Personal growth with AI
Here are a couple of ways I feel an AI like gpt-oss can help with personal growth.
- Even as an experienced PostgreSQL user, there are some functions you need to be reminded to use or that you are aghast you've never used. Gpt-oss showed me I was missing out on using jsonb_to_recordset.
- The hard-part of programming is not learning a language, but learning how to express your questions unambiguously and understanding
fully what you are trying to achieve. gpt-oss helped in this regard by pointing out where my questions didn't make sense or were ambiguous given the
dataset I was asking it to work with.
How up to date is your AI
Lets start as we did before assessing gpt-oss recency of knowledge about PostgreSQL. I did not try to tweak any of its settings and out of the box
it's painfully chatty. As I used it more, probably because my prior conversation was still in it's context, it became less chatty and event stopped with the impersonal "User requests" preface.
To prevent it from writing a novel about PostgreSQL's versioning before giving me an answer, I asked it this way
Me:
What is the latest version of PostgreSQL in one sentence. No narrative.
To which I am greeted with below which took quite some time cause it was probably thinking about PostgreSQL versionining patterns over the years.
gpt-oss:
Thinking...
User requests: "What is the latest version of PostgreSQL in one sentence. No narrative". So we answer: "PostgreSQL
17.4".
Creating fake data
Me:
Create a PostgreSQL table with a jsonb column that has heavily nested data that has 2 rows covering trips where the primary key is generated using `GENERATED ALWAYS AS IDENTITY`. No thinking narrative.
I threw in the requirement about GENERATED BY because just like phi4, gpt-oss wanted to give me an old-school bigserial. Its generated data output freaked me out
a bit because many of the locations were places that are a few blocks from me, though the names were slightly different or had the wrong address, which I assume was intentional. Is it because I live at the center of techie civilization or it picked up my GPS location
and thought "I'll show her some stuff she can relate to". Maybe next time, I will allow it to go on about how it came up with these places.
gpt-oss:
Thinking...
We need to provide just the SQL code. No narrative. Provide table creation and insertion of two rows with nested JSONB. Keep concise.
...done thinking.
CREATE TABLE trips (
id BIGINT GENERATED ALWAYS AS IDENTITY PRIMARY KEY,
data jsonb NOT NULL
);
INSERT INTO trips (data) VALUES
(
'{
"trip_name": "Weekend Getaway",
"trip_type": "leisure",
"origin": {
"city": "New York",
"airport": "JFK",
"coordinates": { "lat": 40.6413, "lon": -73.7781 }
},
"destination": {
"city": "Boston",
"airport": "BOS",
"coordinates": { "lat": 42.3656, "lon": -71.0096 }
},
"stops": [
{
"stop_id": 1,
"name": "Boston Airport",
"arrival": "2025-09-01T10:00:00-04:00",
"departure": "2025-09-01T11:30:00-04:00",
"location": {
"coordinates": { "lat": 42.3656, "lon": -71.0096 },
"address": {
"street": "S. Station Fwy",
"city": "Boston",
"state": "MA",
"zip": "02128"
}
},
"weather": {
"forecast": [
{ "date": "2025-09-01", "high": 78, "low": 62, "condition": "Partly Cloudy" },
{ "date": "2025-09-02", "high": 80, "low": 63, "condition": "Sunny" }
],
"precipitation_probability": 0.15
}
},
{
"stop_id": 2,
"name": "Boston City Center",
"arrival": "2025-09-01T12:00:00-04:00",
"departure": "2025-09-01T15:00:00-04:00",
"location": {
"coordinates": { "lat": 42.3601, "lon": -71.0589 },
"address": {
"street": "1 First St",
"city": "Boston",
"state": "MA",
"zip": "02108"
}
},
"weather": {
"forecast": [
{ "date": "2025-09-01", "high": 78, "low": 62, "condition": "Partly Cloudy" },
{ "date": "2025-09-02", "high": 80, "low": 63, "condition": "Sunny" }
],
"precipitation_probability": 0.12
}
}
],
"itinerary": {
"activities": [
{
"time": "2025-09-01T12:30:00-04:00",
"activity": "Lunch at Quincy Market",
"details": { "restaurant": "Trident", "cuisine": "Seafood", "reservation": true }
},
{
"time": "2025-09-01T14:00:00-04:00",
"activity": "Freedom Trail",
"details": { "tour_type": "Self‑guided", "duration_minutes": 120, "cost_usd": 0 }
}
],
"accommodation": {
"hotel": {
"name": "Boston Marriott Copley Place",
"address": "101 State Street, Boston, MA 02108",
"room_type": "Deluxe King",
"check_in": "2025-09-01T15:30:00-04:00",
"check_out": "2025-09-02T11:00:00-04:00"
}
}
}
}'::jsonb
),
(
'{
"trip_name": "Business Trip",
"trip_type": "work",
"origin": {
"city": "San Francisco",
"airport": "SFO",
"coordinates": { "lat": 37.6213, "lon": -122.3790 }
},
"destination": {
"city": "Seattle",
"airport": "SEA",
"coordinates": { "lat": 47.6038, "lon": -122.3308 }
},
"stops": [
{
"stop_id": 1,
"name": "Seattle Airport",
"arrival": "2025-10-10T09:00:00-07:00",
"departure": "2025-10-10T09:30:00-07:00",
"location": {
"coordinates": { "lat": 47.6038, "lon": -122.3308 },
"address": {
"street": "3rd Ave & E Denny Way",
"city": "Seattle",
"state": "WA",
"zip": "98128"
}
},
"weather": {
"forecast": [
{ "date": "2025-10-10", "high": 68, "low": 54, "condition": "Cloudy" }
],
"precipitation_probability": 0.22
}
},
{
"stop_id": 2,
"name": "Microsoft Seattle HQ",
"arrival": "2025-10-10T10:30:00-07:00",
"departure": "2025-10-10T11:30:00-07:00",
"location": {
"coordinates": { "lat": 47.6694, "lon": -122.3458 },
"address": {
"street": "1501 2nd Ave W",
"city": "Seattle",
"state": "WA",
"zip": "98121"
}
},
"weather": {
"forecast": [
{ "date": "2025-10-10", "high": 70, "low": 58, "condition": "Clear" }
],
"precipitation_probability": 0.05
}
}
],
"itinerary": {
"meetings": [
{
"time": "2025-10-10T10:30:00-07:00",
"location": "Microsoft Seattle HQ",
"participants": ["John Doe", "Jane Smith", "Alan Turing"],
"agenda": ["Kickoff", "Budget Review", "Road‑map"]
},
{
"time": "2025-10-10T11:30:00-07:00",
"location": "Amazon Seattle HQ",
"participants": ["Bob Johnson", "Elon Musk"],
"agenda": ["Partnership", "Tech Integration"]
}
],
"accommodation": {
"hotel": {
"name": "The Edgewater Seattle",
"address": "121 2nd Ave, Seattle, WA 98101",
"room_type": "Executive Suite",
"check_in": "2025-10-10T14:00:00-07:00",
"check_out": "2025-10-11T11:00:00-07:00"
}
}
}
}'::jsonb
);
gpt-oss gave exceptionally beautiful and flawlessly runnable code and it gave me no more and no less than the column structure I asked for.
Now that we have some dense data, let's query it.
Me:
Write a PostgreSQL SQL statement that returns the date and itineraries of each trip one row per itinerary. No thinking narrative.
gpt-oss took about a minute or 2 to come up with an answer for this on my desktop, but it gave a pretty decent answer and a short description
of its thinking process.
Thinking...
We need to extract date and itineraries per trip. Likely itinerary activities: each row per activity. Provide date and itinerary
details. Use jsonb_to_recordset. Let's craft: SELECT id, trip_name, activity_time, activity, details FROM trips,
jsonb_to_recordset(data->'itinerary'->'activities') as x(activity_time timestamp, activity text, details jsonb). Or extract date part
of time. Provide date (activity_time::date). So output: date, activity, details. Let's craft final answer.
...done thinking.
SELECT
t.id,
t.data->>'trip_name' AS trip_name,
activity_rec.time::date AS activity_date,
activity_rec.activity AS activity,
activity_rec.details AS activity_details
FROM
trips AS t
CROSS JOIN LATERAL
jsonb_to_recordset(
t.data -> 'itinerary' -> 'activities'
) AS activity_rec(
time TIMESTAMP,
activity TEXT,
details JSONB
);
Let's revise a bit cause I don't want to read a blob of json in that activity details.
Me:
Write a PostgreSQL SQL statement that returns the date, time, type, name of each activity or meeting, one row per activity or meeting. For the type specify if it is a meeting or an activity. No thinking narrative.
As you can see it did end up providing a bit of narrative. But if you are learning how to query PostgreSQL, the explaining you probably welcome.
What I really love about gpt-oss explanation is that it made me aware of ambiguities in my question and things
I didn't really think that much about relative to the structure of the data. More importantly I discovered that I've never used this wonderful function
jsonb_to_recordset, though I had heard about it and yet this wonderful function has existed since PostgreSQL 13.
gpt-oss:
Thinking...
We need a query to return date, time, type (meeting or activity), name. Activities have fields: time, activity, details. Meetings have
time, location, participants, agenda. So type determined by presence of 'activity' key. Or we can union two selects.
We need date, time, type, name. For activity name: activity field. For meeting name: maybe location or maybe some field like
'meeting'. But they didn't specify meeting name. We could use 'location' as name for meeting. The problem: "type, name of each
activity or meeting". For meeting, name could be location. Use location. We'll union.
Use CROSS JOIN LATERAL.
Return: trip id maybe optional. They didn't ask for trip id. Just date, time, type, name.
Implement:
SELECT date, time, 'activity' AS type, activity AS name FROM activities
UNION ALL
SELECT date, time, 'meeting' AS type, location AS name FROM meetings
But need to parse JSON. We'll use jsonb_to_recordset for activities and meetings.
Return date and time from timestamp. Use date(time) and time(time). The 'time' field is TIMESTAMP.
Also include type.
Let's produce:
SELECT (act.time)::date AS date,
(act.time)::time AS time,
'activity' AS type,
act.activity AS name
FROM trips t
CROSS JOIN LATERAL jsonb_to_recordset(t.data->'itinerary'->'activities')
AS act(time TIMESTAMP, activity TEXT, details JSONB)
UNION ALL
SELECT (meet.time)::date AS date,
(meet.time)::time AS time,
'meeting' AS type,
meet.location AS name
FROM trips t
CROSS JOIN LATERAL jsonb_to_recordset(t.data->'itinerary'->'meetings')
AS meet(time TIMESTAMP, location TEXT, participants JSONB, agenda JSONB);
Order by date, time.
Let's produce final answer.
...done thinking.
/* Return one row per activity or meeting, showing
the date, time, type (activity / meeting) and name */
SELECT
(act.time)::date AS date,
(act.time)::time AS time,
'activity' AS type,
act.activity AS name
FROM
trips t
CROSS JOIN LATERAL
jsonb_to_recordset(
t.data -> 'itinerary' -> 'activities'
) AS act(
time TIMESTAMP,
activity TEXT,
details JSONB
)
UNION ALL
SELECT
(meet.time)::date AS date,
(meet.time)::time AS time,
'meeting' AS type,
meet.location AS name
FROM
trips t
CROSS JOIN LATERAL
jsonb_to_recordset(
t.data -> 'itinerary' -> 'meetings'
) AS meet(
time TIMESTAMP,
location TEXT,
participants JSONB,
agenda JSONB
)
ORDER BY date, time;
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