Haiku 5.5 SQL drafting

Haiku 5.5 SQL drafting should begin with a schema and an expected result, not a connection to a production database. The model can propose a query, but the application must decide whether that query is valid, authorized and affordable to run. A syntactically plausible SELECT can still return the wrong customers or expose another tenant's data.

This page uses a synthetic reporting task with no live database connection. You can compare query drafts as text in the site's workspace. The website does not execute SQL or inspect your database, and the example below is not a measured accuracy result.

Describe the data model before the question

Supply table names, columns, key relationships and the relevant meaning of each status. A field called amount may represent gross charges, net revenue or an amount in minor currency units. Without that definition, a query can be internally consistent and still answer the wrong business question.

For Haiku 5.5 SQL, specify the dialect and timezone convention. Date functions, quoting and pagination differ across database engines. A query written for one dialect should not be treated as portable because its SELECT and WHERE clauses look familiar.

Include the authorization boundary in the task definition. If the report belongs to one tenant, identify the required tenant filter and how the trusted application supplies it. Do not let a natural-language request expand the accessible scope simply because the user asks for all customers.

Use a fixture with a duplicate-counting trap

Consider three synthetic tables: customers, orders and order_items. Each order belongs to one customer, while one order can contain several items. The task is to count paid orders per customer during a specified UTC interval, including customers who placed no paid orders.

A Haiku 5.5 SQL draft that joins items and counts rows can overcount orders. A draft that places an order-status condition in the wrong part of a left join can drop customers with zero matching orders. These are semantic defects even when the database accepts the query.

Define expected rows before seeing the output. Give one customer two paid orders with several items, another only a canceled order and a third no orders. The expected counts are two, zero and zero. This tiny fixture exposes both multiplication and missing-zero-row problems without real customer data.

For this Haiku 5.5 SQL input, use PostgreSQL, tenant A, start_utc 2026-10-01T00:00:00Z and end_utc 2026-11-01T00:00:00Z. These temporary-table statements are an authored fixture, not instructions to modify production data. Copy them as model context; this website does not execute them.

CREATE TEMP TABLE customers (
  tenant_id text, customer_id integer, name text,
  PRIMARY KEY (tenant_id, customer_id)
);
CREATE TEMP TABLE orders (
  tenant_id text, order_id integer, customer_id integer,
  status text, created_at timestamptz,
  PRIMARY KEY (tenant_id, order_id)
);
CREATE TEMP TABLE order_items (
  tenant_id text, item_id integer, order_id integer,
  PRIMARY KEY (tenant_id, item_id)
);
INSERT INTO customers VALUES
  ('A', 1, 'Ada'), ('A', 2, 'Bo'), ('A', 3, 'Cy'), ('B', 1, 'Dee');
INSERT INTO orders VALUES
  ('A', 101, 1, 'paid', '2026-10-01T00:00:00Z'),
  ('A', 102, 1, 'paid', '2026-10-20T12:00:00Z'),
  ('A', 103, 2, 'canceled', '2026-10-21T12:00:00Z'),
  ('A', 104, 1, 'paid', '2026-11-01T00:00:00Z'),
  ('B', 201, 1, 'paid', '2026-10-02T12:00:00Z');
INSERT INTO order_items VALUES
  ('A', 1, 101), ('A', 2, 101), ('A', 3, 102),
  ('A', 4, 103), ('A', 5, 104), ('B', 1, 201);

The Haiku 5.5 SQL answer key is Ada: 2, Bo: 0 and Cy: 0, ordered by customer_id. Exclude Dee and order 104; include order 101 at the interval start. Repeated customer IDs across tenants deliberately test whether joins preserve tenant scope. Do not include this answer key when testing discovery of the mistakes.

Ask for a query and its assumptions

The following authored brief requests a read-only PostgreSQL draft. It does not authorize execution. Supply the actual synthetic schema alongside it, including the tenant key and timestamp type, so the model does not have to invent missing columns.

Draft a read-only PostgreSQL query for the supplied schema.
Count paid orders per customer in [start_utc, end_utc).
Include customers with zero matching paid orders.
Restrict all rows to the trusted tenant parameter.
Use bound parameters, not string concatenation.
Do not join order_items unless the requested calculation needs it.
Return the query, assumptions and expected results for the supplied fixture.
Do not execute it or claim that it was tested.

For Haiku 5.5 SQL review, compare the model's stated assumptions with the provided schema. A helpful-looking query that invents a payment table or assumes a missing column is not ready to run. Resolve those questions explicitly rather than adding guessed fields to production data structures.

Inspect joins and aggregation first

Trace the row count after each join. Ask which table establishes the intended unit of analysis: customers, orders or items. If the unit changes, the aggregation must account for it deliberately. DISTINCT can sometimes mask a multiplication problem while leaving other sums wrong.

Haiku 5.5 SQL acceptance should include the zero-match case and duplicate relationships, not only a customer with one simple order. Check whether predicates preserve the intended outer join. Review NULL handling separately from an actual numeric zero; they may represent different states in the report.

Then inspect interval boundaries. A half-open range includes the start and excludes the end, which can help adjacent reporting periods avoid overlap. The important point is consistency with the stated contract, not assuming one boundary convention is right for every business report.

Keep parameter binding in application code

Treat generated SQL as an untrusted draft. Values supplied by a user should be bound through the database driver's parameter mechanism, not interpolated into a query string. A model's instruction to use parameters is useful, but the executed application path must actually enforce it.

For Haiku 5.5 SQL tools, keep identifiers such as table or column choices on an application-owned allowlist. Value binding does not make an arbitrary generated identifier safe. A reporting assistant should not gain access to every table merely because it can produce a syntactically valid name.

Apply least-privilege credentials and resource limits outside the model. Read-only access reduces some risks but does not prevent expensive queries or inappropriate disclosure. Tenant isolation, row permissions, timeouts and result-size limits are application and database responsibilities, not prompt-only promises.

Validate in a disposable environment

Run the draft only in an authorized test environment containing the synthetic fixture. Compare the actual rows with the expected rows, including ordering when the consumer depends on it. A successful query execution is only the syntax and runtime check; the row comparison tests the business meaning.

A Haiku 5.5 SQL test should also include empty tables, repeated timestamps and a customer from another tenant. Those cases expose assumptions that a single happy-path example misses. Keep the fixture small enough that a reviewer can calculate the expected result independently.

Inspect the query plan when performance matters, using the database's documented tools and an appropriate environment. Do not run an expensive analysis command on production data casually. A correct result on ten rows does not establish acceptable behavior on a large table with different indexes or distributions.

For Haiku 5.5 SQL regression checks, retain the fixture's schema and inserts with the expected result table. Recreate them in a disposable database for each run so old test rows cannot alter the answer. When comparing decimal amounts, use the business rule for units and rounding instead of accepting a visually similar floating-point value. The fixture should also document whether canceled and refunded orders are excluded, since those categories can affect a report differently even when both appear to be unsuccessful purchases.

Compare repair effort, not query length

Use the same schema, fixture and task for each candidate model. Score correct result rows, authorization preservation, unnecessary joins and reviewer corrections. A shorter query is not automatically better if it omits zero-count customers or relies on an unstated assumption.

In Haiku 5.5 SQL comparisons, retain the initial draft and every repair. Otherwise a final correct query can conceal several failed attempts and substantial human intervention. Cost per accepted report should include that work rather than only the last successful generation.

Keep some fixtures hidden from prompt tuning. If every example contains exactly two paid orders, the model may produce an answer that fits the demonstration without representing the actual relationship correctly. Vary counts and status combinations while preserving the same schema contract.

Separate query drafting from execution authority

A production system should make the execution decision after validation, not while the model is still streaming SQL tokens. Record the approved query or query template and the trusted parameters. Do not execute a partial response or let a later continuation modify an already approved statement.

Start Haiku 5.5 SQL use with reviewed reporting drafts before considering automated access. When a question requires a write, schema migration or privileged operation, route it through a separate authorized workflow. The function-calling guide explains tool authorization, while data extraction covers producing validated records without granting database access.