PROPOSAL
https://drive.google.com/file/d/1TYNa3MDAAjVd9tiN14X8sr87mJvQ8bnI/view?usp=sharing

AIFAQ-ToIP Reporting Assistant — Proof of Concept”

Feed AIFAQ a limited amount of documentation (ToIP task force's GitHub repository, meeting minutes and project documentation)

Generate the next TAC report using the approved TAC template, with every substantive statement linked back to supporting ToIP source material.

Then evaluate it on accuracy, completeness, source traceability, hallucination rate and time saved.

AIFAQ a clearly defined technical role while keeping ToIP in control of standards and governance.

three layers AIFAQ:
The knowledge/RAG layer
the reusable reporting-skills layer
trust/provenance layer showing exactly how each AI artifact was generated.

That third layer could ultimately be the most strategically important part for AIFAQ because it extends well beyond ToIP reporting into enterprise AI governance.

“AIFAQ can provide a reference implementation of the Reporting Task Force's vision: an authoritative-source RAG system that transforms ToIP project knowledge into reusable, template-driven reports while preserving citations, provenance, auditability and human approval.”

Look into how AIFAQ can answer your question but prove (verify) the authenticity

1. Email David: Follow up with Carly

2. Test AIFAQ with Toip info

3. https://lf-toip.atlassian.net/wiki/spaces/HOME/overview?mode=global

4. https://docs.google.com/document/d/1JdZZF0cdbeC6E1tTbyojnHXsnAJYkdMHmjgr8wx_DG8/edit?tab=t.0

5. Re-group Thursdsay
   
   
   Use Case
   
   

AFAQ Trusted RAG pilot.

100 ToIP documents: GitHub issues, specifications, TAC reports, meeting minutes and presentations.

“What progress has ToIP made on AI agents during the last six months?”

AIFAQ answer:

SOURCE → AUTHOR/ORGANIZATION → AUTHENTICITY → DATE → VERSION → RETRIEVAL → AI AGENT → MODEL → POLICY → ANSWER

Chain forms provenance/audit trail. Then replacing ordinary assertions in that chain with cryptographically verifiable ToIP assertions.

Building it into AIFAQ.

That exact same architecture could underpin the AI-assisted ToIP Reporting project you are discussing.

AIFAQ could ingest the ToIP project ecosystem and produce a TAC report, but instead of generating an opaque AI-written report:

Every statement in the report could be traceable to authoritative project evidence.

That is almost a textbook demonstration of the 2026 TAC's call for evidence-carrying GenAI systems.

Meeting with Gianluca — September 8, 2026
Objective: Determine whether AIFAQ should pursue a ToIP reporting pilot and how it could evolve into a reusable LFDT AI knowledge and reporting capability.

• ToIP has a large body of information distributed across GitHub repositories, meeting notes, specifications, governance documents and presentations.

• Preparing TAC reports and other recurring updates remains largely manual.

• ToIP's reporting needs provide a strong real-world use case for AIFAQ.

• Question: Is this the right problem for AIFAQ to solve inside LFDT?

Concept: Use AIFAQ as an AI-assisted reporting and knowledge layer for ToIP.

Potential workflow:

ToIP sources → AIFAQ ingestion/RAG → source-grounded synthesis → human review → TAC/AMM reports

Pilot capabilities:

• Ingest selected ToIP GitHub repositories and meeting documentation.

• Search and summarize project activity.

• Identify important changes since the previous reporting period.

• Generate draft TAC reports and AMM updates.

• Answer questions with citations to the underlying ToIP sources.

• Maintain human review and approval before publication.

Discuss how this fits our existing work rather than becoming a separate product.

AIFAQ already provides:

• Multi-source knowledge ingestion

• RAG / AI retrieval

• Source-grounded answers

• Role-based access

• Governance and auditability

• Private/local AI deployment possibilities

Key positioning: AIFAQ isn't creating ToIP content—it helps ToIP find, organize, synthesize and report its existing trusted content.

Explore the progression:

ToIP Reporting Pilot → LFDT Reporting Tool → LFDT Knowledge Assistant → Agentic AI capabilities

Questions:

• Could the reporting workflow become reusable across LFDT projects?

• Should AIFAQ participate in emerging LFDT AI/Agentic AI work?

• Could ToIP become AIFAQ's reference implementation inside LFDT?

• Where does this fit with AIFAQ's private/local AI strategy?