PromptWizard: LLM Prompts Made Easy
PromptWizard addresses the limitations of manual prompt engineering, making the process faster, more accessible, and adaptable across different tasks. Prompt […]
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LLMs & GenAI
DSPy: A New Era In Programming Language Models
What is DSPy? Declarative Self-improving Python (DSPy) is an open-source python framework [paper, github] developed by researchers at Stanford, designed
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Large Concept Models (LCM): A Paradigm Shift in AI
Large Concept Models (LCMs) [paper] represent a significant evolution in NLP. Instead of focusing on individual words or subword tokens,
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SentencePiece: A Powerful Subword Tokenization Algorithm
SentencePiece is a language-independent subword tokenizer and detokenizer introduced by Google for neural text processing. Its open-source library is widely
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WordPiece: A Subword Segmentation Algorithm
WordPiece is a subword tokenization algorithm that breaks down words into smaller units called “wordpieces.” These wordpieces can be common
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Tree of Thought (ToT) Prompting: A Deep Dive
Tree of Thought (ToT) prompting is a novel approach to guiding large language models (LLMs) towards more complex reasoning and
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Key Challenges For LLM Deployment
Transitioning LLM models from development to production introduces a range of challenges that organizations must address to ensure successful and
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What are the Challenges of Large Language Models?
Large Language Models (LLMs) offer immense potential, but they also come with several challenges: Technical Challenges Accuracy and Factuality: Bias
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Addressing LLM Performance Degradation: A Practical Guide
Model degradation refers to the decline in performance of a deployed Large Language Model (LLM) over time. This can manifest
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Decoding Transformers: What Makes Them Special In Deep Learning
Initially proposed in the seminal paper “Attention is All You Need” by Vaswani et al. in 2017, Transformers have proven
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How to Use Chain-of-Thought (CoT) Prompting for AI
What is Chain-of-Thought Prompting? Chain-of-thought (CoT) prompting is a technique used to improve the reasoning abilities of LLMs. It involves
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How To Reduce LLM Computational Cost?
Large Language Models (LLMs) are computationally expensive to train and deploy. Here are some approaches to reduce their computational cost:
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