020_Output_Refinement_Tutorial

020_Output_Refinement_Tutorial is a cursor rule for Cursor from AIFlowML/cursor_rules. It costs 10,033 tokens per session, scanned A, original, no licence file.

A production-oriented setup for refining AI-generated answers with retries and feedback. BestOfN compares several attempts, while Refine improves an answer in stages.

In plain words
What is it for?
Use it to retry, score, and refine DSPy predictions with feedback loops and reward functions.
Why use it?
It helps reduce inconsistent or poor outputs when an AI prediction needs to meet a quality standard.

Cursor rule for Cursor

Written for Cursor: installed under .cursor/.

Good fit Use it to retry, score, and refine DSPy predictions with feedback loops and reward functions.

Compare 6 cursor rules from other repositories ↓
Install with agentmods
npx agentmods add rules/aiflowml/cursor_rules/020_output_refinement_tutorial
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Clone the repo
git clone --depth 1 https://github.com/AIFlowML/cursor_rules

Made for: Cursor.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for 020_Output_Refinement_Tutorial

README.md
[![agentmods](https://agentmods.dev/badge/rules/aiflowml/cursor_rules/020_output_refinement_tutorial/github.svg)](https://agentmods.dev/rules/aiflowml/cursor_rules/020_output_refinement_tutorial)
Your own site
<a href="https://agentmods.dev/rules/aiflowml/cursor_rules/020_output_refinement_tutorial"><img src="https://agentmods.dev/badge/rules/aiflowml/cursor_rules/020_output_refinement_tutorial/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for 020_Output_Refinement_Tutorial

Your own site · 80×15
<a href="https://agentmods.dev/rules/aiflowml/cursor_rules/020_output_refinement_tutorial"><img src="https://agentmods.dev/badge/rules/aiflowml/cursor_rules/020_output_refinement_tutorial.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 10,033 This file is loaded in full into every session.
When invoked 10,033 The same file — it is already loaded in full.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin unknown No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.10033 $0.10033
Opus 5.5 $0.04013 $0.04013
Sonnet 5.5 $0.02007 $0.02007
Haiku 4.5 $0.01003 $0.01003

Measured 1mo ago against content hash 5c8d1c07c5b5, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

020_Output_Refinement_Tutorial scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

.cursor/rules/DSPy_3_examples/020_Output_Refinement_Tutorial.mdc · 1,254 lines

The source is not reproduced here

No licence file

A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.

Read it on GitHub

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 1mo ago First seen · 1,254 lines · 10,033 tokens per session scan A 5c8d1c07c5b5

Subscribe to this mod's changes

020_Output_Refinement_Tutorial is a cursor rule published in the GitHub repository AIFlowML/cursor_rules (24 stars, last pushed 1y ago), with no licence file. It adds 10,033 tokens to every session, about $0.0401 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.