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#Langfuse Reel by @mohamedihebhichri - 💡 Query Enhancement Explained! Your RAG system isn't broken - your query is weak! 😅 Short, vague questions make your Retriever pull the wrong docs,
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@mohamedihebhichri
💡 Query Enhancement Explained! Your RAG system isn’t broken — your query is weak! 😅 Short, vague questions make your Retriever pull the wrong docs, and your LLM hallucinates. The solution? Query Enhancement (Expansion)! An LLM rewrites your question, expands it, and makes it powerful enough to fetch the right documents. In LangChain, it’s simple: LLM + Prompt = Chain. ✅ Watch how this tiny step transforms your RAG answers from wrong to perfect! 🚀 💻 Want the Python code notebook for Query Enhancement? Comment below and I’ll send it to you!#RAG #LLM #LangChain #AI #Python MachineLearning QueryEnhancement
#Langfuse Reel by @withsarvesh.ai - One-shot answers are so 2023. 📉 If you're still copy-pasting the first thing an LLM gives you, you're leaving quality on the table. Enter LangChain
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@withsarvesh.ai
One-shot answers are so 2023. 📉 If you’re still copy-pasting the first thing an LLM gives you, you’re leaving quality on the table. Enter LangChain Deep Agents. 🧠 These aren’t just prompts; they are iterative workflows that: 1️⃣ Review their own work. 2️⃣ Self-critique for improvements. 3️⃣ Refine until the job is done. It’s like having a 24/7 intern that actually learns from its own mistakes. Check out the demo to see it in action! 💻✨ Want the code? Link in bio/comments! 🔗 👉 Follow @withsarvesh.ai for more AI insights. 🌐 https://www.learnwithsarvesh.com #LangChain #AI #GenerativeAI #Python #LLM DeepAgents AISarvesh
#Langfuse Reel by @average.yash - Day 2 of my 50 Days of GenAI challenge. Today we talked about prompts and how LLMs actually follow instructions. There are different types of prompt
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@average.yash
Day 2 of my 50 Days of GenAI challenge. Today we talked about prompts and how LLMs actually follow instructions. There are different types of prompts: - System prompts define the model’s behavior and role - User prompts define the actual task or question System prompts have higher priority and guide how the model responds, even when the user input changes. Prompting is not about clever wording. It’s about structure, clearly defining: - the role - the task - the constraint Once this clicks, prompt outputs become far more predictable and reliable. Code shown in the video is available on GitHub (link in bio). #GenAI #PromptEngineering #LLM #AIForDevelopers #50DaysOfGenAI
#Langfuse Reel by @smabdullah958 - What is LangChain & Why Every Developer Should Care LangChain is a framework that helps developers build real-world AI applications using LLMs like Op
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@smabdullah958
What is LangChain & Why Every Developer Should Care LangChain is a framework that helps developers build real-world AI applications using LLMs like OpenAI or Gemini. Calling an AI API is easy. Building a production-ready AI system is not. That’s where LangChain comes in. It helps you: • Manage prompts properly • Add memory to chatbots • Connect AI to PDFs & databases • Build AI agents • Reduce hallucinations with retrieval If you’re already a MERN developer, you’re closer to AI than you think. The future isn’t “AI vs Developers.” It’s Developers who understand AI architecture. #AI#langchain #llm #mern #softwareengineering
#Langfuse Reel by @average.yash - Day 12 of my 50 days of GenAI challenge. RAG works beautifully - until your documents get long. If you embed an entire PDF as a single vector, you c
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@average.yash
Day 12 of my 50 days of GenAI challenge. RAG works beautifully — until your documents get long. If you embed an entire PDF as a single vector, you compress multiple topics into one representation. When a user asks a specific question, retrieval becomes noisy. The solution is chunking. Instead of embedding one large document, we split it into smaller pieces and embed each chunk separately. Now retrieval happens at the chunk level, not the document level. This improves: • Precision • Relevance • Answer grounding • Context efficiency Chunking strategies include: • Paragraph-based • Fixed token size • Overlapping windows • Sentence-based • Semantic chunking There is no universal best strategy — it depends on your data and use case. Good retrieval starts with good chunking. [genai, rag chunking, document splitting, retrieval pipeline, embedding chunks, semantic retrieval, context optimization, llm architecture] #GenAI #RAG #Chunking #LLMEngineering #AIDevelopment
#Langfuse Reel by @datastreakofficial (verified account) - Want to build production-ready RAG systems? In this video, I break down the complete Retrieval-Augmented Generation (RAG) architecture - from data ing
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@datastreakofficial
Want to build production-ready RAG systems? In this video, I break down the complete Retrieval-Augmented Generation (RAG) architecture — from data ingestion and chunking to embeddings, vector databases, and final LLM response generation. If you're preparing for system design interviews or building real-world GenAI applications, this is a must-know architecture. #rag #genai #llm #systemdesign #aiarchitecture
#Langfuse Reel by @vidyanex_consulting - 🚀 Learn Large Language Models (LLMs) the practical way - no long videos, only hands-on coding. This GitHub repository teaches LLMs from beginner to a
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@vidyanex_consulting
🚀 Learn Large Language Models (LLMs) the practical way — no long videos, only hands-on coding. This GitHub repository teaches LLMs from beginner to advanced level using ✅ Google Colab notebooks ✅ Ready-to-run code ✅ Step-by-step chapters What you’ll learn in this LLM roadmap: • LLM basics, tokens, and embeddings • Transformer architecture explained with code • Text classification using NLP • RAG (Retrieval-Augmented Generation) systems • Semantic search & vector databases • Multimodal LLM applications • Prompt engineering techniques • Fine-tuning and deploying production-ready LLMs 💬 Comment “LLMS” to get the GitHub repo links in DM. 📌 Save this reel if you’re learning AI. 🔥 Follow Vidyanex for daily AI, LLM, and GenAI content. #llm #explore #github #GenAI #explore
#Langfuse Reel by @hasnaat.iftikhar - Spent the last few days deep in LLM Engineering research, frontier models, orchestration logic, role-based prompting, and multi-agent systems. Then I
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@hasnaat.iftikhar
Spent the last few days deep in LLM Engineering research, frontier models, orchestration logic, role-based prompting, and multi-agent systems. Then I gave myself a 4-hour challenge: Build something real. Introducing AgentForge, a multi-agent AI programming simulator! GitHub Link: https://github.com/hasnaat-iftikhar/AgentForge-Multi-Agent-AI-Programming-Simulator It simulates: • 👨‍💻 Junior Developer Agent • 🧑‍💼 Senior Engineer Agent • 🏗 Platform Engineer Agent (architecture + review) Instead of just generating code, these agents: • Collaborate • Review each other • Iterate • Improve output step-by-step Built with: • React + Tailwind + ShadCN • Python + Flask • OpenAI APIs (GPT-4o-mini for testing) Focused on orchestration, iteration control, and system design, not just prompting. GitHub link in comments. Would love your feedback!! #AI #LLM #MultiAgent #OpenSource #ReactJS #Python #Flask #ArtificialIntelligence #AgenticAI #LLMEngineering #BuildInPublic #SoftwareEngineering

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