Claude-ception: Teaching Claude3 to prompt engineer itself Claude3 Opus is excellent at prompt engineering. @alexalbert__ recently laid out a nice workflow: write an prompt, run it on test cases, grade responses, let Claude3 Opus use grades to improve prompt, & repeat.
@langchain
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NVIDIA NIM Integration for GPU-Optimized LLM Inference in RAG
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Our Integration With NVIDIA NIM for GPU-optimized LLM Inference in RAG As enterprises turn their attention from prototyping LLM applications to productionizing them, they often want to turn from third-party model services to self-hosted solutions. We’ve seen many folks
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Building Code Agents: Programming AI Software Engineers
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Webinar Wednesday 3/20: Building Code Agents! Sign up here https://
crowdcast.io/c/codeagents Agents that write and run code are powerful, as Cognition Labs showed with their recent release of Devin, the "AI SWE". But they are complex to program, hard to deploy, and even harder to -
RAG Query Routing: Handling Multiple Datastores
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RAG From Scratch: Routing Our RAG From Scratch video series walks through impt RAG concepts in short / focused videos w/ code. This is the 10th video in our series and focuses on query routing. Problem: We often have multiple possible datastores (e.g., different vectorDBs,
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Voyage AI Embedding Integration Package for LangChain
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@Voyage_AI_ Embedding Integration Package Use the same custom embeddings that power Chat LangChain via the new langchain-voyageai package! Recommended by @AnthropicAI as their preferred embedding provider, Voyage AI builds custom embedding models for your company or
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Graph Retrieval Queries in LangChain for RAG Implementation
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Implementing RAG: How to Write a Graph Retrieval Query in LangChain Great guide from our friends at @neo4j on how to combine LLMs and graph databases. Simplifies it really nicely! Blog: https://
neo4j.com/developer-blog
/rag-graph-retrieval-query-langchain/
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Advanced RAG Series: Retrieval Algorithms Explained
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Advanced RAG Series: Retrieval This is the fourth post in a series focused on retrieval by @divyanshu_van It dives into different retrieval algorithms that can be part of your RAG system Check it out: https://
div.beehiiv.com/p/advanced-rag
-series-retrieval
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Text-to-PowerPoint Generation with LangGraph.js and Next.js
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⚡ Text-to-PowerPoint with LangGraph.js ⚡
— LangChain (@LangChain) 17 mars 2024
You’ve seen text generation – how about PowerPoint generation?@The_GreatBonnie wrote up a neat guide showing how to use LangGraph.js, @tavilyai, and @CopilotKit to build a Next.js app that can make a presentation about an arbitrary… pic.twitter.com/NSior68ANcText-to-PowerPoint with LangGraph.js You’ve seen text generation – how about PowerPoint generation? @The_GreatBonnie wrote up a neat guide showing how to use LangGraph.js, @tavilyai
, and @CopilotKit to build a Next.js app that can make a presentation about an arbitrary -
Can Long Context LLMs Replace RAG Systems?
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Is RAG Really Dead? Testing the retrieval limits of long context LLMs RAG systems often retrieve multiple docs relevant to a user input and reason over them to return an answer. How well can long context LLMs do this across hundreds or thousands of pages of input tokens?
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Build Perplexity-Inspired Answer Engine with Groq and Mixtral
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Build a Perplexity-Inspired Answer Engine Using Groq, Mixtral, Langchain, Brave & OpenAI in 10 Min Fun tutorial by our friends at @devdigest walking through how to build an answer engine over the internet Code: https://
github.com/developersdige
st/llm-answer-engine
… YouTube: https://
youtube.com/watch?v=43ZCeB
TcsS8&t=96s
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