 'how we actually compute this here' — including the rules analysts never documented because they assumed everyone knew.
Ownership, review state, and provenance: who validated this, when, and why.
Why analysts have to own it
The context layer is only as good as the people maintaining it. That has to be the analysts and data team members closest to the business, not a centralized ML team and definitely not an outsourced data labeling vendor. AlchemData's Context Builder is designed around this principle: analysts remain the owners of the trust layer, and the rest of the organization consumes through it.
Related Resources
Glossary
What is a Semantic Layer? (And Why It Matters for AI Analytics)
A semantic layer is the governed translation between raw warehouse tables and the business questions people actually ask. In the AI era, it is the layer that decides whether an LLM hallucinates or answers correctly.
Glossary
Analyst-in-the-Loop: The Operating Model for Trusted AI Analytics
Human-in-the-loop is not a workflow feature. It is the operating model that lets AI analytics scale without losing trust.
Glossary
What is Trusted AI Analytics?
Trusted AI analytics means every answer is grounded, explainable, reviewable, and owned. Here is the working definition and what it takes to deliver it.