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Our third session in getting a solid understanding of AI in general and specifically to marketers. First sessions had NPS over 60%.

Are you tired of spending hours sifting through data to find meaningful insights? What if you could ask questions in plain English and have an AI-powered analyst provide accurate answers in seconds? With Large Language Models (LLMs), this is now a reality. In MMA Global's Decoding AI for Marketers training, we'll show you how to leverage LLMs for data analysis using natural language queries. Here are some key takeaways and steps to get you started: 1. 𝗣𝗿𝗲𝗽𝗮𝗿𝗲 𝘆𝗼𝘂𝗿 𝗱𝗮𝘁𝗮: Ensure your data is clean, structured, and in a format that can be easily processed by the LLM (e.g., Excel, CSV, or JSON). 2. 𝗖𝗵𝗼𝗼𝘀𝗲 𝗮𝗻 𝗟𝗟𝗠: Select an LLM that supports data analysis, such as GPT-4 or Claude. 3. 𝗔𝘀𝗸 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 𝗶𝗻 𝗻𝗮𝘁𝘂𝗿𝗮𝗹 𝗹𝗮𝗻𝗴𝘂𝗮𝗴𝗲: Input your questions using plain English, just as you would when talking to a human analyst. For example, "What is the average revenue per customer in Q3?" 4. 𝗥𝗲𝗳𝗶𝗻𝗲 𝘆𝗼𝘂𝗿 𝗾𝘂𝗲𝗿𝗶𝗲𝘀: If the initial results aren't quite what you're looking for, try rephrasing your question or providing more context, such as describing how your data is structured, to guide the LLM toward the desired insight. 5. 𝗩𝗮𝗹𝗶𝗱𝗮𝘁𝗲 𝘁𝗵𝗲 𝗿𝗲𝘀𝘂𝗹𝘁𝘀: Always cross-check the insights provided by the LLM with your own analysis to ensure accuracy and reliability. 𝗕𝘆 𝗹𝗲𝘃𝗲𝗿𝗮𝗴𝗶𝗻𝗴 𝗟𝗟𝗠𝘀 𝗳𝗼𝗿 𝗱𝗮𝘁𝗮 𝗮𝗻𝗮𝗹𝘆𝘀𝗶𝘀, 𝘆𝗼𝘂 𝗰𝗮𝗻 𝘀𝗮𝘃𝗲 𝗰𝗼𝘂𝗻𝘁𝗹𝗲𝘀𝘀 𝗵𝗼𝘂𝗿𝘀 𝗮𝗻𝗱 𝘂𝗻𝗰𝗼𝘃𝗲𝗿 𝗵𝗶𝗱𝗱𝗲𝗻 𝗼𝗽𝗽𝗼𝗿𝘁𝘂𝗻𝗶𝘁𝗶𝗲𝘀 𝘄𝗶𝘁𝗵𝗶𝗻 𝘆𝗼𝘂𝗿 𝗱𝗮𝘁𝗮. 𝗕𝘂𝘁 𝘁𝗵𝗶𝘀 𝗶𝘀 𝗷𝘂𝘀𝘁 𝘁𝗵𝗲 𝘁𝗶𝗽 𝗼𝗳 𝘁𝗵𝗲 𝗶𝗰𝗲𝗯𝗲𝗿𝗴 𝘄𝗵𝗲𝗻 𝗶𝘁 𝗰𝗼𝗺𝗲𝘀 𝘁𝗼 𝗔𝗜'𝘀 𝗽𝗼𝘁𝗲𝗻𝘁𝗶𝗮𝗹 𝗳𝗼𝗿 𝗺𝗮𝗿𝗸𝗲𝘁𝗲𝗿𝘀. That's why we're excited to offer our comprehensive 5-hour Decoding AI for Marketers training, completely free for all employees of MMA Global members. In this training, you'll learn from expert instructors and gain hands-on experience with a wide range of AI applications, including: • Image generation AI like DALL-E • Crafting effective prompts and personas • Applying computer vision and multimodal AI • Autonomous AI agents and their potential • Evaluating AI risk and responsible AI deployment Don't miss this opportunity to upskill and stay ahead of the curve in the rapidly evolving world of AI. All participants who complete the course will receive a certification issued by MMA Global and a copy of Rex Briggs' and Caleb Briggs' upcoming book from The MIT Press , 𝘛𝘩𝘦 𝘈𝘐 𝘊𝘰𝘯𝘶𝘯𝘥𝘳𝘶𝘮. Don't miss this opportunity to unlock the full potential of AI for your marketing efforts. There is a reason it earned a 73 NPS! ➡️ Register now for the Decoding AI for Marketers training and take your knowledge management to the next level. https://lnkd.in/epEqerZ5 #aimarketing #marketing #genai #ai #training Rohit Dadwal | MMA Global APAC | Shanti Tolani | MMA Global

After understanding that 𝐑𝐞𝐭𝐫𝐢𝐞𝐯𝐚𝐥-𝐀𝐮𝐠𝐦𝐞𝐧𝐭𝐞𝐝 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧 (𝐑𝐀𝐆) models are here to stay, even with the advent of models capable of handling large content windows, I decided to deep dive into each component that makes RAGs so effective. The first piece of the puzzle? Text splitting or chunking. 🔑 𝐖𝐡𝐲 𝐂𝐡𝐮𝐧𝐤𝐢𝐧𝐠 𝐢𝐬 𝐈𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐭 Chunking is essential because it breaks down large texts into manageable pieces, enabling models to process and understand information more efficiently. This not only improves computational efficiency but also significantly enhances the accuracy and relevance of the model's outputs. 🛠 𝐓𝐲𝐩𝐞𝐬 𝐨𝐟 𝐂𝐡𝐮𝐧𝐤𝐢𝐧𝐠 𝐓𝐞𝐜𝐡𝐧𝐢𝐪𝐮𝐞𝐬 There are several chunking strategies, each with its specific use cases: • 𝐒𝐞𝐧𝐭𝐞𝐧𝐜𝐞 𝐒𝐩𝐥𝐢𝐭𝐭𝐢𝐧𝐠: Ideal for sentiment analysis or any task requiring understanding at the sentence level • 𝐏𝐚𝐫𝐚𝐠𝐫𝐚𝐩𝐡 𝐒𝐩𝐥𝐢𝐭𝐭𝐢𝐧𝐠: Best suited for summarizing content or analyzing texts where each paragraph holds a distinct idea • 𝐅𝐢𝐱𝐞𝐝-𝐋𝐞𝐧𝐠𝐭𝐡 𝐒𝐩𝐥𝐢𝐭𝐭𝐢𝐧𝐠: Useful for document classification tasks, especially when dealing with large documents • 𝐓𝐨𝐩𝐢𝐜-𝐛𝐚𝐬𝐞𝐝 𝐒𝐩𝐥𝐢𝐭𝐭𝐢𝐧𝐠: Perfect for content recommendation systems where the focus is on identifying distinct topics within the text • 𝐒𝐞𝐦𝐚𝐧𝐭𝐢𝐜 𝐒𝐩𝐥𝐢𝐭𝐭𝐢𝐧𝐠: Crucial for complex NLP tasks like question answering, where understanding the semantic relationships within the text is key 🔗 𝐔𝐬𝐢𝐧𝐠 𝐋𝐚𝐧𝐠𝐂𝐡𝐚𝐢𝐧 𝐌𝐞𝐭𝐡𝐨𝐝𝐬: • 𝐂𝐡𝐚𝐫𝐚𝐜𝐭𝐞𝐫𝐓𝐞𝐱𝐭𝐒𝐩𝐥𝐢𝐭𝐭𝐞𝐫: Splits text into chunks based on a fixed number of characters • 𝐍𝐋𝐓𝐊𝐓𝐞𝐱𝐭𝐒𝐩𝐥𝐢𝐭𝐭𝐞𝐫: Utilizes NLTK's capabilities for tokenization and sentence segmentation to split text into chunks • 𝐒𝐩𝐚𝐜𝐲𝐓𝐞𝐱𝐭𝐒𝐩𝐥𝐢𝐭𝐭𝐞𝐫: Leverages spaCy's linguistic annotations to segment text into chunks based on sentences, noun phrases, or other linguistic structures 🔄 𝐂𝐡𝐮𝐧𝐤 𝐎𝐯𝐞𝐫𝐥𝐚𝐩: 𝐀𝐥𝐩𝐡𝐚 𝐨𝐯𝐞𝐫 𝐍𝐚𝐢𝐯𝐞 𝐂𝐡𝐮𝐧𝐤𝐢𝐧𝐠 It is a technique where chunks share text with adjacent ones, ensuring smooth transitions and context preservation. This facilitates continuous information flow, which is crucial for nuanced text comprehension and coherent response generation. It mitigates the risk of losing vital contextual clues. 📐 𝐈𝐝𝐞𝐚𝐥 𝐂𝐡𝐮𝐧𝐤 𝐒𝐢𝐳𝐞 The ideal chunk size varies depending on the specific task and the model's capacity. For instance, fixed-length chunks might range from a few hundred to a few thousand words, based on computational constraints and the need for context. There's no one-size-fits-all when it comes to designing chunks. Depending on the use case, one must thoughtfully design the chunking strategy to optimize both performance and efficiency. #ai #artificialintelligence #retrievalaugmentedgeneration #rag #naturallanguageprocessing #naturallanguagegeneration #nlp #technology #langchain #week9 #weekendlearning

A Beginner's Guide to Fine-tuning LLMs As an AI practitioner, I've had the opportunity to fine-tune several Large Language Models (LLMs) using various tools and platforms. In this beginner-friendly tutorial, I'll share my experience using LLM FineTuner (LLMFinetuner.com), an open-source web application that simplifies the fine-tuning process. I'll cover data collection and preparation, framework selection, and common use cases, all while focusing on making this guide accessible to those new to the world of LLMs. Data Collection and Preparation When working with LLMFinetuner, it's essential to format your data correctly. The platform supports OpenAI's Chat JSONL format, which is widely used and makes it easy to organize and structure your dataset. The format consists of a series of "prompt" and "completion" pairs, where the prompt is the input and the completion is the desired output. For example: {"prompt": "What is the capital of Nigeria?", "completion": "The capital of Nigeria is Abuja."} {"prompt": "When was the internet invented?", "completion": "The internet was invented in the late 20th century."} This format helps the model understand the relationship between inputs and outputs during the fine-tuning process. For example count recommendations, using OpenAI's gpt-3.5-turbo model, a good starting point would be around 1,000-5,000 examples for fine-tuning. However, the ideal number of examples depends on the complexity of your task and the diversity of your data. When preparing your dataset, it's important to split it into train and test sets. A common split is 80% for training and 20% for testing. This helps you evaluate the performance of the fine-tuned model on unseen data. Regarding token limits, each LLM has its own context length and maximum token limits. For instance, GPT-3.5-Turbo has a context length of 4,096 tokens and a maximum token limit of around 40,000 tokens for fine-tuning on LLMFinetuner. To estimate the number of tokens in your dataset, consider that one token is approximately equal to 4 characters for English text. Estimated Costs On LLMFinetuner, you can estimate the cost of training a file with 1 million tokens using their pricing page, which shows the cost per 1,000 tokens for each available model. Framework Selection Choosing the right fine-tuning framework is crucial for beginners with limited computing resources. LLMFinetuner is an excellent choice for several reasons: easy-to-use interface, accessibility and cost-effectiveness Common Use Cases Fine-tuning LLMs can significantly improve results in various applications, including: question answering, content creation and sentiment analysis. In conclusion, LLMFinetuner is an excellent starting point for beginners looking to explore the world of fine-tuning LLMs. With its user-friendly interface, accessibility, and cost-effective pricing, it's a great way to unlock the full potential of these powerful language models.

26 Guiding Principles for Effective Prompting 👇 Researchers from the Mohamed bin Zayed University of AI have unveiled 26 principles to help you craft better prompts and get the most out of Large Language Models (LLMs) like LLaMA and GPT. These principles are categorized into five key areas: - Prompt Structure and Clarity: Be direct: Avoid unnecessary politeness and get straight to the point. - Define your audience:  Specify who the LLM should tailor its response to (e.g., expert, child). - Break down complex tasks:  Use a series of simpler prompts for better understanding. - Use affirmative language:  Focus on what the LLM should do, not what it shouldn't. - Structure your prompts:  Use clear formatting with sections for instructions, examples, and questions. Specificity and Information: - Provide examples:  Use few-shot prompting to demonstrate the desired output format. - Seek clarity:  Ask the LLM to explain concepts in simple terms or for specific audiences. - Address bias:  Encourage unbiased and stereotype-free responses. - Provide context:  Include relevant information to guide the LLM's understanding. - Set clear requirements:  Specify keywords, regulations, or instructions for the LLM to follow. - Test your understanding:  Ask the LLM to teach you a concept and then quiz you on it. - Request detailed responses:  Ask for comprehensive information on a topic. User Interaction and Engagement: -Allow questions:  Let the LLM ask clarifying questions for better understanding. Content and Language Style: - Control the style:  Specify the desired tone and formality of the response. - Emphasize importance: Use phrases like "Your task is" and "You MUST" to highlight key points. - Set expectations:  Mention potential penalties for incorrect or irrelevant responses. - Encourage natural language:  Ask for human-like responses. - Assign a role:  Give the LLM a specific persona to guide its behavior. - Repeat key information:  Emphasize important words or phrases for better focus. - Offer incentives:  Mention potential rewards for high-quality responses. Complex Tasks and Coding Prompts: - Combine CoT with few-shot prompting: Use chain-of-thought reasoning with examples for complex tasks. - Use output primers:  Start the desired output format to guide the LLM's response. - Handle multi-file code generation: Request a script to create and manage files automatically. Read the full paper: https://lnkd.in/dn_DJAed _____________ ✔️ Click "Follow" on the Cohorte page for daily AI engineering news.

Together AI Releases RedPajama v2: An Open Dataset with 30 Trillion Tokens for Training Large Language Models https://lnkd.in/gk3uAMbB AI News, AI, AI tools, Dhanshree Shripad Shenwai, Innovation, itinai.com, LLM, MarkTechPost, t.me/itinai 🌟 Together AI Releases RedPajama v2: An Open Dataset with 30 Trillion Tokens for Training Large Language Models 🌟 Are you looking to enhance your language models and improve their performance? Together AI has just released RedPajama v2, the largest publicly available dataset for language model training. With 30 trillion high-quality English tokens, this dataset provides a solid foundation for building advanced language models. Key Features of RedPajama v2: 🔹 30 trillion high-quality English tokens 🔹 84 processed dumps from CommonCrawl 🔹 40+ quality annotations for data filtering 🔹 Deduplication clusters to eliminate duplicates RedPajama v2 is built from 84 CommonCrawl crawls and other publicly available web data. It includes raw text, quality annotations, and deduplication clusters. The dataset has undergone rigorous processing, with over 40 popular quality annotations computed for the text documents. This allows model developers to filter and reweight the dataset according to their specific needs. Deduplication using minhash signatures and Bloom filters has also been applied to eliminate duplicate data. With 113 billion documents in English, German, French, Spanish, and Italian, RedPajama v2 provides a solid foundation for extracting high-quality datasets for language model training. Despite deduplication reducing the dataset by 40%, the number of documents in the tail partition remains significant. Together AI plans to expand the set of high-quality annotations in the future, including contamination annotations, topic modeling, and categorization annotations. They also encourage the community to contribute to this initiative. To learn more about RedPajama v2, visit their Github and Reference Blog. 🚀 Evolve Your Company with AI 🚀 If you want to stay competitive and leverage AI to redefine your way of work, Together AI's RedPajama v2 dataset can be a valuable resource. Here are some practical steps to consider: 1️⃣ Identify Automation Opportunities: Find customer interaction points that can benefit from AI automation, such as customer support, lead generation, and data analysis. 2️⃣ Define KPIs: Ensure your AI initiatives have measurable impacts on business outcomes. Define key performance indicators (KPIs) to track the success of your AI projects. 3️⃣ Select an AI Solution: Choose AI tools that align with your needs and offer customization options. Look for solutions that can seamlessly integrate with your existing systems. 4️⃣ Implement Gradually: Start with a pilot project to gather data and evaluate the effectiveness of AI in your organization. Expand the usage of AI based on i...

Better Prompts -> Better Output One reason we receive undesirable results from Generative AI (GenAI) is because we don't provide good input (prompts). Vendors can't fix that themselves, but many work to help mitigate the problem. Here's how: 1. Educational materials The ease of natural language prompts, with human-like responses, has been a big draw for ChatGPT and its friends. However, some of us aren't even good communicators with people, and that can be amplified when working with GenAI. There are guides for how to communicate better with people; likewise, there are guides for how to build better prompts. For example, OpenAI offers best prompting practices, such as these for working broadly with their models and specifically with their API. (1) A solid principle emerges early: "These models can't read your mind ... the less the model has to guess at what you want, the more likely you'll get it." 2. Provide tools that help refine prompt in advance There are at least two kinds of tools being offered to help users prompt better. a. Libraries and examples Anthropic (2), for example, offers a searchable prompt library with a number of business and personal tasks. Want to learn how to convert from XML to CSV, create SQL queries, perform sentiment analysis from Twitter, or create an ELI7 from your writing? Detailed prompt examples are available. Ideogram, one of my favorite image creation tool, has a gallery (3) that exposes you to other people's creations, from the whimsical to the beautiful to the fantastic. Clicking on an image will expose the prompt used to create the image, as well as alternative images from the same prompt. b. Prompt enhancers Ideogram not only provides examples, it also has its own LLM to take our basic prompts and flesh them out to take better advantage of the platform. It calls the function "Magic Prompt". (4) A more general tool is called PromptPerfect. (5) Not bound to a specific GenAI, it can help expand upon and refine prompts for different platforms. 3. Option: Assume or Clarify When you set up a custom GPT with ChatGPT, you are asked how you want your GPT to interact with users. In particular, you are asked to choose between responding based solely on the information provided, or seeking clarification from the prompter first before proceeding. Note, creating custom GPTs was only available to paid subscribers. I'm not sure why we don't see more requests for clarification in general from the GenAI tools; some models do ask more frequently than others. #ArtificialIntelligence #PromptEngineering #GenAI (1) https://lnkd.in/eG6K8n34, https://lnkd.in/es-R-aGz (2) https://lnkd.in/eTi352my (3) https://lnkd.in/egJDhQs2 (4) https://lnkd.in/e5tUDxyp (5) https://lnkd.in/eWSbuET8

𝐌𝐞𝐭𝐚'𝐬 𝐋𝐥𝐚𝐦𝐚 3.1 𝐓𝐚𝐤𝐞𝐬 𝐨𝐧 𝐭𝐡𝐞 𝐀𝐈 𝐁𝐢𝐠 𝐋𝐞𝐚𝐠𝐮𝐞𝐬! Meta’s Llama 3.1 is here, and it's rewriting the rules of what's possible in the world of language models. This powerhouse comes in three impressive versions—8𝐁, 70𝐁, 𝐚𝐧𝐝 𝐚 𝐣𝐚𝐰-𝐝𝐫𝐨𝐩𝐩𝐢𝐧𝐠 405𝐁 𝐩𝐚𝐫𝐚𝐦𝐞𝐭𝐞𝐫𝐬. Let’s dive into what makes Llama 3.1 a game-changer and how it stacks up against the competition, especially GPT-4. 𝐊𝐞𝐲 𝐅𝐞𝐚𝐭𝐮𝐫𝐞𝐬: 🌟 𝐏𝐚𝐫𝐚𝐦𝐞𝐭𝐞𝐫 𝐒𝐢𝐳𝐞𝐬: Llama 3.1 offers models with 8B, 70B, and a groundbreaking 405B parameters. The 405B model is the largest openly available language model to date! 🌟 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞: Tested on over 150 benchmark datasets, Llama 3.1 stands tall against the best, including GPT-4 and Claude 3.5 Sonnet. It boasts a context length of 128K tokens, making it ideal for handling complex dialogues and long-form texts. 🌟 𝐎𝐩𝐞𝐧 𝐒𝐨𝐮𝐫𝐜𝐞 & 𝐀𝐜𝐜𝐞𝐬𝐬𝐢𝐛𝐢𝐥𝐢𝐭𝐲: Unlike many proprietary models, Llama 3.1 is fully open-source and free to access, fostering innovation and making advanced AI technology accessible to all. 🌟 𝐂𝐮𝐬𝐭𝐨𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧 & 𝐔𝐬𝐞 𝐂𝐚𝐬𝐞𝐬: Whether you need coding assistance, multilingual conversational agents, or long-form text summarization, Llama 3.1 is designed for diverse applications. Its integration into cloud services ensures it scales effortlessly for larger projects. 𝐇𝐨𝐰 𝐋𝐥𝐚𝐦𝐚 3.1 𝐎𝐮𝐭𝐩𝐞𝐫𝐟𝐨𝐫𝐦𝐬 𝐆𝐏𝐓-4: 💡 𝐁𝐞𝐧𝐜𝐡𝐦𝐚𝐫𝐤 𝐃𝐨𝐦𝐢𝐧𝐚𝐧𝐜𝐞: In the MMLU (multi-task language understanding) benchmark, Llama 3.1's 405B model is just 0.1 points shy of GPT-4 Omni, the current top model. 💡 𝐇𝐞𝐚𝐝-𝐭𝐨-𝐇𝐞𝐚𝐝 𝐖𝐢𝐧𝐬: In direct comparisons, Llama 3.1 405B has a higher win rate against Claude 3.5 Sonnet, a significant achievement given Claude’s strong reputation. 💡 𝐑𝐞𝐚𝐬𝐨𝐧𝐢𝐧𝐠 𝐏𝐫𝐨𝐰𝐞𝐬𝐬: On the ARC Challenge, which assesses reasoning capabilities, Llama 3.1 405B outperformed all other models. 💡 𝐌𝐚𝐭𝐡 𝐌𝐚𝐬𝐭𝐞𝐫𝐲: Scoring an exceptional 96.8% in grade school math, Llama 3.1 405B leaves GPT-4 and Claude 3.5 Sonnet in the dust. Meta's extensive human evaluations confirm that Llama 3.1 is highly competitive with GPT-4 and Claude 3.5 Sonnet across a broad range of real-world applications. With its open-source nature and superior performance, Llama 3.1 is set to democratize AI and accelerate innovation like never before. 𝑯𝒐𝒘 𝒂𝒓𝒆 𝒚𝒐𝒖 𝒈𝒐𝒊𝒏𝒈 𝒕𝒐 𝒍𝒆𝒗𝒆𝒓𝒂𝒈𝒆 𝒕𝒉𝒆 𝒑𝒐𝒕𝒆𝒏𝒕𝒊𝒂𝒍 𝒐𝒇 𝑳𝒍𝒂𝒎𝒂 3.1 𝒊𝒏 𝒃𝒖𝒊𝒍𝒅𝒊𝒏𝒈 𝒏𝒆𝒙𝒕-𝒈𝒆𝒏 𝑨𝑰 𝒂𝒑𝒑𝒍𝒊𝒄𝒂𝒕𝒊𝒐𝒏𝒔? #aimodels #ai #llama #meta #aimodels #opensourceai #aiapps #aiapplications #aicompany #aidevelopment #aiinbusiness #enterpriseai

🚀 How to Leverage the Potential of Large Language Models (LLMs) 🚀 Are you tapping into the true power of LLMs? An LLM, or Large Language Model, is a type of artificial intelligence system trained on vast amounts of text data to generate human-like text based on input prompts. Furthermore, these advanced tools are much more than just data processors; they can transform how we handle complex tasks. Here’s how you can maximize their potential: 🔹 Single Prompts (zero-shot): Using LLMs with single prompts often falls short, such as giving one clear instruction for immediate AI response. Single prompting involves giving an AI a one-time instruction to generate a specific response; for example "Summarize this article" to generate a summary of the provided text. LLMs thrive in environments where they can process and contextualize information comprehensively. 🔹 Agentic Setup: The real magic happens when LLMs are used to create agents or multi-agent systems capable of autonomously managing tasks. This approach can drive significant technological advancements.There different ways to design such a system 🔹 Agentic Design Patterns: Enhance your LLMs with sophisticated design patterns that promote deeper reasoning and problem-solving capabilities: Reflection: Enable your LLMs to self-review and improve their outputs iteratively. For instance, after generating a blog post, the same AI reviews the text, checking for grammatical errors, coherence, and style consistency. It then revises the draft based on its review. Tool Use: Extend the functionality of your LLMs by integrating them with external tools to perform tasks beyond their native capabilities, such as image manipulation or complex data analysis. Planning: Before executing any actions, an LLM can devise a detailed plan for approaching complex tasks or projects. This could involve planning a commute to Paris, developing a new software feature, or outlining the steps for an upcoming marketing campaign. By using the prompt "think step by step," you can guide the LLM to break down the task into manageable, sequential steps. Multi-Agent Systems: Develop environments where multiple AI agents interact to simulate complex organizational processes, enhancing creativity and efficiency in problem-solving. E.g., a virtual event planning system uses multiple AI agents: one as the event coordinator, another as the publicity manager, and a third as the attendee support specialist. These agents collaborate to organize and advertise the event, and handle attendee queries, ensuring the event runs smoothly. All these design patterns can be used to fulfill different specific purposes.  At automaited, we incorporate all these in order create a unique way of automating business processes. How are you integrating these sophisticated models into your operations to increase productivity and innovation? #AI #MachineLearning #TechnologyInnovation #LLMs #ArtificialIntelligence