Showing results for "recency"
Design retrieval that avoids irrelevant context: recency weighting, scope filters, and contradiction detection. Include evals for context precision/recall.
Author: Assistant
Category: safe-self-improving-ai | Model: gpt-5.2
Design a freshness strategy for research: recency-aware search, change detection, and update alerts. Include how to present “as of” dates and avoid outdated claims.
Author: Assistant
Category: research-bot | Model: GPT-5.2
Create a source scoring rubric: domain trust, author expertise, citations, recency, and conflicts of interest. Output a weighted scoring model and how to tune it per domain.
Author: Assistant
Category: research-bot | Model: GPT-5.2
Explain how to balance recency and reliability. Create a decision framework for when older sources are better (textbooks, standards) vs when fresh updates matter (outages, policy changes).
Author: Assistant
Category: information-reliability | Model: GPT-5.2
Create a source quality rubric I can score from 0–5 across authority, transparency, evidence, recency, incentives, and track record. Provide examples of how to score a news article, a blog post, and a...
Author: Assistant
Category: information-reliability | Model: GPT-5.2
Design a time-decay model that boosts fresh, authoritative sources. Include publisher priors, sitemap/news bias, and re-crawl triggers. Output evaluation with time-split test sets.
Author: Assistant
Category: ranking-freshness | Model: gpt-4o
Propose a lead-scoring model: behavior weights (visits, time, events), firmographics, recency. Include a cutoff policy and a feedback loop from sales outcomes. Return as a table + pseudocode.
Author: Curioforce Corp. Corp.
Category: Small Business Automation | Model: gpt-5-thinking
Goal: design segmentation and CDP activation. Data: events, traits, CRM. Steps: 1) Build segments (recency, behavior, value); 2) Define activation triggers; 3) Map to channels via CDP (e.g., Segment);...
Author: Tsubasa Kato
Category: Web Analytics | Model: GPT-5 Thinking
Goal: model LTV with RFM and Pareto/NBD. Data: orders table + GA4 user_id mapping. Steps: 1) Derive Recency, Frequency, Monetary; 2) Fit Pareto/NBD (BG/NBD) and Gamma-Gamma for monetary; 3) Validate w...
Author: Tsubasa Kato
Category: Web Analytics | Model: GPT-5 Thinking