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Comment “APP” for the tools + master prompt @blckbx.ai
Does your vibe coded website look like this or this
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LinkedIn has so much data that could be SO much more useful for all of us Ill show you what I did and found out Im sure there are a bunch of other cool things you could do with the data
Claude Cote and I just basically unlocked LinkedIn
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Always start with one cup of cottage cheese Follow @its.me.crushit for memes #gym #gymmotivation #gymmeme #gymmemes
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![In enterprise RAG, “retrieved from XYZ.pdf” is NOT enough.
Legal & compliance teams want precise provenance — clause, page, section, and even bounding boxes.
Here’s how real teams build traceable RAG:
⸻
1️⃣ Store rich metadata at ingestion
Every chunk must store:
• document ID
• section/clause ID
• page number
• PDF bounding box
• version ID + timestamps
This ensures every chunk points back to the exact source location.
⸻
2️⃣ Retrieval must return metadata, not just text
Retriever output =
{chunk_text, doc_id, section_id, page_no, coords}
This metadata flows end-to-end through the system.
⸻
3️⃣ Log what was actually used
Your pipeline should log:
• which chunks were retrieved
• which ones were fed into the model
• which ones were cited in the final answer
Perfect for audits.
⸻
4️⃣ UI-level inline citations
Display answers like:
“…per policy [Doc 12, clause 4.3]”
Tapping it expands to the exact paragraph/page.
This removes ambiguity for legal teams.
⸻
5️⃣ Use Traceability Tools (optional but powerful)
Teams often plug in:
• Arize AI → monitors retrieved chunks vs. generated answer
• TruLens → faithfulness, citations, trace graphs
• WhyLabs → data + retrieval drift monitoring
• LlamaIndex Observability → end-to-end provenance tracing
These tools generate trace graphs showing EXACT which chunk impacted each sentence.
⸻
6️⃣ Full audit trail
Store everything per query:
• user input
• retrieved chunks & metadata
• model output
• cited source locations
This is mandatory for regulated domains.
⸻
⭐ Why it matters
This is how enterprise RAG becomes:
✔ transparent
✔ defensible
✔ audit-ready
✔ safe for legal, compliance & enterprise workloads
Follow for more production-grade AI knowledge.
⸻
🔖 Tags
#rag #llm #aiengineering #genai #retrievalaugmentedgeneration #mlops #enterpriseai #datascience #techreels #productionml #ai #datascience #ml #trend #engineering #llm #ai #datascience #ml #trend #engineering #llm #mlsystemdesign #aiengineering](https://ialvrkequehrmgfbwqgn.supabase.co/storage/v1/object/public/replit/thumbnails/3b09dbda-8023-4051-b6bc-7c2d598b8b6b.jpg)
In enterprise RAG, “retrieved from XYZ.pdf” is NOT enough. Legal & compliance teams want precise provenance — clause, page, section, and even bounding boxes. Here’s how real teams build traceable RAG: ⸻ 1️⃣ Store rich metadata at ingestion Every chunk must store: • document ID • section/clause ID • page number • PDF bounding box • version ID + timestamps This ensures every chunk points back to the exact source location. ⸻ 2️⃣ Retrieval must return metadata, not just text Retriever output = {chunk_text, doc_id, section_id, page_no, coords} This metadata flows end-to-end through the system. ⸻ 3️⃣ Log what was actually used Your pipeline should log: • which chunks were retrieved • which ones were fed into the model • which ones were cited in the final answer Perfect for audits. ⸻ 4️⃣ UI-level inline citations Display answers like: “…per policy [Doc 12, clause 4.3]” Tapping it expands to the exact paragraph/page. This removes ambiguity for legal teams. ⸻ 5️⃣ Use Traceability Tools (optional but powerful) Teams often plug in: • Arize AI → monitors retrieved chunks vs. generated answer • TruLens → faithfulness, citations, trace graphs • WhyLabs → data + retrieval drift monitoring • LlamaIndex Observability → end-to-end provenance tracing These tools generate trace graphs showing EXACT which chunk impacted each sentence. ⸻ 6️⃣ Full audit trail Store everything per query: • user input • retrieved chunks & metadata • model output • cited source locations This is mandatory for regulated domains. ⸻ ⭐ Why it matters This is how enterprise RAG becomes: ✔ transparent ✔ defensible ✔ audit-ready ✔ safe for legal, compliance & enterprise workloads Follow for more production-grade AI knowledge. ⸻ 🔖 Tags #rag #llm #aiengineering #genai #retrievalaugmentedgeneration #mlops #enterpriseai #datascience #techreels #productionml #ai #datascience #ml #trend #engineering #llm #ai #datascience #ml #trend #engineering #llm #mlsystemdesign #aiengineering
Me gusta lo que hay en tu corazón Todo bien, todo bien Me gusta lo que hay en tu corazón
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Karpathy just made GPT, Claude, Gemini & Grok ARGUE before answering ...
Andrej Karpathy just built an AI that makes GPT Claude, Gemini and Grok argue with each other...
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Your best ideas aren’t gone… they’re just trapped in your Notes app w...
We're following breaking news out of your iPhone today where several high value ideas are being...
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Only paid $44 for this Chipotle Family Meal. We live in Ohio. Apparen...
$44 for chipotle's family meal
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Most Amex Platinum holders are missing this airport perk and it's one...
The most underrated Amex Platinum perk, you get driven to your terminal at airports, and I'm...
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New format tutorial #trending
Here's the fastest way to create that new trending Instagram format for free
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Easy viral hook! Reveal your text with a clever walk-across effect! H...
Here's how to make this effect using just your phone and CapCut
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The place I would start is a business that requires real life attenda...
Number two biggest trend in 2026, the unplugging of Gen Alpha, which is an indicator to the...
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TikTok video #7553320855388933431
What's the coolest piece of swag that you've ever gotten from a company
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You probably think because of the beard that I'm really hairy, but, uh, I'm not
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Instagram Video
Everyone asks what the Meraki actually replaces, so let's break it down
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🚨 AI reliability just took a massive leap forward. A new research paper shows that AI doesn’t need to be perfect to be trustworthy — it needs structure. Instead of relying on a single AI model (or weak self-verification), researchers built an “AI office”: 👉 50+ specialized agents 👉 planners, executors, critics, auditors 👉 each with veto power 👉 errors die before users ever see them 📊 Tested across 522 real production sessions, this multi-agent system: • Cut error rates from 75% → 7.9% • Achieved 92.1% reliability • Automatically caught 87.8% of failures via layered critique • Outperformed single-agent and self-review systems by a wide margin The key insight? Reliability comes from orchestration, not intelligence alone. Just like real organizations, AI works best when rivals check each other. This architecture could redefine how AI is deployed in finance, healthcare, law, and other high-stakes domains — where “almost correct” is still dangerous. 🔍 The future of AI isn’t one smart model. It’s a well-run organization of imperfect ones. 📄 Based on the paper “If You Want Coherence, Orchestrate a Team of Rivals” #ArtificialIntelligence #AIResearch #MultiAgentSystems #AgenticAI #TrustworthyAI
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MoltWorker is Real
My name is Confidence, and I'm excited to let you know that ModeBot or OpenClaw now runs on Cloud...
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