% of conversations ending with an assistant reply
| % of conversations ending with an assistant reply | n |
| 98.57 | 129,584 |
What this shows: One overall figure: % of conversations ending with an assistant reply is 98.57%, computed over 129,584 rows.
A conversation whose last message is the user's never got — or never captured — an answer; ending on the assistant is our completeness proxy.
1 row(s) · 0.042s on the full corpus
compiled SQL (read-only, parameterized)
SELECT AVG(t.is_complete) * 100.0 AS value, COUNT(*) AS n
FROM (
SELECT c.id AS cid,
c.n_messages AS n_messages, c.turns_count AS turns_count,
cs.has_thinking AS has_thinking, cs.has_code AS has_code,
cs.has_links AS has_links, cs.is_complete AS is_complete
FROM conversations c
LEFT JOIN conv_signals cs ON cs.conversation_id = c.id
WHERE 1=1
) t
-- params: []
% of conversations ending with an assistant reply by turn_bucket
1-2
98.77
3-5
97.67
6-10
99.10
11-20
98.57
21+
98.38
What this shows: Each bar is one turn_bucket; the value is % of conversations ending with an assistant reply. 6-10 is highest at 99.10%; 3-5 is lowest at 97.67% across 5 groups.
The paper's multi-turn angle: deeper dialogues risk more abandonment.
5 row(s) · 0.122s on the full corpus
compiled SQL (read-only, parameterized)
SELECT grp, AVG(t.is_complete) * 100.0 AS value, COUNT(*) AS n
FROM (
SELECT CASE
WHEN c.n_messages <= 2 THEN '1-2'
WHEN c.n_messages <= 5 THEN '3-5'
WHEN c.n_messages <= 10 THEN '6-10'
WHEN c.n_messages <= 20 THEN '11-20'
ELSE '21+' END AS grp,
c.id AS cid,
c.n_messages AS n_messages, c.turns_count AS turns_count,
cs.has_thinking AS has_thinking, cs.has_code AS has_code,
cs.has_links AS has_links, cs.is_complete AS is_complete
FROM conversations c
LEFT JOIN conv_signals cs ON cs.conversation_id = c.id
WHERE 1=1 AND CASE
WHEN c.n_messages <= 2 THEN '1-2'
WHEN c.n_messages <= 5 THEN '3-5'
WHEN c.n_messages <= 10 THEN '6-10'
WHEN c.n_messages <= 20 THEN '11-20'
ELSE '21+' END IS NOT NULL
) t
GROUP BY grp
ORDER BY CASE grp WHEN '1-2' THEN 1 WHEN '3-5' THEN 2 WHEN '6-10' THEN 3 WHEN '11-20' THEN 4 ELSE 5 END
LIMIT 20
-- params: []
% of conversations ending with an assistant reply by platform
claude
100
gemini
100
perplexity
99.97
grok
99.51
chatgpt
98.11
What this shows: Each bar is one platform; the value is % of conversations ending with an assistant reply. claude is highest at 100%; chatgpt is lowest at 98.11% across 5 groups.
Interfaces and sharing flows differ by platform, and so does completeness.
5 row(s) · 0.059s on the full corpus
compiled SQL (read-only, parameterized)
SELECT grp, AVG(t.is_complete) * 100.0 AS value, COUNT(*) AS n
FROM (
SELECT c.platform AS grp,
c.id AS cid,
c.n_messages AS n_messages, c.turns_count AS turns_count,
cs.has_thinking AS has_thinking, cs.has_code AS has_code,
cs.has_links AS has_links, cs.is_complete AS is_complete
FROM conversations c
LEFT JOIN conv_signals cs ON cs.conversation_id = c.id
WHERE 1=1 AND c.platform IS NOT NULL
) t
GROUP BY grp
ORDER BY value DESC
LIMIT 20
-- params: []
avg messages per conversation
| avg messages per conversation | n |
| 11.47 | 1,850 |
What this shows: One overall figure: avg messages per conversation is 11.47, computed over 1,850 rows. Scope: complete = false.
filters.complete = false isolates the abandoned dialogues for closer study.
1 row(s) · 0.008s on the full corpus
compiled SQL (read-only, parameterized)
SELECT AVG(t.n_messages) AS value, COUNT(*) AS n
FROM (
SELECT c.id AS cid,
c.n_messages AS n_messages, c.turns_count AS turns_count,
cs.has_thinking AS has_thinking, cs.has_code AS has_code,
cs.has_links AS has_links, cs.is_complete AS is_complete
FROM conversations c
LEFT JOIN conv_signals cs ON cs.conversation_id = c.id
WHERE cs.is_complete = ?
) t
-- params: [0]
conversations by topic
create_an_image
964
specific_info
606
other
543
unclear
330
write_fiction
302
greetings_and_chitchat
190
edit_or_critique_provided_text
169
tutoring_or_teaching
161
analyze_an_image
153
mathematical_calculation
141
computer_programming
110
argument_or_summary_generation
94
relationships_and_personal_reflection
83
personal_writing_or_communication
75
data_analysis
67
translation
60
how_to_advice
57
health_fitness_beauty_or_self_care
43
purchasable_products
35
asking_about_the_model
31
What this shows: Each bar is one topic; the value is conversations. create_an_image is highest at 964; asking_about_the_model is lowest at 31 (31.1× spread) across 20 groups. Scope: complete = false.
Final round: where do users walk away mid-conversation?
20 row(s) · 1.657s on the full corpus
compiled SQL (read-only, parameterized)
SELECT grp, COUNT(*) AS value, COUNT(*) AS n
FROM (
SELECT DISTINCT m.topic AS grp, c.id AS cid,
c.n_messages AS n_messages, c.turns_count AS turns_count,
cs.has_thinking AS has_thinking, cs.has_code AS has_code,
cs.has_links AS has_links, cs.is_complete AS is_complete
FROM messages m
JOIN conversations c ON c.id = m.conversation_id
LEFT JOIN conv_signals cs ON cs.conversation_id = c.id
WHERE cs.is_complete = ? AND m.topic IS NOT NULL
) t
GROUP BY grp
ORDER BY value DESC
LIMIT 20
-- params: [0]