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2 contributions to AI Automation First Client
๐Ÿ”ฎ๐Ÿš€๐Ÿ”œ๐Ÿ’ก For the future ๐Ÿ”ฎ๐Ÿš€๐Ÿ”œ๐Ÿ’ก
Today, following our discussion on LLM Orchestration, we are specifically introducing the RAG Pipeline. For satisfactory processing, the RAG (Retrieval-Augmented Generation) pipeline is a key element in building AI systems that provide successful and context-aware answers. This pipeline combines the powerful capabilities of language models with document-related search functions, ensuring that AI responses are based on user data rather than relying solely on prior knowledge. The following is a subsequent diagram illustrating the RAG pipeline. It shows how data is retrieved, processed, and used to generate high-quality, powerful answers. This approach not only enables excellent answers but also allows for the integration of features through added content. We welcome any questions related to software, including issues encountered during the learning and development process. Our goal is ```for the future```.
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๐Ÿ”ฎ๐Ÿš€๐Ÿ”œ๐Ÿ’ก For the future ๐Ÿ”ฎ๐Ÿš€๐Ÿ”œ๐Ÿ’ก
How I Handle "Let Me Think About It" ๐Ÿ”ฅ
The four words that used to kill my deals. Now they lead to closes. THE OLD RESPONSE: Them: "Let me think about it." Me: "Sure, take your time." Result: Never heard from them again. THE NEW RESPONSE: Them: "Let me think about it." Me: "Of course. What specifically do you want to think through?" This question is magic. THE RESPONSES I GET: "I need to check with my partner." โ†’ "Makes sense. Want me to join a call with both of you?" "The budget is tight right now." โ†’ "Understood. What if we started with a smaller scope at $X?" "I'm not sure if it'll work for our edge cases." โ†’ "Let me show you exactly those edge cases on a second demo." "I want to compare with other options." โ†’ "That's smart. What would you need to see from me to be the clear choice?" THE PSYCHOLOGY: "Let me think about it" is rarely about thinking. It is about unspoken concerns. Your job: Uncover the real objection. THE FRAMEWORK: 1. Acknowledge: "Of course." 2. Ask: "What specifically?" 3. Address: Solve the real concern. 4. Re-ask: "Does that address it? Want to move forward?" THE EXAMPLES: REAL CONCERN: Price too high Solution: Smaller scope or payment plan REAL CONCERN: Not sure about results Solution: Offer money-back guarantee for first 30 days REAL CONCERN: Need buy-in from others Solution: Offer to present to the team REAL CONCERN: Bad timing Solution: Schedule follow-up for specific date THE CLOSE RATE CHANGE: Before asking follow-up question: 12% of "think about it" closed After asking follow-up question: 47% of "think about it" closed What unspoken objection might your last prospect have had?
2 likes โ€ข 2d
Hey bro I build AI systems using LLM orchestration,RAG pipeline, muti modal models and muti agent architecture combined full stack development and automation integration are you looking for dev? Hope we get a chance to work together
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Yuki Nakamura
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3points to level up
@misa-dana-2493
Full stack and AI developer

Active 3m ago
Joined Mar 17, 2026