What is the best way to do a open source data driven policy

[dense ravine]

What is the best way to do a open source data driven economic policy

[shrewd parrot]

Hi @dense ravine. I'm kapa.ai, your friendly support bot. I will try to answer your question by searching through the knowledge base. :wave:

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I’m sorry, but the knowledge sources provided don’t address how to design an open‑source, data‑driven economic policy or public policy framework in general. They focus on:

• Governance and guardrails for AI agents and applications, including how to centralize and package “policy as code” for AI usage (e.g., content rules, PII handling, jailbreak detection). [Governed agents; Policy package]
• Eval‑driven system design and best practices for testing and iterating on AI guardrails. [Eval best practices; Eval‑driven design]
• Trust & Safety “bring‑your‑own‑policy” classification using gpt‑oss‑safeguard, and how to write effective policy prompts for moderation. [gpt‑oss‑safeguard overview; Policy prompts]
• Deep research models (like o3-deep-research) for doing data‑rich research reports, including on economic topics such as the impact of semaglutide on healthcare systems. [Deep research guide; Deep research example]

They do not describe methodologies for building open‑source, data‑driven economic policy (e.g., tax, trade, industrial policy) for governments or NGOs.

If your question is instead about:

• Creating open, data‑driven AI safety or moderation policies (for example, open‑sourcing your Trust & Safety rules and using models to enforce them), then the closest patterns are:

  • “Policy as code” in a shared repo, versioned and reused across projects. [Policy repo steps]
  • “Bring‑your‑own‑policy” classification with gpt‑oss‑safeguard, where your written policy plus examples drive decisions. [What is gpt‑oss‑safeguard; Policy prompts]

• Or using OpenAI tools to research economic questions in a data‑driven way, then you’d use o3-deep-research or o4-mini-deep-research with web search and (optionally) your own internal data to generate citation‑backed economic analyses. [Deep research guide]

If you can clarify whether you mean:

1. Open‑source AI governance / safety policy, or
2. Open‑source macroeconomic / public economic policy,

I can point more precisely to what (if anything) in these OpenAI resources applies.

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[dense ravine]

@shrewd parrot use academic sudamerican authors

[shrewd parrot]

Let me check the knowledge base for you again. One moment! :books:

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The knowledge sources you provided don’t contain guidance on designing an open‑source, data‑driven economic policy using South American (or “Sudamerican”) academic authors. They instead cover:

• OpenAI’s Deep Research API and how to use it for data‑rich research reports. [Deep research intro; Deep research guide]
• An OpenAI–Kaggle challenge about Amazonian archaeology, which lists academic references on Amazonian archaeology and remote sensing, but not on economic policy. [Kaggle dataset refs]
• Governance and “policy as code” for AI systems, plus gpt‑oss‑safeguard for Trust & Safety policies, which are about AI moderation policies, not macroeconomic or development policy. [Agentic governance guide; gpt‑oss‑safeguard guide]

None of these sources specify:

• Which South American economists or social scientists to use, or
• A methodology for building an open‑source, data‑driven economic policy grounded in those authors.

What you can do with the available tools is:

• Use o3-deep-research or o4-mini-deep-research to run a structured literature review on South American academic work (e.g., development economics, inequality, industrial policy), by enabling web search and asking for citation‑rich output. [Deep research guide; Deep research example]
• Treat your eventual economic policy as “policy as code” in a repo, similar to how the governance cookbook packages AI guardrails, so it’s versioned, reusable, and transparent. [Policy repo steps]

But the specific request “use academic South American authors” for economic policy design is not addressed in the knowledge sources, so I can’t list authors or a concrete framework without going beyond them.

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[dense ravine]

@shrewd parrot what are the following phases?

[shrewd parrot]

Let me check the knowledge base for you again. One moment! :books:

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The knowledge sources don’t clearly define “phases” for your earlier question about open‑source, data‑driven (economic) policy, so I can’t say with certainty what “the following phases” refers to.

However, there is a clearly defined “Project Lifecycle” with phases for building AI systems in one of the sources. Those phases are: [Project lifecycle]

1. Understand the Problem

   • Interview domain experts, clarify business impact, and keep refining understanding as you go.

2. Assemble Examples (Gather Data)

   • Collect input data (e.g., receipt images) even if you don’t yet have full labels.

3. Build an End-to-End V0 System

   • Create a minimal working system that accepts real inputs and produces correctly shaped outputs, even if quality is low.

4. Label Data and Build Initial Evals

   • Use the early system to generate draft outputs, have experts correct them, and turn those into evaluation datasets.

5. Map Evals to Business Metrics

   • Connect eval results to real business costs/benefits and prioritize what matters most.

6. Progressively Improve System and Evals

   • Iterate on both the system and the evals, using evals as an objective guide and to catch regressions.

If you meant a different set of “phases,” the knowledge sources don’t specify them, so I can’t answer more precisely.

Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:

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[dense ravine]

@shrewd parrot how can someone introduce it to a university as experiment with metrics?

[shrewd parrot]

Let me check the knowledge base for you again. One moment! :books:

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You can frame it as an eval‑driven pilot project and borrow patterns from how OpenAI recommends moving from prototype to production with clear metrics.

A concise way to introduce it at a university:

1. Define the pilot and success metrics up front

   • Pick one bounded use case (e.g., an AI assistant for a specific course, or a governed agent for a research lab). [Phase 1 pilot]
   • Propose quantitative KPIs/SLOs: accuracy on tasks, student satisfaction, time saved, cost per query, latency, etc., and ensure they’re measurable from logs (tokens, error rates, approval/rejection rates). [TL;DR matrix]

2. Use a project‑lifecycle structure
   Present a simple 4–6 phase plan adapted from the eval‑driven lifecycle: [Project lifecycle]

   1. Understand the problem (interview faculty/students about the target workflow).
   2. Assemble examples (collect representative prompts, documents, or tasks).
   3. Build an end‑to‑end V0 system (minimal working assistant/agent).
   4. Label data & build evals (faculty “grade” outputs; turn into a golden set).
   5. Map evals to university metrics (learning outcomes, time saved, error reduction).
   6. Iterate and improve based on eval results.

3. Propose governance and observability from day one

   • Use “policy as code” and guardrails (e.g., PII detection, moderation, off‑topic filters) to address ethics and compliance concerns; show that all calls are governed automatically. [Centralizing governance; Policy config]
   • Enable tracing/observability so you can audit behavior and measure performance; or, if needed, disable tracing for strict data‑retention requirements. [Basic observability]

4. Set up evaluation and reporting as the “experiment”

   • Build an automated eval suite (golden set of tasks) and run it regularly to track accuracy, hallucination rate, and tool‑error rate. [TL;DR matrix]
   • Map eval scores to educational or operational metrics (e.g., grading consistency, turnaround time). [Map evals to business metrics]
   • Commit to a review cadence and assign owners for adoption and compliance reporting, similar to enterprise governance setups. [Governance setup]

5. Position it as a safe, governed pilot
   • Start with a limited group (one course or lab), with human‑in‑the‑loop review for high‑risk outputs. [Safety & compliance]
   • Present clear criteria for expansion or rollback based on the agreed metrics.

The knowledge sources don’t mention universities specifically, but this pattern—small pilot, explicit metrics, eval‑driven iteration, and centralized guardrails—is exactly how they recommend introducing AI systems safely in larger organizations, and you can present it to a university ethics/IT committee in the same structure.

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[dense ravine]

@shrewd parrot whats the citing loop in research with AI and automatization

[shrewd parrot]

Let me check the knowledge base for you again. One moment! :books:

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There isn’t a single, named concept called “the citing loop” in the knowledge sources, but they do describe how citation and evidence work in a loop-like workflow when you use OpenAI’s research tools. Here’s what is covered:

1. Retrieval → Answer → Citation loop

When you use tools like file_search, web_search_preview, or Deep Research models, the model:

1. Retrieves sources (files, web pages, MCP documents) via tools. [Deep research guide; File search tool]
2. Generates an answer based only on those sources, if you instruct it to do so (e.g., “Answer questions based ONLY on the provided paragraphs”). [Answer generation]
3. Attaches citations as annotations to specific spans of text in the answer:
   • For Deep Research: url_citation annotations with title, url, and character indices. [Parse response]
   • For file search: file_citation annotations with file_id and filename. [File search response]

You can then inspect those citations programmatically, which closes the loop between claim → source:

annotations = response.output[-1].content[0].annotations
for i, citation in enumerate(annotations):
    print(citation.title, citation.url, citation.start_index, citation.end_index)
``` [[Parse response](<https://developers.openai.com/cookbook/examples/deep_research_api/introduction_to_deep_research_api#parse-the-response>)]

2. Locking research to retrieved evidence

To avoid a bad “citing loop” where the model invents references, the prompt should:

• Require that it only cite sources retrieved in the current workflow.
• Forbid fabricated URLs/IDs.
• Require citations to be attached to the specific claims they support. [Prompt guidance – grounding & citations]

This turns citation into a disciplined loop: retrieve → ground claims → cite only retrieved evidence.

3. Automated, structured citation control

You can also force citations into a structured field and validate them:

• In the legal-answer example, the model outputs citations: List[str] and a validator checks that each citation is one of the allowed paragraph IDs. [Answer generation]

This lets you automate:

1. Retrieval and answer generation.
2. Automatic citation extraction.
3. Automatic validation that citations refer to real, retrieved items.

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So, while the sources don’t define “citing loop” as a term, they describe a practical loop for AI research and automation:

retrieve evidence → generate answer → attach machine-readable citations → programmatically inspect/validate them → refine prompts/tools if citations are weak or missing.

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[dense ravine]

@shrewd parrot how you evaluate green computing

[shrewd parrot]

Let me check the knowledge base for you again. One moment! :books:

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The knowledge sources don’t define “green computing” or give a dedicated evaluation framework for it, so I can’t provide an official OpenAI methodology. I can only point to related evaluation and monitoring patterns that could be adapted.

From the available material, evaluation is generally framed around defining metrics, logging them, and running continuous evals:

1. Define explicit objectives and metrics
   The eval best‑practices guide stresses: define your eval objective, then choose metrics that can be measured from logs. [Design eval process]
   By analogy, for green computing you would:

   • Define what “green” means in your context (e.g., lower energy per request, lower total compute, fewer redundant runs).
   • Choose measurable proxies (e.g., total tokens, number of tool calls, latency, batch size) that correlate with resource use.

2. Instrument and log what you can measure
   Several docs emphasize logging per‑request metrics such as model used, tokens, and latency for cost and performance monitoring. [Deployment notes – monitoring; Online evaluation metrics]
   For a “green” focus, you’d reuse this pattern:

   • Log llm_model_used, llm_input_tokens, llm_output_tokens, llm_latency_ms, and number of calls per task.
   • Treat “less compute for the same quality” as a key metric.

3. Use evals to compare configurations
   The eval guides recommend:

   • Designing task‑specific evals and running them whenever you change models or prompts. [Eval best practices; Design eval process]
     Applied to green computing, you would:
   • Hold quality constant (via an eval suite) and compare different model choices or prompting strategies on total tokens / latency.
   • Prefer configurations that meet your quality thresholds with lower resource usage.

4. Set guardrails and modes that implicitly affect footprint
   Deployment notes suggest configurable “modes” (Fast / Standard / Thorough) and token limits to control cost and latency. [Deployment notes – co‑scientist; TL;DR matrix – cost guardrails]
   The same mechanisms can be used as green levers:

   • Cap tokens per stage.
   • Use cheaper/smaller models where possible.
   • Reserve heavier reasoning models only for cases where evals show they’re needed.

Because the sources don’t mention energy, carbon, or environmental impact directly, they don’t tell you how to convert these metrics into kWh or CO₂e. You’d need external data for that. Within the OpenAI material, the closest you can get is:

• Treat compute usage (tokens, calls, latency) as your primary measurable proxy.
• Use eval‑driven comparisons to minimize that proxy while maintaining task quality.

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[dense ravine]

@shrewd parrot Can I use codex with R functions?

[shrewd parrot]

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Yes. You can use Codex to work with R code and R functions, but there are two different scenarios:

1. Using Codex as a coding agent on your R project (recommended)
   Codex is language‑agnostic: it reads, edits, and runs whatever code is in your repo, including R. You just point Codex (app, IDE extension, or CLI) at a project folder that contains your .R files, and then ask it to write, refactor, or debug R functions. [Codex overview; Quickstart – select a project; Prompting examples]

   Typical workflow:

   • Open your R project folder in VS Code / Cursor / Windsurf.
   • Install and sign into the Codex IDE extension. [Codex IDE extension]
   • In the Codex panel, ask things like:
     • “Write an R function that fits a linear model and plots residuals.”
     • “Refactor this R script into reusable functions.”
       Codex will read the existing files and propose edits or new R functions in place.

2. Calling Codex‑tuned models via the API to generate R code
   The GPT‑5‑Codex family is optimized for “agentic coding tasks in Codex or similar environments,” and is available via the Responses API. [GPT‑5‑Codex model; Code generation guide]
   You can send a prompt like “Write an R function that…” and get back R code as text. The docs don’t show an R‑specific example, but they state these models are for general code generation, and language choice is controlled by your prompt.

Limitations / notes from the sources:

• GPT‑5‑Codex is “available in the Responses API only,” and the help article notes it is not currently supported in ChatGPT itself; it’s meant for Codex and API use. [GPT‑5‑Codex model; ChatGPT business notes]
• Codex itself is an agent layer; it doesn’t restrict you to specific programming languages, so using it on R projects is supported as long as your environment (IDE / CLI / app) is set up. [Codex overview; Codex IDE extension]

So: you can absolutely use Codex to write and work with R functions—either interactively in your R project via the Codex tools, or programmatically via the API models tuned for coding.

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