Editing Openai/691a41cd-2efc-800c-9eff-de439224a90d (section)

==== This is where it becomes lethal for “LLM + old database” pain. ====

===== Most attempts look like this: =====
# User: “Show me all customers who churned after a failed payment and then came back within 90 days.”
# System: Prompt an LLM with the DB schema.
# LLM: Generates SQL.
# DB runs it. Sometimes correct. Sometimes totally wrong.
# Next day, slightly different wording, the LLM generates different SQL with different bugs.

Problems:
* No persistent memory of the schema beyond each prompt.
* No guarantees on correctness or safety.
* No shared semantics across multiple DBs / services / logs.
* If you change schema, your prompt recipes break.

===== SAIQL is designed to be both: =====
# The “brain” language – what you or an AI thinks in about your data.
# The compiler – it translates SAIQL → SQL / NoSQL / vector / filesystem ops.

Workflow:
# You define your semantic mapping once: - “This customers table’s closed_at means the churn date.” - “This payments table’s status = 'failed' is a failed payment event.” - “This log stream + these docs are all related to incident reports.”
# SAIQL stores that mapping in LoreTokens (your semantic schema).
# When you ask a question like: >  SAIQL: - Knows which tables and fields correspond to those concepts. - Compiles a correct, tested SQL query (or multiple queries across systems). - Executes them deterministically. - Returns results back in a semantic structure (LoreTokens), which AI can then summarize, visualize, or act on.
# If your schema changes later (“we renamed closed_at to churned_at”): - You update the semantic mapping once. - All SAIQL queries that use the “customer churn” concept still work.

So SAIQL acts as a stable semantic API over unstable physical schemas.

===== Old way: =====
* Every team builds ad-hoc SQL, Python, and brittle prompt chains to interrogate EHR data or trade histories.
* Every time a column name or table structure changes, half your analytics break.

SAIQL way:
* “Encounter,” “diagnosis,” “medication order,” “trade,” “limit order,” “emergency stop,” etc. become semantic entities encoded as LoreTokens.
* SAIQL knows: - Which tables/fields implement those entities in each system. - How to convert from one system’s schema to another’s.

Then:
* An AI agent can ask SAIQL: >
* SAIQL compiles that into: - A set of SQL queries across multiple EHR tables - Possibly a vector search over clinical notes - Filters on lab results

But the AI never has to learn the raw schema. It just speaks SAIQL-level concepts.