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[GH-ISSUE #209] Strategy builder phased inputs generation. #485

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opened 2026-03-13 20:36:22 +03:00 by kerem · 0 comments

kerem commented

2026-03-13 20:36:22 +03:00

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Originally created by @AJaySi on GitHub (Aug 18, 2025).
Original GitHub issue: https://github.com/AJaySi/ALwrity/issues/209

Originally assigned to: @AJaySi, @uniqueumesh on GitHub.

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=...&region=... (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.

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=...&region=... → 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

Phase 1 (Low risk): presets + constraints + per-field lock, no sectioning.
Phase 2: sectioned generation behind a feature flag; per-field regenerate.
Phase 3: RAG-lite snippets and explain modal; start learning from acceptances in prompts.
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

Originally created by @AJaySi on GitHub (Aug 18, 2025). Original GitHub issue: https://github.com/AJaySi/ALwrity/issues/209 Originally assigned to: @AJaySi, @uniqueumesh on GitHub. ### 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=...&region=...` (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=...&region=...` → 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

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