process_log.md

Process Log — CPI Tender Matcher

AIMS KTT Hackathon · T2.2 · Multilingual Grant & Tender Matcher

Author: Samson Niyizurugero
Date: 2025-04-23
Total Time: ~3.5 hours

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⏱ Hour-by-Hour Timeline

Time	Activity
0:00–0:30	Read brief carefully. Identified 5 core deliverables: parser, ranker, summarizer, Gradio UI, village_agent.md. Sketched architecture on paper.
0:30–1:00	Built generate_data.py — synthetic tender generator producing 40 docs in EN/FR across 6 sectors and 4 budget tiers. Tested output, verified language distribution (60/40).
1:00–1:45	Built src/parser.py — rule-based field extraction (budget regex, deadline regex, language detection, sector keywords). Tested on 5 sample tenders manually.
1:45–2:15	Built src/ranker.py — TF-IDF vectorizer + hybrid scoring. Defined formula: 0.45×tfidf + 0.25×sector + 0.20×budget + 0.10×urgency. Justified weights with judge-explainability in mind.
2:15–2:30	Built src/summarizer.py — template-based summary generator in EN and FR. Ensured ≤80-word output. Added FR word choices for cooperative context.
2:30–2:45	Built matcher.py CLI — full pipeline orchestration. Added --profile, --all, --eval flags. Tested: python matcher.py --profile 02 --topk 5.
2:45–3:15	Built app.py — Gradio UI with profile dropdown, language selector, top-k slider, markdown output, JSON scores panel, plain-text audio summary.
3:15–3:30	Wrote village_agent.md — cost analysis for 3 delivery models with RWF math. Wrote privacy/consent plan.
3:30–3:45	Wrote README.md, SIGNED.md, process_log.md. Created evaluation notebook skeleton.
3:45–4:00	Final review: tested pipeline end-to-end, checked all files present, verified GitHub structure.

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🤖 LLM & Tool Usage Declaration

Tool	How Used
Claude (Anthropic)	Architecture planning, code scaffolding, debugging, documentation writing
GitHub Copilot	Inline autocompletion during coding
No other tools used	—

What I added beyond the LLM: Local context about cooperative financing in Rwanda, RWF cost estimates from real-world experience, word choice decisions in French summaries (e.g., "coopérative" vs "organisation"), debugging the TF-IDF index for multilingual corpus, and the urgency scoring function design.

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📝 Three Sample Prompts Sent to Claude

Prompt 1 (Used):

"Design a hybrid scoring function for tender-profile matching that combines TF-IDF cosine similarity, sector match, budget compatibility, and deadline urgency. Make it explainable for non-technical judges. Show the formula and justify each weight."

Why: I needed a scoring function that was both performant and easy to explain in a 4-minute demo. Claude suggested the 0.45/0.25/0.20/0.10 split which I kept after validating against sample profiles.

Prompt 2 (Used):

"Write a village_agent.md for rural Uganda deployment of a tender matching system. Include cost analysis for 3 options: voice call center, WhatsApp audio broadcast, printed bulletin. Give RWF math for 500 cooperatives."

Why: The product/business section is worth 20% of the score. I used Claude to structure the comparison table, then manually adjusted costs based on real Rwandan mobile data prices I know from experience.

Prompt 3 (Discarded):

"Use sentence-transformers paraphrase-multilingual-MiniLM-L12-v2 for embeddings in the ranker instead of TF-IDF."

Why discarded: After checking, the model is ~470MB — exceeding the 150MB constraint. I switched to TF-IDF with sklearn which is fast, CPU-only, and fully explainable. This was the right call for the constraint.

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🧩 Hardest Decision

The hardest decision was choosing between embedding-based similarity and TF-IDF.

The brief mentions paraphrase-multilingual-MiniLM as an option, which would give better cross-lingual retrieval (matching a French tender to an English profile). However, the MiniLM model at ~470MB violates the 150MB constraint.

I chose TF-IDF with ngram_range=(1,2) and sublinear_tf=True for three reasons:

1. It stays within the size constraint (no model file — vectorizer is built at runtime)
2. It runs in <1 second for 40 documents even on CPU
3. The formula is 100% explainable to judges — I can show the term weights live

The trade-off is weaker cross-lingual matching. I mitigated this by including language detection in the pipeline and boosting sector keywords during query construction.