Ashish Belwase’s Post

Building Your Own LLM Application: A Step-by-Step Guide by Aishwarya Naresh Reganti Feeling overwhelmed by terms like RAG, Prompts, Memory, and Agents in LLM applications? Fear not! With this comprehensive guide, you'll master these concepts step by step and create sophisticated LLM applications. It starts with fundamental concepts and explore frameworks like LlamaIndex, LangChain, and Hugging Face. For each component, find detailed explanations and a curated list of free resources including coding notebooks, videos, and articles. What You'll Cover: 1️⃣ Creating a basic LLM app: Start with prompts and LLMs. 2️⃣ Chaining prompts: Progress to multiple objectives. 3️⃣ Integrating an external knowledge base: Implement RAG. 4️⃣ Enhancing LLMs with memory: Contextual understanding. 5️⃣ LLMs interacting with external tools: Enabling dynamic responses. 6️⃣ Developing LLM agents: Decision-making capabilities. 7️⃣ Fine-tuning LLMs: Exploring frameworks and PEFT methods. Follow this structured approach to grasp each component thoroughly. Happy learning! Thank you Aishwarya Naresh Reganti for sharing this invaluable resource! #llms #genai #rag #finetuning #llmagents

Building Your Own LLM Application: A Step-by-Step Guide by Aishwarya Naresh Reganti. Feeling overwhelmed by terms like RAG, Prompts, Memory, and Agents in LLM applications? Fear not! With this comprehensive guide, you'll master these concepts step by step and create sophisticated LLM applications. It starts with fundamental concepts and explore frameworks like LlamaIndex, LangChain, and Hugging Face. For each component, find detailed explanations and a curated list of free resources including coding notebooks, videos, and articles. What You'll Cover: 1️⃣ Creating a basic LLM app: Start with prompts and LLMs. 2️⃣ Chaining prompts: Progress to multiple objectives. 3️⃣ Integrating an external knowledge base: Implement RAG. 4️⃣ Enhancing LLMs with memory: Contextual understanding. 5️⃣ LLMs interacting with external tools: Enabling dynamic responses. 6️⃣ Developing LLM agents: Decision-making capabilities. 7️⃣ Fine-tuning LLMs: Exploring frameworks and PEFT methods. Follow this structured approach to grasp each component thoroughly. Happy learning! Thank you Aishwarya Naresh Reganti for sharing this invaluable resource! #llms #genai #rag #finetuning #llmagents

Step by step guide to build LLM Apps every AI Aspirant should know. #sriniw8z #airobohub

👩💻 Building LLM applications becomes simple when you take it step by step. Check out my detailed guide, covering everything from basics to advanced components, with free resources and notebooks for each stage. 😨 If you're looking to build LLM applications and feel overwhelmed by terms like RAG, Prompts, Memory, Vector databases, Agents, etc., fear not! You can master it all step by step to create a sophisticated LLM application. 💡 We'll begin with fundamental concepts and explore various frameworks such as LlamaIndex, LangChain, and Hugging Face. For each component, we'll provide detailed explanations along with a list of the best free resources like coding notebooks, videos, and articles. 🖇 Here's the link to my guide: https://lnkd.in/e6GTJT6Y Here's what you'll cover 1️⃣ Creating a basic LLM app (using a prompt and LLM). 2️⃣ Progressing to chaining prompts for multiple objectives. 3️⃣ Integrating an external knowledge base and implementing RAG. 4️⃣ Enhancing LLMs with memory for contextual understanding. 5️⃣ Enabling LLMs to interact with external tools. 6️⃣ Developing LLM agents capable of decision-making. 7️⃣ Exploring frameworks and PEFT methods for fine-tuning LLMs. By following this structured approach, you'll grasp each component thoroughly without confusion. Happy learning! 🚨 I post #genai content daily, follow along for the latest updates! #llms #rag #finetuning #llmagents

Basics to advanced components for building a LLM applications. #generativeai #ai #llms #rag #finetuning #llmagents #nlp #advancenlp

💥 Step-by-step guide to building LLM applications, from basics to advanced components. 💥 1️⃣ Creating a basic LLM app (using a prompt and LLM). 2️⃣ Progressing to chaining prompts for multiple objectives. 3️⃣ Integrating an external knowledge base and implementing RAG. 4️⃣ Enhancing LLMs with memory for contextual understanding. 5️⃣ Enabling LLMs to interact with external tools. 6️⃣ Developing LLM agents capable of decision-making. 7️⃣ Exploring frameworks and PEFT methods for fine-tuning LLMs. credits: Aishwarya Naresh Reganti #generativeai #ai #llms #rag #finetuning #llmagents #nlp #advancenlp 

🚀 Introducing the Future: LLM OS! 🚀 Imagine an operating system where LLMs (Large Language Models) are the kernel process, seamlessly solving problems by coordinating various resources like memory and computation tools. Welcome to LLM OS, a revolutionary leap in technology! 🌐💡 Key Features of LLM OS: 📝 **Read/Generate Text**: Effortlessly process and produce written content. 📚 **Vast Knowledge**: Possesses more knowledge than any single human on all subjects. 🌍 **Internet Browsing**: Access the web for real-time information and resources. 🛠 **Software Integration**: Utilize existing software infrastructure (calculators, Python, mouse/keyboard). Coming soon ... capabailty 🎨 **Visual Creativity**: See and generate images and videos with ease. 🎶 **Audio Capabilities**: Hear, speak, and even create music. 🧠 **Deep Thinking**: Engage in prolonged, complex thought processes. 🔧 **Self-Improvement**: Continuously enhance its capabilities in various domains. 🔍 **Customization**: Fine-tune for specific tasks to meet unique needs. 🔗 **LLM Communication**: Interact and collaborate with other LLMs. Join us on this exciting journey into the future of technology with LLM OS. Let's redefine what operating systems can do! 🌟✨ #Innovation #AI #TechRevolution #LLMOS #FutureOfComputing #AIOS

Pro Tip for Working with LLMs: When you ask a Language Learning Model (LLM) to generate code and it doesn't work, request fixes. If you hear "I apologize for the oversight" three times, it's time to pause—further attempts might make the code messier. At this point, you have two options: 1. Manually correct the code. 2. Start over with the same or a different LLM, though there's no guarantee of better results. Happy coding!

Hey fellow developers! 🚀 When it comes to coding for real-world use, we're all about efficiency and excellence. This rings especially true when we're dealing with large language models (LLMs). To ensure our applications are top-notch, it's crucial to systematically evaluate LLM outputs. Also, always wondered how LLMs can be tested and how to achieve a test-driven LLM development. Hereby a solution to do that how. Enjoy your reading! #LLMs #GenAI #QA https://lnkd.in/ehTgMWdm

For anyone working on LLMs - I highly recommend this short yet informative course - "Improving Accuracy of LLM Applications" for insights on Finetuning LLMs and how it might be a better alternative to using RAGs. Thanks DeepLearning.AI for making this possible.