103 lines
5.3 KiB
Markdown
103 lines
5.3 KiB
Markdown
### Autofill: Learning, Personalization, and Explainability
|
||
|
||
This document outlines next-step enhancements for Content Strategy Autofill focusing on: learning from user acceptances, industry presets, constraint-aware generation, explainability, and RAG-lite context. It also captures the trade-offs for sectioned generation vs single-call generation.
|
||
|
||
## Goals
|
||
- Increase accuracy, personalization, and trust without increasing UI complexity.
|
||
- Keep costs predictable while reducing timeouts and retries.
|
||
- Preserve user control: never overwrite locked/accepted fields without consent.
|
||
|
||
## Single-call vs Sectioned Generation
|
||
- Single-call (current):
|
||
- Pros: 1 AI request, simpler orchestration.
|
||
- Cons: Larger prompt, higher timeout risk, brittle for structured JSON, hard to pinpoint failures.
|
||
- Sectioned (per category):
|
||
- Pros: Shorter prompts, better accuracy, quicker partial results, granular retries; lower latency per section; easier streaming (“Category X complete”).
|
||
- Cons: More calls; must cap/parallelize and cache to control cost.
|
||
- Recommendation: Hybrid
|
||
- Default: single-call for fast baseline; fallback/option: sectioned generation for users with large sites or when single-call fails/times out.
|
||
- Implement a server flag `mode=hybrid|single|sectioned` and a per-user policy (feature flag).
|
||
|
||
## Learning from Acceptances
|
||
- Data we already persist: `content_strategy_autofill_insights` (accepted fields + sources/meta).
|
||
- Learning policy:
|
||
- Build a per-user profile vector of “accepted values” and “field tendencies” (e.g., formats: video, cadence: weekly; brand voice: authoritative).
|
||
- During refresh:
|
||
- Use these as soft priors in prompt (“Bias toward previously accepted values unless contradictory to new constraints”).
|
||
- Prefer stable fields to remain unchanged unless explicitly unconstrained.
|
||
- Storage additions:
|
||
- Add fields to `content_strategy_autofill_insights` meta: `industry`, `company_size`, `accepted_at`.
|
||
- Maintain a compact, cached user profile (derived) for prompt injection.
|
||
- Safety:
|
||
- Respect locked fields (frontend lock) → never modified by refresh.
|
||
|
||
## Industry Presets
|
||
- Purpose: Cold-start quality boost.
|
||
- Source: curated presets per industry, company size, and region.
|
||
- Shape:
|
||
- Minimal key set aligned to core inputs (e.g., `preferred_formats`, `content_frequency`, `brand_voice`, `editorial_guidelines` template).
|
||
- Retrieval:
|
||
- Endpoint: GET `/autofill/presets?industry=...&size=...®ion=...` (cached).
|
||
- Merge policy:
|
||
- Apply only to empty fields; AI may override if constraints request.
|
||
|
||
## Constraint-Aware Generation
|
||
- User constraints: budget ceiling, cadence/frequency, format allowlist, timeline bounds.
|
||
- UI:
|
||
- “Constraints” panel (chip-set) accessible from header/Progress area.
|
||
- Backend:
|
||
- Accept constraints in refresh request (query/body).
|
||
- Inject constraints into prompt header and soft-validate outputs.
|
||
- Validation:
|
||
- Enforce with server-side validators; warn if AI violates, and auto-correct when safe.
|
||
|
||
## Explain This Suggestion (Mini-modal)
|
||
- Trigger: info icon next to each field.
|
||
- Content:
|
||
- Short justification text (one or two sentences), sources (onboarding/RAG docs), confidence.
|
||
- No raw chain-of-thought; ask model for a concise rationale summary that’s safe to expose.
|
||
- Backend payload additions:
|
||
- For each field: `meta[field] = { rationale: string, sources: string[] }` (optional).
|
||
- Caution: redact sensitive content; keep rationale brief and non-speculative.
|
||
|
||
## RAG-lite: Retrievable Context for Refresh
|
||
- Context sources:
|
||
- Latest website crawl snippets (top pages, headings, meta), recent analytics top pages (if connected), competitor headlines if available.
|
||
- Ingestion:
|
||
- Lightweight index (in-memory/SQLite) with page URL, title, summary; refresh on demand with TTL.
|
||
- Prompt strategy:
|
||
- Provide 3–5 top relevant snippets per category; keep token budget small.
|
||
- Controls:
|
||
- User toggle “Use live site signals” in refresh.
|
||
|
||
## API Additions
|
||
- Refresh
|
||
- GET `/autofill/refresh/stream?ai_only=true&constraints=...&mode=hybrid&use_rag=true`
|
||
- Non-stream POST variant mirrors params.
|
||
- Presets
|
||
- GET `/autofill/presets?industry=...&size=...®ion=...` → returns compact preset payload.
|
||
- Acceptances (existing)
|
||
- POST `/{strategy_id}/autofill/accept` → persist accepted fields with transparency/meta.
|
||
|
||
## UI Enhancements
|
||
- Per-field lock and regenerate
|
||
- Lock prevents overwrite; Regenerate calls sectioned refresh for that field’s category.
|
||
- Diff view on refresh
|
||
- Show before → after per field with accept/revert quick actions.
|
||
- Constraints chips
|
||
- Visible summary in header; edit inline.
|
||
- “Explain” modal
|
||
- Shows rationale and sources for the current value.
|
||
|
||
## Observability & Metrics
|
||
- Track per-field fill-rate, violation corrections, latency (per section), AI cost per refresh.
|
||
- Alert on sudden drops in non-null field count or spike in violations/timeouts.
|
||
|
||
## Rollout Plan
|
||
1) Phase 1 (Low risk): presets + constraints + per-field lock, no sectioning.
|
||
2) Phase 2: sectioned generation behind a feature flag; per-field regenerate.
|
||
3) Phase 3: RAG-lite snippets and explain modal; start learning from acceptances in prompts.
|
||
4) Phase 4: tune/fine-grain priors and add advanced validation rules per industry.
|
||
|
||
## References
|
||
- Gemini structured output: https://ai.google.dev/gemini-api/docs/structured-output |