Data Privacy for LLMs

LangChain · Intermediate ·🧠 Large Language Models ·2y ago

Key Takeaways

Data privacy techniques for Large Language Models (LLMs) using DeepsenseAI and Opaque solutions

Original Description

Many teams are thinking about how to use LLMs while upholding their standards for data privacy. We talked with DeepsenseAI and Opaque about how. https://deepsense.ai/ https://opaque.co/
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1 Chat With Your Documents Using LangChain + JavaScript
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 3 LangChain "OpenAI functions" Webinar
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 4 LangSmith Launch
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 5 LangChain x Pinecone: Supercharging Llama-2 with RAG
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 8 Benchmarking Question/Answering Over CSV Data
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 9 LangChain "RAG Evaluation" Webinar
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 10 Fine-tuning in Your Voice Webinar
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 11 Tabular Data Retrieval
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 12 Building an LLM Application with Audio by AssemblyAI
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 13 Superagent Deepdive Webinar
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 14 Lessons from Deploying LLMs with LangSmith
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 15 Shortwave Assistant Deepdive Webinar
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 16 Cognitive Architectures for Language Agents
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 17 Effectively Building with LLMs in the Browser with Jacob
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Data Privacy for LLMs
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 19 "Theory of Mind" Webinar with Plastic Labs
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 20 LangChain Templates
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 21 Using Natural Language to Query Postgres with Jacob
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 22 Building a Research Assistant from Scratch
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 23 Benchmarking RAG over LangChain Docs
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 24 Skeleton-of-Thought: Building a New Template from Scratch
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 25 Benchmarking Methods for Semi-Structured RAG
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 26 LangSmith Highlights: Getting Started
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 27 LangSmith Highlights: Debugging
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 28 LangSmith Highlights: Datasets
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 29 LangSmith Highlights: Evaluation
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 30 LangSmith Highlights: Human Annotation
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 31 LangSmith Highlights: Monitoring
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 32 LangSmith Highlights: Hub
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 33 SQL Research Assistant
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 34 Getting Started with Multi-Modal LLMs
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 35 Build a Full Stack RAG App With TypeScript
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 36 Auto-Prompt Builder (with Hosted LangServe)
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 37 LangChain v0.1.0 Launch: Introduction
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 38 LangChain v0.1.0 Launch: Observability
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 39 LangChain v0.1.0 Launch: Integrations
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 40 LangChain v0.1.0 Launch: Composability
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 41 LangChain v0.1.0 Launch: Streaming
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 42 LangChain v0.1.0 Launch: Output Parsing
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 43 LangChain v0.1.0 Launch: Retrieval
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 44 LangChain v0.1.0 Launch: Agents
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 45 Build and Deploy a RAG app with Pinecone Serverless
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 46 Hosted LangServe + LangChain Templates
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 47 LangGraph: Intro
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 48 LangGraph: Agent Executor
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 49 LangGraph: Chat Agent Executor
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 50 LangGraph: Human-in-the-Loop
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 51 LangGraph: Dynamically Returning a Tool Output Directly
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 52 LangGraph: Respond in a Specific Format
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 53 LangGraph: Managing Agent Steps
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 54 LangGraph: Force-Calling a Tool
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 55 LangGraph: Multi-Agent Workflows
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 58 OpenGPTs
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 59 Open Source RAG with Nomic's New Embedding Model (and ChromaDB and Ollama)
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 60 LangGraph: Persistence
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The video discusses data privacy techniques for LLMs using DeepsenseAI and Opaque solutions, providing insights into AI safety and ethics. Viewers can learn how to implement data privacy for LLMs and apply AI safety principles. The video features experts from DeepsenseAI and Opaque, offering a comprehensive overview of LLM data privacy.

Key Takeaways
  1. Research DeepsenseAI and Opaque solutions
  2. Implement data privacy techniques for LLMs
  3. Apply AI safety principles
  4. Use LangChain for LLM development
💡 Data privacy is a crucial aspect of LLM development, and using solutions like DeepsenseAI and Opaque can help teams uphold their standards for data privacy.

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