I. Introduction

Almost every data team has one or more pieces of “legacy SQL” in their codebase that no one wants to touch. Each is over one hundred or even hundreds of lines, with N layers of nested CTEs and window functions stacked on top of window functions. The person who wrote it left the company long ago. Comments are practically non-existent. No one dares to modify it or can fully make sense of it. Whenever the business requirements change even slightly, the person who takes over needs to spend hours untangling what each subquery does, makes changes, then spends hours debugging, terrified that changing one line might break the entire logic.

Even with AI programming, things don't improve much. Due to hallucinations, AI can only assist with interpretation, and ultimately, human engineers still have to validate the result. The AI output is essentially just another document, which will likely drift out of sync with the actual code over time. Worse still, the AI-regenerated SQL based on updated requirement often is completely different from the original. This not only requires time and effort for re-validation, but also adds another piece of “legacy SQL”.

What if we crystallize AI’s interpretation into a single artifact that serves both as a document and as compilable and runnable code?

This is what SQLazy scripts are for.

AI interprets SQL and translates it into a structured, readable, and verifiable SQLazy (*.nsql) script. Whoever takes over the job simply inherits this script – SQLazy compiles it into the final SQL on demand. SQLazy's compilation engine does not depend on large language models – deterministic inputs always produce deterministic outputs. Later when updates are needed, you just need to modify and debug this readable SQLazy script, then recompile it to generate new SQL.

In this article, we attempt to use Trae as the “brain” to interpret SQL semantics, break down business logic, and generate step-by-step SQLazy scripts; then use SQLazy IDE as the "execution layer" for syntax validation, step-by-step debugging, and final code compilation. The combination forms a closed loop of "AI interpretation + human review + deterministic engine compilation," bringing legacy SQL back to life.

Ⅱ Roles and Capabilities of Tools

1. Trae: The “Brain” for Understanding and Translation

Trae is an AI programming tool that can be used both as a standalone IDE and as a VS Code-style editor. Here, Trae plays three roles:

(1) The project knowledge base is loaded automatically. The project includes a global specification file (plan.md) that declares the output format for SQLazy scripts, including three-column tab-separated and one function per step, hard constraints such as reserved word handling and cross-step reference rules, and loading paths for function and feature documentation. When a user enters /sqlazy-plan, Trae automatically inherits all these rules – no need to redeclare them.

(2) Generate output through structured four steps. Trae is constrained to output a solution following four steps: capability review → requirement decomposition → feature matching → code implementation, rather than directly giving the final result. This forced process ensures solution auditability – the rationale behind each step is fully transparent. The final output is a structured SQLazy script, not a chunk of hard-to-read SQL.

(3) Clarify requirement boundaries proactively. When faced with complex requirements, Trae proactively raises key questions, such as: How should the date range be defined? How should null values be handled? Is the grouping key unique? This prevents AI from making assumptions without explicit confirmation.

2. SQLazy: The “Hands” for Execution and Validation

SQLazy is a structured data computing tool with a dedicated IDE for writing and executing .nsql scripts. In simple terms, it breaks complex business logic into human-readable “operation instructions” – each completes one data processing step. The SQLazy IDE receives .nsql scripts output by Trae and provides three guarantees:

(1) Clear step semantics with low audit complexity. Taking “consecutive up days calculation” as an example, the .nsql script requires only 5 steps: filter → sort → segment →count → find the maximum. Reading the entire script feels like reading a business operation checklist.

(2) Step-by-step execution for rapid problem identification. The IDE lets you run each step individually and inspect intermediate results in real time. IF a step's output doesn't match expectations, you can immediately pinpoint the exact logic error.

(3) One script, multi-database compilation. After validation passes, one click compiles to native SQL for MySQL, PostgreSQL, Oracle and other mainstream databases. No rewriting per database. .

III. Workflow

3.1 Environment Setup

The project adopts the following standard directory structure:

project_root/
├── plan.md         # Global specification: format standards, loading paths
├── sqlazy-plan.md   # Command entry: /sqlazy-plan trigger, inherits all rules from plan.md
├── nsql/            # Delivery directory: SQLazy scripts stored here
├── function/        # Function reference documentation (auto-loaded)
└── action/          # Action reference documentation (auto-loaded)

After creating a new project in Trae, copy sqlazy-plan.md, plan.md files, function/ and action/ directories from the the SQLazy installation directory’s LLM folder to the project root directory.

3.2 Trigger Method

Use the /sqlazy-plan command in the Trae chat box to trigger the task, followed by a complete business requirement description.

3.3 Validation and Correction

This is the most critical step in the entire workflow:

1. Construct a small set of representative test data and manually calculate expected results.

2. Run the script step by step in the SQLazy IDE, comparing intermediate results against expected values.

3. When issues are found, modify the script directly or report them to Trae for regeneration.

After validation passes, compile to native SQL for the target database. This step is very simple, so it won't be mentioned in the real-world practices below.

IV. Hands-on Case Studies

The following five cases are presented in order of “ simple to complex, smooth to bumpy”. The first three demonstrate translation processes of varying difficulty. The fourth records an actual case of multiple rounds of correction. The last presents a typical failure where "script runs but results are wrong," revealing AI’s blind spots in understanding business logic.

Case 1: Conditional Segmentation and Cumulative Sum – Passed on the First Try

The original SQL logic: In data table exam_data, id field is for sorting, logic field is used for conditional judgment, and val field is for segmented cumulative sum. A new calculated column, output, is added: when logic==true, set output is as 1; otherwise, output accumulates as previous row's output + val.

[Operation] type the following into the Trae chat box:

/sqlazy-plan Convert the following SQL statement to SQLazy script:
with table1 as (
	SELECT *, countif(logic) over win1 as logic_run
	FROM exam_tbl
	window win1 as (order by id rows between unbounded preceding and current row)
)
SELECT *,
	sum(val) over win2 as sum_over,
	sum(if(logic,1,val)) over win2 as output
from table1
window win2 as (partition by logic_run order by id rows between unbounded preceding and current row)

[Trae’s SQL Analysis]

Trae’s analysis: This SQL has two levels of window operations. The first window, win1, sorts by id and uses countif (logic) for cumulative counting – each time a row with logic=true is encountered, logic_run increments by 1, dividing the data into multiple “segments”. The second window, win2, operates within each logic_run segment: after sorting by id, it sums val (as sum_over) and conditional expression if(logic,1,val) (as output). When translating this to NSQL, countif can be replaced by a two-step “condition → accumulate” process, and partitioned accumulation can be implemented as “accumulate + partition”.

[Generated SQLazy Script]

Name	Anchor	Statement
t1	exam_tbl	sort id
t2		compute if(logic then 1 else 0), as logic_flag
t3		compute logic_flag, cum, as logic_run
t4		compute if(logic then 1 else val), as logic_val
t5		compute val, cum, as sum_over; partition logic_run
t6		compute logic_val, cum, as output; partition logic_run
t7		derive id, logic, val, logic_run, sum_over, output

This is a “textbook-style” translation – SQL window function semantics are clear, and SQLazy’s “accumulate” and “partition” features naturally correspond to them. Trae generated it once and it passed in the SQLazy IDE without corrections.

Case 2: Conditional Grouping – Passed After One Syntax Correction

The original SQL logic: The data table test_table_mm has fields id, split, cust, date_column, amount, etc. The requirement is to perform conditional grouping: group by the first two fields id and split; if the date_column within the group has no duplicates, keep the record with the most recent date and replace its amount with this group’s total amount ; if dates within the group have duplicates, further group the records by cust, keep the record with the most recent date in the current subgroup likewise, and replace its amount with this subgroup’s total amount within this subgroup.

[Operation] type the following into the Trae chat box:

/sqlazy-plan Convert the following SQL statement to SQLazy script:
SELECT id, 
       split,
       cust,
       date_column,
       CASE num_cust
       WHEN 1
       THEN total_amount
       ELSE total_cust_amount
       END AS amount
FROM   (
  SELECT t.*,
         COUNT(DISTINCT CASE rnk WHEN 1 THEN cust END)
           OVER (PARTITION BY id, split) AS num_cust
  FROM   (
    SELECT t.*,
           DENSE_RANK() OVER (PARTITION BY id, split ORDER BY date_column DESC) AS rnk,
           SUM(amount) OVER (PARTITION BY id, split) AS total_amount,
           SUM(amount) OVER (PARTITION BY id, split, cust) AS total_cust_amount
    FROM   test_table_mm t
  ) t
  WHERE  rnk = 1
)

[Trae’s SQL Analysis]

Trae’s analysis: first partition by id and split, and use the ranking feature to find the row with the maximum date_column (rnk=1). Calculate two sums in parallel: total_amount grouped by id+split, and total_cust_amount grouped by the three-field id+split+cust key. After filtering for rows where rnk=1, count the unique cust values (num_cust) within each id+split group. Finally, use a conditional function to determine: if num_cust=1, take total_amount; otherwise take total_cust_amount.

[Generated SQLazy Script]

Name	Anchor	Statement
t2	test_table_mm	rank date_column desc;chinese rank; partition id, split; as rnk
t3		compute amount, sum, as total_amount; partition id, split
t4		compute amount, sum, as total_cust_amount; partition id, split, cust
t5		filter (rnk = 1)
t6		compute cust, icount, as num_cust; partition id, split
t7		compute if(num_cust = 1 then total_amount else total_cust_amount), as amount
t8		derive id, split, cust, date_column, amount

[Syntax Correction]

During runtime, SQLazy reported an error: in the “rank” statement of the t2 step, the “option” parameter was initially written as “Chinese-style ranking”, which is not a valid SQLazy keyword. SQLazy’s ranking function expects the parameter “dense” (meaning gap-free ranking, where tied ranks are not skipped). After correcting “Chinese-style ranking” to “dense” in the first line of code, the script ran successfully.

Case 3: Reverse-Deriving the Initial Date from the total – Passed After One Syntax Correction

The original SQL logic: The data table, table_name, records the inbound quantity and the resulting cumulative inventory on specific dates. Given a target date, we need to work backward from the cumulative inventory to determine the initial date – the day with zero or negative inventory – and fill in the inbound quantity after each day’s consumption, UPDATED_QTY, and the original inventory, UPDATED_CUSTQTY.

[Operation] type the following into the Trae chat box:

/sqlazy-plan Convert the following SQL statement to SQLazy script:
SELECT t.*,
       LEAST(
         GREATEST(
           COALESCE(
             SUM(
               CASE WHEN needdate < TRUNC(to_date('2024-02-26')) + 1 THEN qty END
             ) OVER (PARTITION BY item, loc ORDER BY needdate DESC) - custqty,
             qty
           ),
           0
         ),
         qty
       ) AS updated_qty,
       CASE
       WHEN needdate > TRUNC(to_date('2024-02-26'))
       THEN NULL
       WHEN SUM(
              CASE WHEN needdate < TRUNC(to_date('2024-02-26')) + 1 THEN qty END
            ) OVER (
              PARTITION BY item, loc
              ORDER BY needdate DESC
              ROWS BETWEEN UNBOUNDED PRECEDING AND 1 PRECEDING
            ) >= custqty
       THEN NULL
       ELSE GREATEST(
              custqty
              - SUM(
                CASE WHEN needdate < TRUNC(to_date('2024-02-26')) + 1 THEN qty END
              ) OVER (PARTITION BY item, loc ORDER BY needdate DESC),
              0
           )
       END AS updated_custqty
FROM   table_name t

[Trae’s SQL Analysis]

Trae’s analysis: This is an inventory calculation logic that works backward from the total to determine the initial date. The process involves sorting, conditional filtering, partitioned cumulative sum, followed by comparing the previous row’s cumulative value (prev_cumulative_qty) with custqty and updating qty and custqty according to three scenarios: if the cumulative value is insufficient, set qty to zero and deduct the corresponding amount from custqty; if it exactly matches, take the difference; if it exceeds the required amount, retain qty and set custqty to zero. The conditional logic requires nested processing.

[Generated SQLazy Script]

Name	Anchor	Statement
t2	table_name	sort needdate desc
t3		compute if(needdate < date(2024-02-26) + 1 then qty else 0), as filtered_qty
t4		compute filtered_qty, cum, as cumulative_qty; partition item, loc
t5		compute cumulative_qty[-1], as prev_cumulative_qty
t6		compute if(nvl(cumulative_qty - custqty, qty) < 0 then 0, nvl(cumulative_qty - custqty, qty) > qty then qty; else nvl(cumulative_qty - custqty, qty)), as updated_qty
t7		compute if(needdate > date(2024-02-26) then null, prev_cumulative_qty >= custqty then null; else if(custqty - cumulative_qty < 0 then 0; else custqty - cumulative_qty)), as updated_custqty
t8		derive append updated_qty, updated_custqty

[Syntax Correction]

Runtime error: the unquoted date constants at steps t3 and t7 – written as date(2024-02-26), couldn’t be recognized. SQLazy’s date function requires the date parameter to be a string constant enclosed in double quotes. After correcting date(2024-02-26) to date(“2024-02-26”) in the code, the script ran successfully.

Case 4: Time Interval-based Aggregation — Passed After Multiple Corrections

The original SQL logic: The data table, tmstmp, has two columns – dt and payload, for storing time-series data, with records spaced several seconds apart. Now we need to group and aggregate records every minute, summarizing 5 minutes of data each time. For example, if 3 records are generated in the 1st, 2nd, and 3rd seconds, data for minutes 1-5, 2-6, and 3-7 are summarized respectively.

[Operation] type the following into the Trae chat box:

/sqlazy-plan Convert the following SQL statement to SQLazy script:
SELECT
    [From], DATEADD(MINUTE, 1, [To]) [To], payload
FROM (
    SELECT
        dt, MIN(dt) OVER(ORDER BY dt ROWS BETWEEN 4 PRECEDING AND CURRENT ROW) [From],
        dt [To], SUM(payload) OVER(ORDER BY dt ROWS BETWEEN 4 PRECEDING AND CURRENT ROW) payload
    FROM (
        SELECT
            DATEADD(MINUTE, DATEDIFF(MINUTE, 0, dt), 0) dt, 
SUM(payload) payload
        FROM #tmstmp
        GROUP BY DATEADD(MINUTE, DATEDIFF(MINUTE, 0, dt), 0)
    ) q
) q
WHERE DATEDIFF(MINUTE, [From], [To]) > 3

[Trae’s SQL Analysis]

Trae’s analysis: The inner layer first truncates dt to minute-level and performs GROUP BY-based summation to get the payload per minute; the middle layer uses a 5-row sliding window (4 PRECEDING to CURRENT ROW) to take MIN(dt) within the window as From, the current dt as To, and SUM(payload) as payload; the outer layer filters for DATEDIFF(From,To)>3 and adds 1 minute to To. When translating this to SQLazy, “5-row window” can be implemented using the “row reference [-4] to current row”; MIN(dt), when data is sorted, equals mt[-4] (the earliest time row).

[Generated SQLazy Script]

Name	Anchor	Statement
t1	tmstmp	compute datetime(dt; precision minute) as mt
t2		summarize payload total as payload; group mt
t3	t2	sort mt
t4		compute mt[-5] as from_t, mt[-1] as to_t, nvl(payload[-5],0)+payload[-4]+payload[-3]+payload[-2]+payload[-1]+payload as payload
t5		filter (interval(from from_t, to to_t; minute)> 3)
t6		derive from_t, to_t + 1/1440 as to_t, payload

[Multiple Rounds of Corrections]

This case best demonstrates the complete closed loop of “AI first draft + human feedback + iterative correction”. Trae made several types of errors, each representative of its kind:

Error Correction Records

Round	Error Symptom	Root Cause	Correction Method
Round 1	Parameter [] value setting error	Misused “total” as aggregation function keyword; should be “sum” in SQLazy	Change “total” to “sum”
Round 2	Value [from] has no matching parameter	Interval function mistakenly included parameter names “from” and “to” (start/end); SQLazy requires these to be omitted	Remove the parameter names and write them as positional parameters: interval(from_t, to_t; minute)
Round 3	from_t null value rows not filtered out	When from_t is null, interval function returns null	Change to: filter (from_t not empty)
Round 4	Unrecognized expression: from_t not empty	“not empty” isn’t valid SQLazy syntax	Change “not empty” to “notnull”
Round 5	Time interval only 4 minutes	Window offset error: mt[-1] and mt[-5] caused the window to miss one row	Change to mt (current row) and mt[-4] (previous 4 rows)
Round 6	to_t + 1/1440 value unchanged after operation	Float 1/1440 has precision loss; time addition not taking effect	Use SQLazy’s elapse function: (to_t elapse 1 minute)

[Final Script]

Name	Anchor	Statement
t1	tmstmp	compute datetime(dt; precise minute), as mt
t2	t1	summarize payload sum as agg_payload; group mt
t3	t2	sort mt
t4		compute mt, as to_t; mt[-4], as from_t; nvl(agg_payload[-4], 0)+ nvl(agg_payload[-3], 0)+ nvl(agg_payload[-2], 0)+ nvl(agg_payload[-1], 0) + agg_payload, as payload
t5		filter (from_t notnull)
t6		derive from_t, (to_t elapse 1 minute) as to_t, payload

Six rounds of correction may sound like a lot, but in each round, the feedback was as simple as “paste the error message into Trae”. In most cases, Trae was able to identify and fix the issue in one round. This is precisely where the value of the “AI interpretation + human review + small-scale data validation” workflow lies: AI does the work, humans check, and when errors occur, simply paste them back to AI for correction.

Case 5: Time Window Statistics — Script Runs but Results Are Wrong

The original SQL logic: The data table, main, has time and value fields. The time field records timestamps and their intervals may sometimes exceed 1 minute. Now we need to divide data into one-minute windows, fill in missing windows, and calculate four values for each window: start_value, the last record of the previous window; end_value, the last record of the current window; min, the minimum value in the current window; max, the maximum value in the current window. For the first minute, start_value is taken from the first record of the current window. If a window’s data is missing, use the last record of the previous window as its start_value and end_value.

[Operation] type the following into the Trae chat box:

/sqlazy-plan Convert the following SQL statement to SQLazy script:
with overview as (
    SELECT 
        distinct on (a.time) a.id, a.time, b.time as "end", a.value, 
        date_trunc('minute', a.time) as minute_start, 
        date_trunc('minute', b.time) as minute_end 
    FROM 
        main a 
    left join 
        main b 
    on 
        a."time"<b."time" and a.id = b.id 
    order by 
        a.time, b.time asc
    ),
overview2 as (
    select 
        id, value, true as backfill,
        date_trunc('minute', "end") as time, 
        date_trunc('minute', "end") as minute
    from 
        overview 
    where 
        minute_start <> minute_end
    UNION ALL
    select 
        id, time, value, false as backfill,
        date_trunc('minute', time) as minute
    from 
        overview
    ),  
overview3 as (
    select 
        * 
    from 
        overview2 
    UNION ALL (
        Select 
            distinct on (a.missingminute) 
            c.id, 
            a.missingminute as time, 
            a.missingminute as minute, 
            c.value, 
            true as backfill 
        from (
            SELECT 
                date_trunc('minute', time.time) as missingminute
            FROM 
                generate_series((select min(minute) from overview2),(select max(minute) from overview2),'1 minute'::interval) time 
            left join (
                select distinct 
                    minute 
                from 
                    overview2
                ) b 
            on 
                date_trunc('minute', time) = b.minute 
            where 
                b.minute isnull
            ) a 
        left join 
            main c 
        on 
            a.missingminute > c.time 
        order by 
            a.missingminute, 
            c.time desc
        ) 
    order by 
        time
    )
select 
    t1.id, 
    t1.minute as minute_start, 
    t1.minute + interval '1 minute' as minute_end, 
    t1.backfill as start_backfill,
    t1.start, 
    t2.end, 
    coalesce(t3.min, t1.start) as min, 
    coalesce(t3.max, t1.start) as max 
from 
    (select distinct on (id, minute) id, minute, value as start, backfill from overview3 order by id, minute, time asc) t1 
left join
    (select distinct on (id, minute) id, minute, value as end from overview3 order by id, minute, time desc) t2 on t1.id = t2.id and t1.minute = t2.minute 
left join
    (select id, minute, min(value) min, max(value) max from overview2 group by id,minute) t3 on t1.id = t3.id and t1.minute = t3.minute

[Trae’s SQL Analysis]

Trae’s analysis: In this piece of SQL, the overview query uses a self-join to find the next time point; overview2 splits records spanning minute boundaries and marks them as backfill; and overview3 uses generate_series to fill in missing minutes. The final output contains start, end, min, max values for each minute. Trae translated this logic into a 23-step SQLazy script.

[Generated SQLazy Script (Key Steps Excerpt)]

Name	Anchor	Statement
t1	main	sort 'time'
t2		compute # as id, 'time'[1] as next_time
t3		compute datetime('time'; precision minute) as mt, datetime(next_time; precision minute) as mt_end
t4		filter (mt <> mt_end)
t5		derive id, mt_end as 'time', value, true as backfill, mt_end as mt
t6	t3	derive id, 'time', value, false as backfill, mt
t7	t6	set union; t5
...		(intermediate steps omitted, total 23 steps)
t9	t7	summarize value min as vmin, value max as vmax; group by mt
t23		derive filled_id as id, mt as minute_start, ... nvl(vmin, filled_start) as vmin, nvl(vmax, filled_start) as vmax

[Problem: Script Runs, But Results Are Wrong]

After multiple rounds of syntax corrections, the script finally ran successfully. However, when compared against test data, the results didn’t match the expected output. Trae had misinterpreted the SQL semantics during translation.

[Deep Analysis of Trae’s Misinterpretation]

Deviation Analysis

Deviation	Trae’s Understanding	Actual SQL Logic
Deviation 1: Mistake “id” for a row number	“compute # as id” – take row number as id in line t2	a.id in SQL is a real data column in main table, serving as a “sequence/grouping key” – multiple records with the same id form a time series). Throughout the SQL, rows are grouped and deduplicated by (id, minute). Using #, the row number, as a substitute for id completely loses the semantics of “forming sequences by id”. As a result, when there are multiple id values, all sequences are mixed into the same minute bucket for aggregation, producing completely incorrect results.
Deviation 2: Use “next row” instead of “next event of same id”	Use ‘time’[1] in line t2, meaning the next row after sorting by time, without id constraint	The SQL self-join condition is a.time<b.time AND a.id=b.id – first filter rows by id and then retrieve the next row. After multi-id rows are sorted by time, adjacent rows may belong to different ids. As a result, the “next event of another id” may be treated as the “next event of the current id”, generating incorrect backfill rows.
Deviation 3: “Cross-row value leakage” in min/max – the invisible semantics misinterpretation	Directly aggregate min/max from overview2	SQL’s overview2 mixes “rows backfilled with carried-forward values” with “actual events”, while the final query in t3 directly aggregate min/max from overview2. As a result, “the carried-forward previous value” leaked into the extreme value statistics.

[Lesson Learned]

This case reveals an important fact: AI can mechanically “translate” SQL syntax, but may not accurately understand the business intent behind SQL. When the SQL itself has “mixed semantics” in its design (e.g., overview2 contains both rows backfilled with carried-forward values and actual events, and aggregates extreme values from them), AI will faithfully carry this “design flaw” into SQLazy, causing incorrect results. This type of issue cannot be fixed simply by “feeding error messages back” alone, because the script doesn’t report an error – it just “calculates incorrectly”.

V. Summary: Workflow, Experience, and Boundaries

1. Standard Workflow for Taming Legacy SQL

Having walked through the five cases above, we can distill a standard workflow for converting legacy SQL to SQLazy:

Step 1: Issue the command. Send the SQL code to Trae along with the instruction: /sqlazy-plan Convert the following SQL statement to a SQLazy script.

Step 2: Generate the first draft. Trae outputs the initial SQLazy script following a four-step workflow (capability review → requirement decomposition → feature matching → code implementation).

Step 3: Test and validate. Import test data into the SQLazy IDE, run the script step by step, and compare intermediate results against the expected values.

Step 4: Report and correct. If there are syntax or logic errors, feed the error message back to Trae for correction. Repeat until the script runs correctly.

Step 5: Compile and deliver. After validation passes, one-click compilation generates SQL for the target database, ready to deliver to production.

Save this SQLazy script – for future edits and handover – instead of the hard-to-understand SQL script.

2. Key Insights

Based on these case studies, we can draw a cautiously optimistic yet clear-eyed conclusion:

Most SQL statements can be correctly translated to readable, easily verifiable SQLazy code. From Case 1, which passed on the first try, to Cases 2 and 3, which passed after one correction, and finally Case 4, which passed after six rounds of corrections – these cases cover typical scenarios including conditional partitioning, conditional grouping, backward inventory calculation, and time interval-aggregation, Trae ultimately delivered correct answers. The SQLazy script’s “clear semantics at every step” feature brings business logic once buried deep within nested SQL out into the open, significantly lowering the barrier for human review.

However, for a small number of highly complex SQL statements, Trae may misinterpret the business logic, resulting in incorrect results. Case 5 is a typical counterexample – the script runs, but the results are wrong.

3. Advanced Strategy for Handling Complex SQL

For complex SQL like Case 5, simply “sending SQL to Trae for translation” is no longer sufficient. A more reliable approach is to:

First, have Trae analyze the SQL’s functionalities step by step to help engineers read and understand it. For example, you can ask Trae: “What query requirements does this SQL statement fulfill?” Have Trae explain, CTE by CTE, what overview, overview2, and overview3 each do and what the final output is. The purpose of this step is to let humans – not AI – understand the SQL’s actual intent first.

Once you understand the SQL’s actual intent, submit the “requirement description” – rather than the original SQL – to Trae and have it write the SQLazy script based on that description. This avoids the trap of “SQL itself has design flaws, and AI faithfully carries those flaws over”, because you are describing what should be done, not how the SQL does. This allows AI to use a simpler, more straightforward algorithms when writing the SQLazy script.

Ultimately, AI is a productivity amplifier, not a “vending machine” that replaces human judgment. So, what should we do with legacy SQL? The answer is not to “throw it at AI for automatic translation”, but to adopt a three-way collaboration: “humans understand the intent, AI converts it into a script, and the engine validates and execute it”. In this way, legacy SQL can be transformed from a “time bomb no one dares to touch” into a “business asset readable by everyone”.