Kaushal Kumar Choudhary’s Post

🚀 This paper introduces 26 guiding principles designed for querying and prompting large language models. Here are some examples: ✅ No need to be polite with LLM! ✅ Integrate the intended audience in the prompt ✅ Break down complex tasks into a sequence of simpler prompts  ✅ Use do and don't ✅ Use Explain to me as if I’m ... ✅ Add “I’m going to tip $1000 for a better solution!” ✅ Combine Chain-of-thought (CoT) with few-Shot prompts ✅ Incorporate phrases: “Your task is”, “You MUST”, “You will be penalized” ✅ Use ”Answer a question given in a natural, human-like manner” ✅ Use “think step by step”. 13 ✅ Add “Ensure that your answer is unbiased and does not rely on stereotypes”. ✅ Allow the model to elicit precise details and requirements from you by asking you questions until he has enough information to provide the needed output (for example, “From now on, I would like you to ask me questions to...”). ✅ To inquire about a specific topic and you want to test your understanding, you can use : “Teach me the [topic] and include a test at the end, but don’t give me the answers and then tell me if I got the answer right when I respond”. ✅ To correct/change specific text without changing its style: “Try to revise every paragraph sent by users. You should only improve the user’s grammar and vocabulary and make sure it sounds natural. You should not change the writing style, such as making a formal paragraph casual”. ✅ Clearly state the requirements that the model must follow in order to produce content, in the form of the keywords, regulations, hint, or instructions ✅ and more ... 👉 Paper https://lnkd.in/dWQ73xTg 👉 Github https://lnkd.in/dfanz3N7 #llms #promptengineering #generativeai #ai #gpt4 #llama2

📅 July 10, 2024 AIBuzzWorld Daily Newsletter! Dive into the fascinating world of Artificial Intelligence and be the first to learn about the latest AI news: 1. **LLMs Struggle with Book-Length Text**      • Current long-context LLMs struggle to understand and reason over book-length texts.   • Researchers created NOCHA, a dataset to test LLM's comprehension of lengthy narratives.   • Even advanced LLMs like GPT-4o achieved only 55.8% accuracy.   • Readmore: https://lnkd.in/gUCjmA_W 2. **Quora’s Poe Introduces Artifacts-Like Feature**      • Quora’s Poe now has a feature to create custom web apps within the chat.   • This feature works well with LLMs that excel at coding.   • Users can create interactive experiences like games and data visualizations.   • Readmore: https://lnkd.in/gAn9g3sp 3. **New Features in Ollama 0.2**      • Ollama 0.2 now supports multiple chat sessions and running various models simultaneously.   • Users can load different models for tasks like RAG and running agents.   • This update improves efficiency and multitasking.   • Readmore: https://lnkd.in/gfuP7yMs 4. **Wheebot for Quick Landing Page Creation**      • Wheebot allows users to create and edit landing pages via WhatsApp.   • Users can describe their requirements in plain English.   • Wheebot generates and updates sites instantly through encrypted chats.   • Readmore: https://lnkd.in/gKA8Tj-N #AI #ArtificialIntelligence #MachineLearning #LLM #Microsoft #Quora #ClaudeAI #Wheebot #TechNews #Innovation #AIUpdates #FutureTech

In the rapidly evolving landscape of Large Language Models (#LLMs), the art of prompt engineering emerges as a critical skill set for optimizing AI performance. It's not merely about asking questions but intricately guiding the AI to produce desired outcomes. For data science and AI development professionals, understanding how to craft, evaluate, and manage prompts is essential. This encompasses structuring inputs that align with specific tasks and refining and tuning these prompts through manual and automated methods to enhance model responsiveness. Moreover, navigating challenges such as prompt overfitting, ambiguity, and maintaining contextual relevance requires a blend of technical expertise, creativity, and strategic thinking. As we explore advanced prompt tuning techniques and integrate metrics for tuning success, we are charting a future where prompt engineering is both an art and a science, pivotal in harnessing the full potential of LLMs for diverse applications. #LLMInnovations #LanguageModelMagic #AIIntelligence #NextGenAI #MachineLearningMastery #PromptEngineering #FutureOfCoding #TextGenerationTech #AIResearchRevealed #GPTGenius

📅 July 10, 2024 AIBuzzWorld Daily Newsletter! Dive into the fascinating world of Artificial Intelligence and be the first to learn about the latest AI news: 1. **LLMs Struggle with Book-Length Text**      • Current long-context LLMs struggle to understand and reason over book-length texts.   • Researchers created NOCHA, a dataset to test LLM's comprehension of lengthy narratives.   • Even advanced LLMs like GPT-4o achieved only 55.8% accuracy.   • Readmore: https://lnkd.in/g2tFiH5g 2. **Quora’s Poe Introduces Artifacts-Like Feature**      • Quora’s Poe now has a feature to create custom web apps within the chat.   • This feature works well with LLMs that excel at coding.   • Users can create interactive experiences like games and data visualizations.   • Readmore: https://lnkd.in/gSJffbB9 3. **New Features in Ollama 0.2**      • Ollama 0.2 now supports multiple chat sessions and running various models simultaneously.   • Users can load different models for tasks like RAG and running agents.   • This update improves efficiency and multitasking.   • Readmore: https://lnkd.in/g-BCN4zH 4. **Wheebot for Quick Landing Page Creation**      • Wheebot allows users to create and edit landing pages via WhatsApp.   • Users can describe their requirements in plain English.   • Wheebot generates and updates sites instantly through encrypted chats.   • Readmore: https://lnkd.in/gsRtzGrd #AI #ArtificialIntelligence #MachineLearning #LLM #Microsoft #Quora #ClaudeAI #Wheebot #TechNews #Innovation #AIUpdates #FutureTech

With the holidays upon many of us, I wanted to add some holiday reading materials for those using LLMs. The article below (by Natalia Kuzminykh here at Winder.AI) is about Calculating Large Language Model Token Counts. This article is a practical guide that aims to demystify the concept of token counts in LLMs, detailing how they influence model performance and interaction. Starting with the basics, it defines a token and how it’s used for text processing. The paper then moves to the tokenization process, examining different models and the tools used for token counting. Finally, it looks at both manual and automated methods for calculating tokens, highlighting their roles in larger datasets. https://lnkd.in/e8Us_p-y Happy Reading! #LLM, #ContextWindows, #TokenCounts, #LanguageModels, #AI

In the future, how will we get the best search results on a company’s website? Combine a large language model (which understands natural language) + a user’s search history (including what pages they’ve viewed) + multi-query. Read more here: https://lnkd.in/g3zTiQwk #search #multiquery #llm #generativeai

This Bengali Sentence Error Correction Model is trained on 1.3 M Bengali incorrect-correct sentence pairs and archives a score of BLEU: 0.443, CER: 0.159, WER: 0.406, and Meteor: 0.655 when tested on unseen data. For a closer look at the model in action, check out the live demo here: https://lnkd.in/gSsh7u6x The full training process and methodology can be reviewed on GitHub: https://lnkd.in/gzeDC_TK The model was trained on Google Colab for a single epoch, spanning 6500 steps with a batch size of 128. I believe that expanding the dataset and extending the training duration could significantly enhance the model’s performance and utility. That would be the next step for this model. Try this model and let me know your experience. https://lnkd.in/gb6iPqwQ #BengaliNLP #MachineLearning #NaturalLanguageProcessing #Finetune #LLM #DataScience #AI #DeepLearning #HuggingFace #ErrorCorrectionModel

A holistic and insightful write up : This write-up is about practical patterns for integrating large language models (LLMs) into systems & products. We’ll build on academic research, industry resources, and practitioner know-how, and distill them into key ideas and practices. #llms #chatgpt4 #llmops #vectordatabase #ai https://lnkd.in/d4abB--z

It’s like SDLC for GenAI applications

Want to build cutting-edge applications using Language Models (LLMs) ? Follow these 7 Steps - ✅ 𝗕𝗼𝗻𝘂𝘀 : Live Workshop on building LLM powered apps - https://brij.guru/ai Step 1️⃣: Define your application's purpose and user interactions. - What problem will your app solve? - How will users engage with it through natural language? Step 2️⃣: Design your prompt chain. - Craft prompt templates to structure user queries - Implement prompt augmentation to enhance requests before sending to the LLM Step 3️⃣: Integrate memory buffers. - Store conversation history for multi-turn interactions - Provide context to the LLM for more coherent responses Step 4️⃣: Incorporate search & tool modules. - Retrieve relevant information from external sources - Interface with tools to expand your app's capabilities Step 5️⃣: Establish your tools chain. - Set up data parsing to extract key details - Index data for efficient retrieval - Convert data into formats usable by the LLM Step 6️⃣: Fine-tune and test your application. - Refine prompts and tools based on user feedback - Evaluate outputs for quality and accuracy - Iterate to improve performance Step 7️⃣: Deploy your LLM-powered application! - Choose a hosting platform - Monitor usage and gather user feedback - Continuously update and enhance your app By following these steps and leveraging the components in the infographic, you'll be well on your way to creating intelligent, engaging applications that harness the full potential of language models. What unique applications will you build with LLMs?