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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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How To Make a Career in GenAI In 2024

Towards AI

Later, Python gained momentum and surpassed all programming languages, including Java, in popularity around 2018–19. Major language models like GPT-3 and BERT often come with Python APIs, making it easy to integrate them into various applications.

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5000x Generative AI: Intro, Overview, Models, Prompts, Technology, Tools, Comparisons & the Best…

Mlearning.ai

350x: Application Areas , Companies, Startups 3,000+: Prompts , Prompt Engineering, & Prompt Lists 250+: Hardware, Frameworks , Approaches, Tools, & Data 300+: Achievements, Impacts on Society , AI Regulation, & Outlook 20x: What is Generative AI?

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Accelerate your learning towards AWS Certification exams with automated quiz generation using Amazon SageMaker foundations models

AWS Machine Learning Blog

In 2018, BERT-large made its debut with its 340 million parameters and innovative transformer architecture, setting the benchmark for performance on NLP tasks. For text tasks such as sentence classification, text classification, and question answering, you can use models such as BERT, RoBERTa, and DistilBERT.

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Large language models: their history, capabilities and limitations

Snorkel AI

BERT, the first breakout large language model In 2019, a team of researchers at Goole introduced BERT (which stands for bidirectional encoder representations from transformers). By making BERT bidirectional, it allowed the inputs and outputs to take each others’ context into account. BERT), or consist of both (e.g.,

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Large language models: their history, capabilities and limitations

Snorkel AI

BERT, the first breakout large language model In 2019, a team of researchers at Goole introduced BERT (which stands for bidirectional encoder representations from transformers). By making BERT bidirectional, it allowed the inputs and outputs to take each others’ context into account. BERT), or consist of both (e.g.,

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Against LLM maximalism

Explosion

For instance, you can design a number of different prompts, and run a tournament between them, by answering a series of A/B evaluation questions where you pick which of two outputs is better without knowing which prompt produced them. Language model pretraining has proven to be a very strong general answer to this requirement.

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