Closes the human-in-the-loop gap from docs/brief/feature-set.md and
Phase 2 of the 2026-04-23 feature-complete roadmap.
Storage (kon-storage):
- Migration v10 adds the `feedback` table: (target_type, target_id,
rating, original_text, corrected_text, context_json, profile_id,
created_at) with CHECK constraints on target_type and rating, plus
indexes on (target_type, rating, created_at DESC) for prompt-time
retrieval and (profile_id, target_type, created_at DESC) for
per-profile scoping.
- New public API: `FeedbackTargetType`, `RecordFeedbackParams`,
`FeedbackRow`, `record_feedback`, `list_feedback_examples`.
- Tests updated — the RB-02 rollback regression now discovers the
real max version at runtime instead of hard-coding v10 for its
poison migration.
LLM (kon-llm):
- `prompts::FeedbackExample` — local shape for few-shot exemplars so
kon-llm stays independent of kon-storage.
- `prompts::build_conditioned_system_prompt` — appends a "here is
the style this user prefers" block to the base system prompt
when examples are available; returns the base prompt unchanged
when empty, so new users and early sessions see generic output.
- `LlmEngine::decompose_task_with_feedback` and
`LlmEngine::extract_tasks_with_feedback` thread examples through
to the builder. The old one-arg variants are preserved and now
call through with an empty slice.
- 4 unit tests covering empty, empty-input-skip, correction-wins,
and thumbs-up-only fallback.
Tauri (src-tauri):
- New commands::feedback module: `record_feedback`,
`list_feedback_examples_cmd`.
- `decompose_and_store` and `extract_tasks_from_transcript_cmd`
now fetch the last 5 positive/neutral feedback rows for their
target type and pass them through to the LLM, wiring the
learning loop end-to-end.
- Shared `to_llm_examples` helper parses the `context_json.input`
field (where the recorder stashes the parent task text / transcript
chunk) back into the exemplar shape.
Frontend (MicroSteps.svelte):
- Thumbs-up and thumbs-down buttons on every micro-step row.
Hover-revealed; the vote recolours the icon; clicking again
clears the local highlight (the row itself stays in the audit
trail).
- Pencil icon + double-click to edit step text. Save flows through
update_task_cmd for persistence and records a correction feedback
row with (original_text, corrected_text) — the highest-value
training signal.
- Parent task text is captured in context_json.input at record time
so the prompt builder can reconstruct the (input, preferred-output)
pair on subsequent decompositions.
- Feedback capture is best-effort — a record_feedback failure never
interrupts the primary action.
What's deferred to a later phase:
- Thumbs + corrections on extracted tasks (same pipeline, different
surface — probably TasksPage after the AI-extraction path)
- Thumbs on transcript cleanup output
- Semantic retrieval over the feedback corpus (once there is enough
data to justify embedding infrastructure; the storage shape is
already ready for it)
Jake
46be0a5aca