75 lines
2.3 KiB
Markdown
75 lines
2.3 KiB
Markdown
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# Design
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## Product Boundary
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The first user is a software job seeker. The product should begin with
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technical interview preparation because it gives clear task loops:
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- ask a question
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- receive an answer
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- grade against a rubric
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- ask follow-ups
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- extract memory
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- recommend review
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- show progress and next challenge
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General students remain a future expansion path, but the first requirements
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should not be diluted by full K-12 scope.
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## Architecture Boundary
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Use a Go backend as the product service boundary. Internalize `agent-farm-go`
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workflow patterns and contracts inside that backend boundary while keeping agent
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behavior configuration-first where possible. Use `third-one` as the LLM
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execution kernel with `deepseek-v4-flash` as the default configured model
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target.
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The product backend owns durable user, learner, memory, ontology, and asset
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records. External memory or graph projects may inform design or become adapters,
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but they should not own product privacy or tenant semantics.
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## Memory Boundary
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The platform should not model memory as a flat RAG corpus. It should keep
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structured learning state:
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- learner profile
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- concept mastery
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- misconception
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- evidence
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- intervention
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- review schedule
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Every durable memory update must include evidence so the product can explain why
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it believes a learner is weak or strong on a concept.
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## Ontology Boundary
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Uploaded materials should become source-backed learning graphs. Inferred gaps
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and generated explanations are candidates until reviewed or otherwise validated.
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## Visual Asset Boundary
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Image generation should support diagrams and slide-like learning slices, but the
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asset pipeline must preserve prompt lineage, source concept links, and review
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state. The desired image provider key is `gpt-image-v2`, but production
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implementation must verify the current OpenAI API model name before wiring.
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## Gamification Boundary
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Use game-design principles to create healthy persistence: adaptive challenge,
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visible growth, clear goals, strong session endings, and long-term readiness
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progress. Do not optimize for compulsion alone. Random rewards, punitive streaks,
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and shame-based leaderboards are out of scope for the first product baseline.
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The initial learning loop is:
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- readiness goal
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- interview question
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- answer
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- rubric feedback
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- follow-up or correction
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- memory update
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- visible progress
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- next best challenge
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