# Roadmap: Tutor Platform

## Milestone 1: Job-Seeker Interview Tutor MVP

### Phase 1: Go Backend Foundation and Workflow Boundary

**Goal:** Establish the Go service skeleton and typed workflow boundary for
internalized `agent-farm-go` patterns.

**Requirements:** BACK-01, BACK-02, BACK-03, BACK-04, BACK-05

**Success Criteria:**

- Go backend scaffold exists with clear module boundaries.
- No manually authored source file exceeds 600 lines.
- Workflow interfaces are typed and isolated from HTTP handlers.
- Runtime config can identify the `third-one` / `deepseek-v4-flash` target.
- Basic build/test command is documented in `AGENTS.md`.

### Phase 2: Diagnostic Interview Loop

**Goal:** Prove the first job-seeker loop from role selection through graded
diagnostic interview.

**Requirements:** INT-01, INT-02, INT-03, INT-04, INT-05, INT-06

**Success Criteria:**

- User can choose target role and stack.
- Backend can create a diagnostic session.
- System produces role-specific interview questions.
- User answers are graded through typed workflow results.
- Grading evidence and original answer are persisted.

### Phase 3: Learner Memory

**Goal:** Convert graded answer evidence into structured learner memory.

**Requirements:** MEM-01, MEM-02, MEM-03, MEM-04, MEM-05

**Success Criteria:**

- Learner profile is persisted.
- Concept mastery updates require evidence.
- Misconceptions link to supporting answers.
- Session context and durable memory remain separate.
- Memory extraction workflow emits typed candidates.

### Phase 4: Progression and Gamified Learning Routine

**Goal:** Make readiness and next challenge visible without empty rewards.

**Requirements:** PROG-01, PROG-02, PROG-03, PROG-04, PROG-05

**Success Criteria:**

- Readiness map displays concept states.
- Challenge ladder exists for the first backend interview track.
- Next challenge is selected from learner memory and grading evidence.
- Boss question unlocks after prerequisite stability.
- Streak/reward behavior avoids punitive and random-reward mechanics.

### Phase 5: Source-Backed Ontology Builder

**Goal:** Start material ingestion and ontology candidate generation.

**Requirements:** ONTO-01, ONTO-02, ONTO-03, ONTO-04

**Success Criteria:**

- User/operator can add source material.
- Concepts, prerequisites, rubrics, and question candidates carry provenance.
- Missing prerequisites and weak areas are flagged.
- Generated/inferred content is not promoted as canonical automatically.

### Phase 6: Visual Teaching Asset Pipeline

**Goal:** Generate reviewable teaching asset candidates from ontology concepts.

**Requirements:** ASSET-01, ASSET-02, ASSET-03

**Success Criteria:**

- Asset prompt generation contract exists.
- Generated assets store prompt lineage, source concept, source evidence, model
  config, and review state.
- Actual image model identifier is verified before production image calls.

## Parking Lot

- General student mode.
- Teacher/parent dashboards.
- School tenant administration.
- Company-specific interview packs.
- Human ontology review console.

## Milestone 2: Frontend MVP

### Phase 7: Web App Shell and Diagnostic Start

**Goal:** Serve the first web app from the Go service and let a job seeker
start diagnostic practice without API tooling.

**Requirements:** WEB-01, WEB-02, WEB-03

**Success Criteria:**

- Go service serves a web app at `/`.
- User can enter target role, stack, and interview timeline.
- User can create a diagnostic session from the browser.
- User can submit an answer and see typed grading feedback.
- UI has loading and error states for the diagnostic flow.

### Phase 8: Learning Progress View

**Goal:** Show evidence-backed learning progress after practice.

**Requirements:** WEB-04

**Success Criteria:**

- User can see learner profile and concept mastery after answering.
- User can see readiness percentage and concept ladder state.
- User can see the next recommended challenge and its evidence.
- Empty states explain what to do before memory/progression exists.

### Phase 9: Material and Asset Workspace

**Goal:** Let an operator use ontology and teaching asset prompt workflows from
the web app.

**Requirements:** WEB-05, WEB-06, WEB-07, WEB-08

**Success Criteria:**

- Operator can ingest text material from the browser.
- Operator can inspect ontology candidate concepts, edges, and gaps.
- Operator can generate teaching asset prompt candidates from a concept.
- UI clearly shows candidate review state, source evidence, and model
  verification guard.

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*Roadmap updated: 2026-04-26 after v2 Frontend MVP milestone start.*