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About Retrieval Augmented Generation in Production
In today’s rapidly changing AI technology environment, software engineers often struggle to build real-world applications with large language models (LLM). The benefits of incorporating open source LLMs into existing workflows is often offset by the need to create custom components. That’s where Haystack comes in. This open source framework is a collection of the most useful tools, integrations, and infrastructure building blocks to help you design and build scalable, API-driven LLM backends. With Haystack, it’s easy to build extractive or generative QA, Google-like semantic search to query large-scale textual data, or a reliable and secure ChatGPT-like experience on top of technical documentation. This guide serves as a collection of useful retrieval augmented generation (RAG) mental models and offers ML engineers, AI engineers, and backend engineers a practical blueprint for the LLM software development lifecycle. An emerging paradigm is the leveraging of Generative AI to unlock data-centric insights for customers across various industries using large language models (LLMs) such as the OpenAI GPT models, Anthropic’s Claude models, Google Gemini, Meta’s Llama models, Mistral, etc. However, an engine alone cannot propel a vehicle. State-of-the-art LLMs like GPT-4 excel at language-based tasks due to their a priori knowledge, acquired through training on a vast representative corpus of documents (including websites, books, etc.) and tasks involving these documents.
Detailed Information
| Author: | Skanda Vivek |
|---|---|
| Language: | English |
| File Size: | 1.8 |
| Format: | |
| Price: | FREE |
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