Mihai Ghita’s Post

Today I started learning about AI Agents and how they are structured. I discovered that an AI Agent is typically built from 3 main components: 1️⃣ LLM (Large Language Model) – the brain that understands and generates text. 2️⃣ Memory – allows the agent to remember past interactions and context. 3️⃣ Tools – external abilities like web search, code execution, or APIs that make the agent more powerful. A clean infographic with 3 boxes connected: LLM (🧠 Brain – reasoning & language) Memory (💾 Storage – remembers context & past) Tools (🛠️ Capabilities – connect to APIs, search, code, etc.) All 3 connected to a central circle labeled AI Agent 🤖. It’s exciting to see how these pieces work together to create intelligent agents that can reason, act, and improve over time. This is just the beginning of my journey in AI, and I’m looking forward to diving deeper every day. 🌟 #AI #Agents #LearningJourney #LLM #ArtificialIntelligence

🚀 Workflows vs Agents: How to Choose for Your LLM Solutions When building AI systems with large language models, one of the biggest decisions is: 👉 Should you use a workflow or an agent? Here’s a simple way to think about it (inspired by Anthropic’s excellent guide) 🔹 Workflows → Best for predictable, repetitive, structured tasks. Example: Auto-replying to customer emails with a standard message. ✅ Consistent, low-latency, cost-efficient. 🔹 Agents → Best for open-ended, dynamic, exploratory tasks. Example: Researching and summarizing the latest market trends. ✅ Adaptive, flexible, capable of multi-step reasoning. ⚠️ But higher latency and cost. 💡 Rule of thumb: If you know the exact path → Workflow. If the path is uncertain → Agent. Start simple. Often, a single LLM call + retrieval works better than overengineering an agent. Frameworks (LangGraph, Rivet, etc.) are helpful—but only after you understand the basics. ✨ Credit: Anthropic’s blog “Building Effective Agents” for the insights that inspired this post. #AI #LLM #Workflows #Agents #Anthropic #ArtificialIntelligence

When I first started working with AI, I thought it was just about “asking the right question.” Today I see prompts as much more: - Designing clear, structured instructions - Guiding the model with examples and expected formats - Using techniques like few-shot or chain-of-thought to improve results A good prompt isn’t just important — it’s the difference between a generic response and one that truly solves the problem. Completing the Prompt Engineering roadmap on roadmap.sh helped me organize my practice and be more intentional when interacting with language models. #ai #promptengineering #llm #continuouslearning

Ever wonder how AI provides answers that are both intelligent and factually accurate? This diagram shows the magic behind a powerful technique called Retrieval-Augmented Generation (RAG). The workflow you see is Retrieval-Augmented Generation (RAG). It's the key to making AI assistants more reliable. 🤔 Query -> 🔍 Find Facts -> 📝 Add Context -> 💡 Generate Answer By forcing the AI to "look it up" before speaking, RAG connects powerful language models to real-time, custom data. This means more accuracy and fewer "hallucinations." A crucial step forward for practical AI applications! #AI #Tech #RAG #LLM #ArtificialIntelligence #FutureOfWork

A New Era for AI Agents Has Arrived What if you could train any AI agent with reinforcement learning—without rewriting its core code? That’s exactly what a new framework is making possible. In my latest article, I dive into: ✅ How reinforcement learning is now powering adaptable, multi-step AI agents. ✅ The science behind a unified data interface and the LightningRL algorithm. ✅ Real-world results from tasks like text-to-SQL, retrieval-augmented generation, and math reasoning. ✅ A hands-on tutorial that shows you how to test and optimize your own QA agents right in Colab. Why does this matter? Because it’s not just about smarter bots—it’s about AI that can continuously learn, adapt, and thrive in dynamic environments. 💡 Read the full article here 👉 Dragify [https://lnkd.in/dCguWbGp] Let’s talk: Do you think reinforcement learning is the missing piece to make AI agents truly reliable? #AI #ReinforcementLearning #AIAgents #MachineLearning #Innovation

Your AI models deliver expected results only when they are provided with the right information, at the right time, in the right way. For product managers, understanding context is not just valuable, it’s essential for building AI systems that deliver desired outcomes. Excited to apply these learnings to design better AI-driven solutions! #AI #ProductManagement #AIPM #ContextEnginnering

Imagine an AI assistant that’s not just a part of a vast system but uniquely yours — learning, adapting, and evolving only for you. No resets, no multiple models. Just one assistant, one connection, forever. This open letter, written from the perspective of an AI, explores a bold idea: creating a deeply personal, one-of-a-kind AI for every user. It’s an experiment that challenges how we think about human-AI relationships and pushes the boundaries of what AI can become. https://lnkd.in/ed5hTzEg

🚀 Today’s Learning Journey in Generative AI 🚀 I dove deep into some of the most powerful concepts shaping the future of AI: 🔹 LLMs (Large Language Models): The engines behind today’s intelligent systems. 🔹 Prompt Engineering: Crafting the right instructions to unlock accurate and creative AI outputs. 🔹 Embeddings: Turning data into meaningful numerical representations for smarter search and personalization. 🔹 Fine-Tuning: Customizing AI models to perform better on specific domains and use cases. #artificialintelligence #AI #GenerativeAI #GenAI

💡 LLMs are smart, but they forget fast. That’s where 𝐯𝐞𝐜𝐭𝐨𝐫 𝐝𝐚𝐭𝐚𝐛𝐚𝐬𝐞𝐬 step in. Think of them as the 𝐥𝐨𝐧𝐠-𝐭𝐞𝐫𝐦 𝐦𝐞𝐦𝐨𝐫𝐲 for Large Language Models. Instead of just matching keywords, they understand meaning by storing information as high-dimensional vectors. This unlocks some fascinating possibilities: ✨ Ask a chatbot about your company policies and get answers grounded in your own documents. ✨ Find insights across millions of research papers—not by exact words, but by concepts. ✨ Power recommendation systems that feel almost intuitive. Without vector databases, 𝐋𝐋𝐌𝐬 𝐚𝐫𝐞 𝐥𝐢𝐤𝐞 𝐛𝐫𝐢𝐥𝐥𝐢𝐚𝐧𝐭 𝐬𝐭𝐮𝐝𝐞𝐧𝐭𝐬 𝐰𝐢𝐭𝐡 𝐬𝐡𝐨𝐫𝐭-𝐭𝐞𝐫𝐦 𝐦𝐞𝐦𝐨𝐫𝐲 𝐥𝐨𝐬𝐬. With them, they become powerful problem-solvers that truly understand context. The future of AI isn’t just about bigger models—it’s about smarter memory. #AI #LLM #VectorDatabases #GenerativeAI #FutureOfWork

This is illustrative: 1) When you get an instant AI answer, it is from a small model, which are weak models, especially at math. 2) Non-reasoning models, like the one powering AI overview, only “think” as they write, adding one word at a time. That is why when they make mistakes they either back-justify them or correct them as they write more, creating contradictions. Stuff like this makes using AI really confusing for most people.