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Classification: AI peer matching experiment

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評估

難度
5/5
預估耗時
一週以上
新手友好度
25/100
Issue 類型
功能
描述清晰度
需要釐清
活躍度
冷清
技術堆疊
machine-learning, python
領域
backend, machine-learning

研究方向

從現有的 AI Assessments pipeline 開始，了解它如何接受指南或評分規準並產生帶有分數的 bucket。整理出提議的約 30 篇短篇故事組成的 dummy 集合及其寫作指南，然後執行初始的 organic bucketing，並將結果與 Deodar 的分類進行比較。完成的標準是記錄該實驗是否足夠可靠，從而有理由迭代 prompt 和評分規準。

由索引模型根據 Issue 內容生成。

描述

Is your feature request related to a problem?
Deodar's Use Case 1 (submission cleanup) is on hold due to low volume. The real issue is Use Case 2: classifying writers for peer matching, as new writers need credible feedback and peer groups of similar skill. The challenge is whether AI can classify 50–100+ writers reliably.

Describe the solution you'd like

  • Assemble a dummy set of ~30 short stories (good/middling/bad) with guidelines.
  • Experiment with AI by:
    1. Providing samples and guidelines to the AI for organic bucketing.
    2. Comparing AI's buckets with Deodar's.
    3. Asking AI to propose a rubric and provide scoring and feedback.
  • Use prompt engineering without model training; iterate the rules for improvement.
  • Ensure existing AI Assessments pipeline is utilized for classification tasks.
  • Kaapi to assist with prompt structure and initial rounds, and provide access for self-iteration afterwards.
Original issue

Context

Deodar's Use Case 1 (submission cleanup) is parked — volume (~700–800/year) doesn't justify AI. The real problem is Use Case 2: classifying writers for peer matching. New writers need credible feedback and want peer groups at or above their own skill. Deodar can bucket 30–40 stories by hand; the question is whether AI can do this reliably at 50–100+ writers. The AI's job is classification at the entry point only — assign a writer to the right room; everything after is human-to-human.

Consent blocker & workaround

Deodar needs to take permission from writers at submission and the stories are the writers' own product, so real submissions can't be sent. Workaround: Deodar assembles a dummy set of ~30 short stories (good/middling/bad, free to share) plus a written guideline (not a rubric) on what makes writing good/bad and what characterises Indian fiction.

First experiment

  1. Give the AI the 30 samples + guideline; let it bucket organically into top/middle/bottom.
  2. Compare its buckets against Deodar's.
  3. Ask the AI to propose its own rubric; score and give feedback per story; sample-check; iterate.
  • No model training — entirely prompt engineering (3–6 page prompts workable). First round will underperform; value is in iterating the rules.
  • Platform fit: the existing AI Assessments pipeline works (opinionated toward assessment, but classification uses the same rubric-in/scored-buckets-out mechanism). Kaapi stays involved for 2–3 iterations, then hands Deodar a UI to self-iterate.

Notes

  • Product shape (login → upload → AI feedback emailed; gated persona → room assignment) is exploratory, not committed. Platform must disclose AI is the first-level reader.
  • Volume assumptions (50–100 simultaneous writers) are aspirational; market viability unvalidated; no internal deadline.

Next steps (Kaapi)

  • Help structure the prompt and rubric; run the first rounds jointly; provide self-serve platform access once early rounds show promise.
主要語言
Python
星號
18
分支
11
平均合併
3 天 20 小時
30 天內合併 PR
14

環境準備

從這裡開始

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  2. 在 Issue 下留言說明你要接手 —— 這能避免兩個人做同樣的事。
  3. Fork 儲存庫，在一個分支上完成修改。
  4. 送出 Pull Request，並在描述裡引用這個 Issue 編號。

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