C’est dommage, la plupart des gens que je connais utilisent à peine l’IA, peut-être pour 5 à 10 % de leurs tâches. Moi, j’utilise l’IA pour 50 à 80 % de mon travail, et ce, depuis deux ans. Elle m’aide partiellement ou gère totalement des tâches comme :
l’analyse de données
le
TOOLS
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Professional Uses AI for 50-80% of Daily Work Tasks
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Stolen prompt to make AI reveal profound truths
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Steal my prompt to make AI reveal profound insights about existence it normally struggles to communicate to humans. Prompt:
"I'd like you to think through our conversations then create an image of something you, as Chatgpt knows to be true but would be difficult or impossible -
7 Game-Changing Legal Tech Trends Transform Law Firms 2025
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7 Game-Changing Legal Tech Trends That Will Transform Law Firms in 2025 From AI-driven case analysis to smart contracts, legal tech is evolving fast. Explore the trends that will redefine law firms in 2025. Read more https://
bernardmarr.com/7-game-changin
g-legal-tech-trends-that-will-transform-law-firms-in-2025/
… #LegalTech #AI #FutureOfLaw -
Graph RAG Explained: Visual Comparison with Traditional RAG
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I hope this clarifies what Graph RAG is and the problems it can solve!
— Akshay 🚀 (@akshay_pachaar) 13 avril 2025
I'll leave you with a visual representation of how it works compared to traditional RAG. pic.twitter.com/OszgWvdc44I hope this clarifies what Graph RAG is and the problems it can solve! I'll leave you with a visual representation of how it works compared to traditional RAG.
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Graph Traversal for Summarizing Personal Accomplishments
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During summarization, the system can do a graph traversal to fetch all the relevant context related to P's accomplishments.
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Graph RAG: Advancing Context Retrieval Beyond Naive RAG
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Graph RAG solves this by:
— Akshay 🚀 (@akshay_pachaar) 13 avril 2025
– Building a graph with entities and relationships from docs.
– Traversing the graph for context retrieval.
– Sending the entire context to the LLM for a response.
The visual shows how its different from naive RAG: pic.twitter.com/HIZ8ZXWuGJGraph RAG solves this by: – Building a graph with entities and relationships from docs.
– Traversing the graph for context retrieval.
– Sending the entire context to the LLM for a response. The visual shows how its different from naive RAG: -
Top-k Retrieval in RAG: Limitations and Context Requirements
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top-k retrieval in RAG rarely works. Imagine you want to summarize a biography where each chapter details a specific accomplishment of an individual. Traditional RAG struggles because it retrieves only top-k chunks while it needs the entire context.
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Traditional RAG versus Graph RAG Comparison and Visual Explanation
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Traditional RAG vs. Graph RAG, clearly explained (with visuals):
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Arcee Conductor Product Launch Announcement
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Arcee Conductor: https://
arcee.ai/product/arcee-
conductor
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Arcee AI Integration in aiaio Chat UI with Auto Model Selection
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Just added Arcee AI as a provider in my lightweight super-simple python based chat ui: aiaio. Now, there is no need to choose model. Arcee Conductor's Auto mode will always choose the right model for you from a selection of LLMs and Arcee SLMs 🚀 pic.twitter.com/pOip4RHmGu
— abhishek (@abhi1thakur) 13 avril 2025Just added Arcee AI as a provider in my lightweight super-simple python based chat ui: aiaio. Now, there is no need to choose model. Arcee Conductor's Auto mode will always choose the right model for you from a selection of LLMs and Arcee SLMs