Juq 699 Portable — Direct Link

| Metric | Target (6 months) | |--------|-------------------| | | ≥ 70 % of suggested tags accepted | | Search click‑through rate | ↑ 15 % vs. baseline keyword‑only search | | Average search latency | ≤ 500 ms | | Model inference latency | ≤ 300 ms | | Admin config changes | ≤ 2 per month (indicates stability) | | User satisfaction (NPS) | +5 points relative to pre‑launch baseline |

| ID | Requirement | Details | |----|--------------|----------| | | Real‑time Tag Suggestion | • Triggered after 3 characters in title/description. • Uses a lightweight transformer (e.g., DistilBERT) fine‑tuned on the organization’s corpus. • Returns up to 5 ranked tags with confidence scores. | | FR‑02 | Tag Acceptance UI | • Checkboxes next to each suggestion. • “Add all”, “Reject all”, and “Edit manually” actions. • Persist accepted tags in the item’s metadata. | | FR‑03 | Contextual Search API | • New endpoint POST /search/contextual that accepts free‑text queries. • Internally expands the query using the same model + synonym dictionary. • Returns ranked results with relevance score and highlighted matches. | | FR‑04 | Admin Settings Page | • Slider for confidence threshold (0‑1). • Multi‑select for allowed tag vocabularies (e.g., “Finance”, “HR”). • Toggle for “Auto‑apply tags without confirmation”. | | FR‑05 | Analytics Dashboard | • Charts: Tag suggestion acceptance rate, avg. confidence of accepted tags, search success (click‑through rate). • Export CSV/JSON. | | FR‑06 | Fallback & Privacy | • If the model fails (e.g., latency > 500 ms) fall back to keyword‑only suggestions. • No personal data leaves the organization; the model runs on‑prem or in a VPC‑isolated cloud environment. |

(under Settings → AI Features )

+-------------------+ +-------------------+ +-------------------+ | Front‑end (SPA) | <--API--> | API Gateway | <--gRPC-->| Tagging Service | +-------------------+ +-------------------+ +-------------------+ | ^ | | | | | | | v | v +----------+ +-----------+ | Search | | Model(s) | | Service | | (DistilBERT| +----------+ +-----------+

| Risk | Likelihood | Impact | Mitigation | |------|------------|--------|------------| | – tags become less relevant as language evolves | Medium | High | Schedule weekly re‑training on latest data; monitor acceptance rate and trigger auto‑retrain if < 60 %. | | Performance spikes during large batch uploads | Low | High | Use a separate “batch‑mode” endpoint that processes asynchronously and writes tags in bulk. | | Privacy breach if model sends data to external API | Low | Critical | Run all inference on‑prem; enforce data‑masking for any logging. | | User confusion over auto‑suggested tags | Medium | Medium | Provide clear UI affordances (tooltips, “Why this tag?”) and easy reject options. | | Tag taxonomy conflict with existing manual taxonomy | Medium | Medium | Admin UI to map auto‑generated tags to canonical taxonomy; allow black‑listing of problematic tags. | juq 699

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Users spend a lot of time manually classifying and locating content (documents, images, tickets, etc.). Existing keyword‑only search returns too many irrelevant results, while the current tagging system is static and requires users to remember a predefined taxonomy. • Returns up to 5 ranked tags with confidence scores

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| # | As a … | I want to … | So that … | |---|--------|-------------|-----------| | | Content Creator | see suggested tags as I type a title/description | I can quickly add accurate metadata without leaving the editor | | JUQ‑699‑02 | Content Creator | edit or accept/reject the suggested tags | I stay in control of the taxonomy and can correct any mistakes | | JUQ‑699‑03 | Power User | search using natural language (e.g., “draft proposals from last quarter”) | I find the exact items I need without remembering exact keywords | | JUQ‑699‑04 | Admin | configure the confidence threshold for auto‑tagging and whitelist/blacklist tag vocabularies | I can tune the system to match our domain terminology | | JUQ‑699‑05 | Product Analyst | view analytics on tag usage and search success rates | I can measure adoption and continuously improve the model | • Persist accepted tags in the item’s metadata

(global header)

adds an AI‑powered “Smart Tagging & Contextual Search” feature that automatically suggests high‑confidence tags while users create content and lets anyone search using natural‑language queries. It delivers faster, more relevant results, reduces manual tagging effort, and provides administrators with full control and analytics. The spec above includes user stories, functional & non‑functional requirements, UI sketches, architecture, rollout plan, success metrics, and risk mitigation so you can move straight into design and implementation.