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GenAI – How Will You Decide The Chunk Size For Your LLM ?
GenAI – How Will You Decide The Chunk Size For Your LLM ? Scenario: You’re asked whether to use 200-token chunks or 800-token chunks in your RAG pipeline. How would you decide? Answer:
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GenAI – How Do You Decide Whether To Finetune Or Do Prompt Engineering ?
GenAI – How Do You Decide Whether To Finetune Or Do Prompt Engineering ? Scenario: Your team is debating whether to fine-tune an LLM or just use prompt engineering. What’s your take? Answer:
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GenAI – User Wants Customizable Summaries How Do You Implement It ?
GenAI – User Wants Customizable Summaries How Do You Implement It ? Scenario: Your users want to generate summaries of articles — sometimes brief, sometimes detailed. How do you design this feature? Answer:
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GenAI – How To Make LLM Response More Explainable For Non-Technical Users ?
GenAI – How To Make LLM Response More Explainable For Non-Technical Users ? Scenario: You’re asked to make your LLM responses more explainable for non-technical users. What’s your strategy? Answer: Approach – 1: Approach – 2: Approach – 3:
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GenAI – RAG Is Retrieving Irrelevant Documents What Would You Do ?
GenAI – RAG Is Retrieving Irrelevant Documents What Would You Do ? Scenario: You’re using GPT-4 to summarize legal documents, but the summaries are factually incorrect. What’s your fix? Answer:
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GenAI – What Will You Do If Your LLM Model Hallucinate ?
GenAI – What Will You Do If Your LLM Model Hallucinate ? Scenario: Your LLM app sometimes generates factually incorrect or hallucinated information even when retrieval seems accurate. How do you handle this? Answer:
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GenAI – How Will You Scale RAG For Millions Of Docs ?
GenAI – How Will You Scale RAG For Millions Of Docs ? Scenario: Your product needs to scale RAG to millions of documents. What architecture do you suggest? Answer:
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GenAI – How Will You Handle Secure Data In GenAI Pipeline ?
GenAI – How Will You Handle Secure Data In GenAI Pipeline ? Table Of Content: Identify & Classify Sensitive Data. Secure Data At All Stages. Data Minimization & Preprocessing. Use Secure Model Hosting & Deployment. Access Control & Monitoring. Model Level Security Practices. Compliance & Legal Safeguards. (1) Identify & Classify Sensitive Data. (2) Secure Data At All Stages (3) Data Minimization & Preprocessing (4) Use Secure Model Hosting & Deployment (5) Access Control & Monitoring (6) Model Level Security Practices. (7) Compliance & Legal Safeguards (8) Summary Checklist (9) Security Level On ” Data At Rest “. (10) Security
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GenAI – What Will You Do If Inaccurate AI Output in Domain-Specific Use Case ?
GenAI – How Do You Solve Prompt Injection Issue In LLM ? Scenario: A user finds a way to bypass system behavior with a prompt injection like “Ignore previous instructions…”. How do you mitigate this? Answer:
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GenAI – How Will You Implement Multi Language RAG Support ?
GenAI – How Will You Implement Multi Language RAG Support ? Scenario: You’re asked to support multilingual queries in your RAG pipeline. How do you implement this? Answer:
