How to Use R2R for Output refinement passes

SoTA production-ready AI retrieval system. Agentic Retrieval-Augmented Generation (RAG) with a RESTful API. - SciPhi-AI/R2R Confirm live details on r2r.com/cgi-sys/suspendedpage.cgi before production use.

Practical Output refinement passes examples

Example 1

Scenario:
R2R — Output refinement passes (pass 1). Context: SoTA production-ready AI retrieval system. Agentic Retrieval-Augmented Generation (RAG) with a RESTful API. - SciPhi-AI/R2R

Objective:
Deliver a reviewable output refinement passes result using R2R.

Inputs:
- Verified facts from r2r.com/cgi-sys/suspendedpage.cgi
- Audience, channel, or technical constraints
- Success criteria and forbidden claims

Workflow:
Open R2R → Configure for output refinement passes → Pilot with sample inputs → Review against success criteria → Iterate one axis → Finalize

Requirements:
- Use only verified R2R capabilities; do not invent features.
- Confirm live details on r2r.com/cgi-sys/suspendedpage.cgi before promising volume or pricing.
- Human-review before external publish, send, billing, or compliance use.

Expected output:
A concrete output refinement passes artifact plus a short verification checklist.

Example 2

Scenario:
R2R — Output refinement passes (pass 2). Context: SoTA production-ready AI retrieval system. Agentic Retrieval-Augmented Generation (RAG) with a RESTful API. - SciPhi-AI/R2R

Objective:
Deliver a reviewable output refinement passes result using R2R.

Inputs:
- Verified facts from r2r.com/cgi-sys/suspendedpage.cgi
- Audience, channel, or technical constraints
- Success criteria and forbidden claims

Workflow:
Open R2R → Configure for output refinement passes → Pilot with sample inputs → Review against success criteria → Iterate one axis → Finalize

Requirements:
- Use only verified R2R capabilities; do not invent features.
- Confirm live details on r2r.com/cgi-sys/suspendedpage.cgi before promising volume or pricing.
- Human-review before external publish, send, billing, or compliance use.

Expected output:
A concrete output refinement passes artifact plus a short verification checklist.

Example 3

Scenario:
R2R — Output refinement passes (pass 3). Context: SoTA production-ready AI retrieval system. Agentic Retrieval-Augmented Generation (RAG) with a RESTful API. - SciPhi-AI/R2R

Objective:
Deliver a reviewable output refinement passes result using R2R.

Inputs:
- Verified facts from r2r.com/cgi-sys/suspendedpage.cgi
- Audience, channel, or technical constraints
- Success criteria and forbidden claims

Workflow:
Open R2R → Configure for output refinement passes → Pilot with sample inputs → Review against success criteria → Iterate one axis → Finalize

Requirements:
- Use only verified R2R capabilities; do not invent features.
- Confirm live details on r2r.com/cgi-sys/suspendedpage.cgi before promising volume or pricing.
- Human-review before external publish, send, billing, or compliance use.

Expected output:
A concrete output refinement passes artifact plus a short verification checklist.

Example 4

Scenario:
R2R — Output refinement passes (pass 4). Context: SoTA production-ready AI retrieval system. Agentic Retrieval-Augmented Generation (RAG) with a RESTful API. - SciPhi-AI/R2R

Objective:
Deliver a reviewable output refinement passes result using R2R.

Inputs:
- Verified facts from r2r.com/cgi-sys/suspendedpage.cgi
- Audience, channel, or technical constraints
- Success criteria and forbidden claims

Workflow:
Open R2R → Configure for output refinement passes → Pilot with sample inputs → Review against success criteria → Iterate one axis → Finalize

Requirements:
- Use only verified R2R capabilities; do not invent features.
- Confirm live details on r2r.com/cgi-sys/suspendedpage.cgi before promising volume or pricing.
- Human-review before external publish, send, billing, or compliance use.

Expected output:
A concrete output refinement passes artifact plus a short verification checklist.

Example 5

Scenario:
R2R — Output refinement passes (pass 5). Context: SoTA production-ready AI retrieval system. Agentic Retrieval-Augmented Generation (RAG) with a RESTful API. - SciPhi-AI/R2R

Objective:
Deliver a reviewable output refinement passes result using R2R.

Inputs:
- Verified facts from r2r.com/cgi-sys/suspendedpage.cgi
- Audience, channel, or technical constraints
- Success criteria and forbidden claims

Workflow:
Open R2R → Configure for output refinement passes → Pilot with sample inputs → Review against success criteria → Iterate one axis → Finalize

Requirements:
- Use only verified R2R capabilities; do not invent features.
- Confirm live details on r2r.com/cgi-sys/suspendedpage.cgi before promising volume or pricing.
- Human-review before external publish, send, billing, or compliance use.

Expected output:
A concrete output refinement passes artifact plus a short verification checklist.

Checklist before you ship

• Confirm the workflow stays inside verified R2R capabilities
• Review outputs against r2r.com/cgi-sys/suspendedpage.cgi when accuracy or pricing claims matter
• Keep a short verification list for any claim you would publish externally