Design

Product Boundary

The first user is a software job seeker. The product should begin with technical interview preparation because it gives clear task loops:

ask a question
receive an answer
grade against a rubric
ask follow-ups
extract memory
recommend review
show progress and next challenge

General students remain a future expansion path, but the first requirements should not be diluted by full K-12 scope.

Architecture Boundary

Use a Go backend as the product service boundary. Internalize agent-farm-go workflow patterns and contracts inside that backend boundary while keeping agent behavior configuration-first where possible. Use third-one as the LLM execution kernel with deepseek-v4-flash as the default configured model target.

The product backend owns durable user, learner, memory, ontology, and asset records. External memory or graph projects may inform design or become adapters, but they should not own product privacy or tenant semantics.

Memory Boundary

The platform should not model memory as a flat RAG corpus. It should keep structured learning state:

learner profile
concept mastery
misconception
evidence
intervention
review schedule

Every durable memory update must include evidence so the product can explain why it believes a learner is weak or strong on a concept.

Ontology Boundary

Uploaded materials should become source-backed learning graphs. Inferred gaps and generated explanations are candidates until reviewed or otherwise validated.

Visual Asset Boundary

Image generation should support diagrams and slide-like learning slices, but the asset pipeline must preserve prompt lineage, source concept links, and review state. The desired image provider key is gpt-image-v2, but production implementation must verify the current OpenAI API model name before wiring.

Gamification Boundary

Use game-design principles to create healthy persistence: adaptive challenge, visible growth, clear goals, strong session endings, and long-term readiness progress. Do not optimize for compulsion alone. Random rewards, punitive streaks, and shame-based leaderboards are out of scope for the first product baseline.

The initial learning loop is:

readiness goal
interview question
answer
rubric feedback
follow-up or correction
memory update
visible progress
next best challenge

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