How to Introduce Your Employees to Artificial Intelligence
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AI
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How to Introduce Your Employees to Artificial Intelligence
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HumanEval Benchmark: Assessing AI Code Generation Capabilities
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Time to read again Loubna’s nice post from last week diving in the HumanEval benchmark
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RAG Technique: Solving Modern LLM Limitations Without Fine-Tuning
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Let’s dive in to one of the most-talked about AI techniques—retrieval augmented generation (RAG)— that developers are using as a precursor to fine-tuning. It helps solve the recent issue of modern LLMs while being not-really-that-hard to implement.
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RAG Closes Gap Between Open Source and API Providers
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And using RAG could close the gap even further between the performance of open source alongside the API providers, particularly for companies that don’t want to hand over control to a provider and are looking for a cheaper, faster, and perhaps more importantly, predictable tool.
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Retrieval Systems Fetch Parent Documents for Enhanced Context
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Usually retrieval system then fetch each chunk individually. The idea behind this is you fetch the parent document they come from (for more context)
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Open Source AI Models Accessible Without Fine-Tuning Resources
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It's a tantalizing prospect for companies that are exploring the use of open source models, but don't have the resources (personnel or financial) to fine-tune or pre-train a model. It works right out of the box without any significantly advanced technical requirements.
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Adding Memory to Frozen LLM Models Reduces Costs
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It offers the ability to endow a kind of "memory" to models that are frozen in time, as well as lower the overall cost of LLM usage. Or as @bobvanluijt told me, a way to make a stateless tool more stateful.
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Mystery AI Hype Theater 3000: Three Episodes Near 1K Views
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Mystery AI Hype Theater 3000 video leaderboard: Three episodes have over 900 views, but which will cross 1k first? #12 “It’s All Hell”
#14 “Henry Kissinger, Machines of War, and the Age of Military AI Hype”
#10 “Don’t be a Lawyer ChatGPT” https://
peertube.dair-institute.org/c/mystery_ai_h
ype_theater/videos?sort=-originallyPublishedAt&s=3
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RAG: Retrieval Augmented Generation Explained for Enterprises
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RAG stands for retrieval augmented generation, which enables companies to fetch important data relative to a query in a prompt in order to improve its results.
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RAG Emerges as Developer Priority for Model Performance Optimization
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Among all the developers, experts, and sources I talk to, no subject comes up more than RAG. Developers are increasingly working with it as a precursor to fine-tuning to squeeze more performance out of less-powerful models.