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www.whiteboxml.com | ||
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www.jamesserra.com
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| | | | | [AI summary] This technical blog post explains the concepts behind LLMs and Generative AI, details their architecture, and guides developers on using Azure and Microsoft Copilot Studio to apply these models to enterprise data via RAG techniques. | |
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www.index.dev
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| | | | | Learn all about Large Language Models (LLMs) in our comprehensive guide. Understand their capabilities, applications, and impact on various industries. | |
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isthisit.nz
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| | | | | August 2024 Update: Now a solved problem. Use Structured Outputs. Large language models (LLMs) return unstructured output. When we prompt them they respond with one large string. This is fine for applications such as ChatGPT, but in others where we want the LLM to return structured data such as lists or key value pairs, a parseable response is needed. In Building A ChatGPT-enhanced Python REPL I used a technique to prompt the LLM to return output in a text format I could parse. | |
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www.onehouse.ai
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| | | Onehouse can host your vector embeddings, at low cost and with great performance. You can then move only needed vectors to a vector database for vector search use cases. | ||
