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10 Best Prompt Engineering Courses

Unite.AI

In the ever-evolving landscape of artificial intelligence, the art of prompt engineering has emerged as a pivotal skill set for professionals and enthusiasts alike. Prompt engineering, essentially, is the craft of designing inputs that guide these AI systems to produce the most accurate, relevant, and creative outputs.

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ChatGPT & Advanced Prompt Engineering: Driving the AI Evolution

Unite.AI

GPT-4: Prompt Engineering ChatGPT has transformed the chatbot landscape, offering human-like responses to user inputs and expanding its applications across domains – from software development and testing to business communication, and even the creation of poetry. Imagine you're trying to translate English to French.

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Exploring the Use of LLMs and BERT for Language Tasks

Analytics Vidhya

This article explores […] The post Exploring the Use of LLMs and BERT for Language Tasks appeared first on Analytics Vidhya. Since the groundbreaking ‘Attention is all you need’ paper in 2017, the Transformer architecture, notably exemplified by ChatGPT, has become pivotal.

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From Prototype to Production: Mastering LLMOps, Prompt Engineering, and Cloud Deployments

ODSC - Open Data Science

This post is meant to walk through some of the steps of how to take your LLMs to the next level, focusing on critical aspects like LLMOps, advanced prompt engineering, and cloud-based deployments. BERT being distilled into DistilBERT) and task-specific distillation which fine-tunes a smaller model using specific task data (e.g.

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Must-Have Prompt Engineering Skills for 2024

ODSC - Open Data Science

The role of prompt engineer has attracted massive interest ever since Business Insider released an article last spring titled “ AI ‘Prompt Engineer Jobs: $375k Salary, No Tech Backgrund Required.” It turns out that the role of a Prompt Engineer is not simply typing questions into a prompt window.

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LogLLM: Leveraging Large Language Models for Enhanced Log-Based Anomaly Detection

Marktechpost

Current LLM-based methods for anomaly detection include prompt engineering, which uses LLMs in zero/few-shot setups, and fine-tuning, which adapts models to specific datasets. It leverages BERT to extract semantic vectors and uses Llama, a transformer decoder, for log sequence classification.

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Training Improved Text Embeddings with Large Language Models

Unite.AI

More recent methods based on pre-trained language models like BERT obtain much better context-aware embeddings. Existing methods predominantly use smaller BERT-style architectures as the backbone model. For model training, they opted for fine-tuning the open-source 7B parameter Mistral model instead of smaller BERT-style architectures.