How to Learn AI Evals for PMs in 5 Minutes

🔮 Future-Aware AI Agents Are Here — smolagents just got superpowers. With Output Schema support in smolagents v1.22, AI agents can now “see the future” of tool outputs: ⚡ Faster execution ✅ More reliable performance 🚀 Smarter, schema-aware code with fewer steps 👉 Full details here: https://lnkd.in/eVshcgmw Huge thanks to Hugging Face and Model Context Protocol (MCP) for the support in making this possible 🙏 And according to benchmarks, these improvements hold true across leading models — OpenAI GPT-4.1, Mistral AI Mistral-Small, Google DeepMind Gemini 2.5 Flash, and Anthropic Claude.

💡 Automating PDF OCR with Mistral AI + n8n (and exporting neatly into Markdown) I’ve been working on a problem that many of us face: ➡️ How do you take a scanned or image‑based PDF and turn it into structured, editable text, without hopping between different tools? So, I built an end‑to‑end no‑code/low‑code workflow in n8n, powered by Mistral OCR 🚀 Here’s what happens under the hood: 1️⃣ Upload a PDF through a simple form. 2️⃣ It’s sent to Mistral OCR for high‑quality text + image extraction. 3️⃣ Each page is processed and preserved in Markdown format (.md). 4️⃣ The user instantly gets a downloadable file, with pages cleanly separated and images kept inline. ✨ Result → A single, well‑structured, portable .md file you can open anywhere, version‑control, or feed into downstream NLP / LLM pipelines. This means: No more copy‑pasting from scanned PDFs. Easy to integrate with documentation workflows, knowledge bases, or AI prompt pipelines. Completely extensible (could just as easily output .docx, .txt, or pipe into Slack / Notion). Automation isn’t just about saving time - it’s about building bridges between tools. By combining Mistral’s OCR power with n8n’s flexibility, you can unlock endless text‑processing automation! 🔗 If you’ve ever struggled with scanned PDFs, this approach might just save you hours. 🙌 Curious to hear: How are you handling OCR or document parsing today? #OCR #Automation #n8n #MistralAI #Markdown #Productivity #NoCode #AI

🚀 Breaking Down RAG Architecture: Retriever + Generator Retrieval-Augmented Generation (RAG) is one of the most powerful techniques driving today’s AI applications – from intelligent chatbots to enterprise search assistants. At its core, a RAG system has two main components: 🔹 Retriever – Finds the most relevant information. 🔹 Generator – Uses that information to produce accurate, context-aware responses. But what makes the retriever so effective? Let’s look deeper: ✅ Vector Index – A data structure (like a map) that makes searching for similar vectors faster. It often uses approximate nearest neighbor search, trading a bit of accuracy for speed. ✅ Vector Similarity Search – Finds vectors closest to a query. Common methods include: • Cosine Similarity (best for meaning-based text comparison) • Euclidean Distance (focuses on intensity/magnitude) ✅ Embedding Model – Converts text into embeddings (lists of numbers). These embeddings capture meaning/context and allow machines to “understand” language. ✅ Text Chunking – Splitting text into smaller, meaningful pieces improves retrieval accuracy. Choosing the right chunking strategy (sentence, paragraph, sliding window) is key. ✅ Retriever Pipeline – Query → Embedding → Vector Search → Return Relevant Chunks → Pass to Generator. ✨ The magic of RAG lies in how well the retriever prepares knowledge for the generator. A strong retriever = a more accurate, trustworthy AI system. 👉 What do you think is the biggest challenge in building an effective retriever – embedding quality, vector indexing, or chunking strategy? #AI #RAG #VectorSearch #Embeddings #LLM #GenerativeAI

AI agent routing is foundational. Here’s a quick tour of four approaches. 👇 ML-based routing: Use a separate classifier trained on a labeled dataset. Unlike an LLM, the decision isn’t generated from a prompt—the selection logic is encoded in the model’s weights. LLM-based routing: Give the language model a prompt so it parses the request and returns a simple label/command indicating the next step. Example: “Analyze the query and return exactly one category: ‘Order status,’ ‘Product information,’ ‘Tech support,’ or ‘Other.’” The agent reads the label and follows the appropriate path. Embedding-based routing: Convert the request into a vector (embedding) and compare it with vectors that represent different routes/capabilities. Choose the path whose vector is closest. This is about semantics—deciding by meaning, not just keywords. If-Else routing: Works on predefined if-else rules that look at keywords, patterns, or structured fields extracted from the request. In practice, LLM-based routing is used most often—it delivers the best results and underpins most high-level agent libraries. Dedicated ML models for routing are less common. The other approaches—if-else and embeddings—are more often used as supporting layers.

𝑷𝒓𝒐𝒋𝒆𝒄𝒕 : 𝑨𝒖𝒕𝒐𝒎𝒂𝒕𝒆𝒅 𝑴𝒖𝒍𝒕𝒊-𝑴𝒐𝒅𝒆𝒍 𝑬𝒗𝒂𝒍𝒖𝒂𝒕𝒊𝒐𝒏 𝑭𝒓𝒂𝒎𝒆𝒘𝒐𝒓𝒌 𝒇𝒐𝒓 𝑳𝒐𝒄𝒂𝒍 𝑳𝑳𝑴𝒔 𝒘𝒊𝒕𝒉 𝑷𝒓𝒐𝒎𝒑𝒕𝒇𝒐𝒐 𝒂𝒏𝒅 𝑶𝒍𝒍𝒂𝒎𝒂 I’m excited to share my latest project LLM Testing Framework, where I evaluated and compared two local AI models (Gemma 2B & Mistral 7B) using 𝗣𝗿𝗼𝗺𝗽𝘁𝗳𝗼𝗼.To analyze how different Large Language Models (LLMs) respond to the same set of prompts, and measure consistency, reasoning ability, and adaptability across models completely offline using Ollama. 𝗧𝗼𝗼𝗹𝘀 & 𝗧𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝗶𝗲𝘀:  - 𝗣𝗿𝗼𝗺𝗽𝘁𝗳𝗼𝗼 – For structured LLM evaluation and test automation  - 𝗢𝗹𝗹𝗮𝗺𝗮 – To run models locally and ensure data privacy  - 𝗚𝗲𝗺𝗺𝗮 𝟮𝗕 (𝗚𝗼𝗼𝗴𝗹𝗲) & 𝗠𝗶𝘀𝘁𝗿𝗮𝗹 𝟳𝗕 (𝗠𝗶𝘀𝘁𝗿𝗮𝗹 𝗔𝗜) – Tested AI models  - 𝗬𝗔𝗠𝗟 𝗖𝗼𝗻𝗳𝗶𝗴𝘂𝗿𝗮𝘁𝗶𝗼𝗻 – For prompt design and test management 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗛𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁𝘀: 1. Ran multi-model evaluations using defined prompts and variables 2. Compared outputs from both models side-by-side 3. Observed how Gemma simplifies answers while Mistral provides deeper reasoning 4. Created a reusable LLM testing workflow for QA or AI benchmarking This project helped me understand how local LLMs differ in reasoning, tone, and adaptability, and how tools like Promptfoo make LLM testing measurable, automated, and repeatable.I plan to extend this framework to include cloud-based models like GPT-4 and integrate automated evaluation metrics for factual accuracy and tone consistency. 𝗚𝗶𝘁𝗵𝘂𝗯 𝗥𝗲𝗽𝗼 : https://lnkd.in/gRMarVzs 𝗔 𝘃𝗶𝘀𝘂𝗮𝗹 𝘀𝗻𝗮𝗽𝘀𝗵𝗼𝘁 𝗼𝗳 𝗵𝗼𝘄 𝘁𝘄𝗼 𝗟𝗟𝗠𝘀 - 𝗚𝗲𝗺𝗺𝗮 𝗮𝗻𝗱 𝗠𝗶𝘀𝘁𝗿𝗮𝗹 𝗱𝗲𝗳𝗶𝗻𝗲 𝘁𝗵𝗲 𝘀𝗮𝗺𝗲 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻 𝘁𝗵𝗿𝗼𝘂𝗴𝗵 𝗰𝗼𝗺𝗽𝗹𝗲𝘁𝗲𝗹𝘆 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁 𝗹𝗲𝗻𝘀𝗲.

MCP by Anthropic 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝗡𝗲𝗲𝗱𝘀 𝗠𝗼𝗿𝗲 𝗧𝗵𝗮𝗻 𝗝𝘂𝘀𝘁 𝗣𝗿𝗼𝗺𝗽𝘁𝘀 ! And 𝗠𝗼𝗱𝗲𝗹 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗣𝗿𝗼𝘁𝗼𝗰𝗼𝗹 (𝗠𝗖𝗣) could be a starting point for something bigger! We’ve reached a tipping point with agentic AI systems. We’re chaining tools, documents, APIs, and memory into complex flows — but we're still feeding LLMs with unstructured, brittle prompts. The problem? There’s no shared language between agents, memory, tools, and the models that power them. That’s exactly what Model Context Protocol (MCP) is designed to solve. 𝗜𝗺𝗮𝗴𝗶𝗻𝗲 𝗱𝗲𝘀𝗰𝗿𝗶𝗯𝗶𝗻𝗴 𝘆𝗼𝘂𝗿 𝘁𝗮𝘀𝗸 𝗹𝗶𝗸𝗲 𝘁𝗵𝗶𝘀: ➘ 𝗚𝗼𝗮𝗹: Summarize the latest research paper ➘ 𝗧𝗼𝗼𝗹𝘀 𝗮𝘃𝗮𝗶𝗹𝗮𝗯𝗹𝗲: Arxiv API, citation extractor ➘ 𝗠𝗲𝗺𝗼𝗿𝘆: Past queries and generated summaries ➘ 𝗖𝗼𝗻𝘀𝘁𝗿𝗮𝗶𝗻𝘁𝘀: Max 500 words, technical accuracy And then sending that as a clean, structured context object to your model — one it can actually understand and reason over. No bloated prompt. No guessing. But what does MCP solve? ✅ It gives structure to ambiguity. Instead of stuffing everything into a giant prompt, MCP lets you explicitly define context as 𝗴𝗼𝗮𝗹𝘀, 𝗰𝗼𝗻𝘀𝘁𝗿𝗮𝗶𝗻𝘁𝘀, 𝘁𝗼𝗼𝗹𝘀, 𝗵𝗶𝘀𝘁𝗼𝗿𝘆, 𝗮𝗻𝗱 𝗺𝗼𝗿𝗲. ✅ It enables true 𝘢𝘨𝘦𝘯𝘵 𝘪𝘯𝘵𝘦𝘳𝘰𝘱𝘦𝘳𝘢𝘣𝘪𝘭𝘪𝘵𝘺. Think of it as a protocol that allows agents, tools, and memory systems to speak the same language when interacting with models. ✅ It’s the bridge between 𝘳𝘢𝘸 𝘤𝘰𝘮𝘱𝘶𝘵𝘢𝘵𝘪𝘰𝘯 and 𝘤𝘰𝘨𝘯𝘪𝘵𝘪𝘷𝘦 𝘢𝘳𝘤𝘩𝘪𝘵𝘦𝘤𝘵𝘶𝘳𝘦. MCP treats LLMs as context-sensitive reasoning engines — giving them the environment and memory they need to behave more intelligently and predictably. In simpler terms: > It’s 𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝗽𝗿𝗼𝗺𝗽𝘁 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴. > It’s 𝗰𝗼𝗻𝘁𝗲𝘅𝘁 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴. And that’s the shift we need if we’re serious about building autonomous research agents, multi-agent planners, dev assistants, or any intelligent AI system that works across tools and time. If you’re working on LangGraph, AutoGen, CrewAI, OpenDevin, or your own agent framework — keep an eye on MCP. This protocol might just become the TCP/IP of intelligent agents. Want to know more? Follow me or connect🥂 Please don't forget to like❤️ and comment💭 and repost♻️ x.com/sina_riyahi medium.com/@Sina-Riyahi Instagram.com/Cna_Riyahi github.com/sinariyahi

Getting started with AI Agents can be very overwhelming Here's a simple guide to help you move faster.... If you checked my post on the CB Insights report, 2025 leads with major AI Agent investments. So this is the right time to start learning more about them Start with the fundamentals of Agent architectures and decision-making frameworks. and understand the basics of LLMs and their role as the "brains" of your agents. 📌 Master the core components of Agent systems: ↳ Planning mechanisms ↳ Memory systems ↳ Tool use & function calling ↳ Add MCP to make the Tool use easier ↳ Environment interaction ↳ Multi-Agent collaboration ↳ Core agent orchestration ↳ Data Bias and Recovery ↳ Workflow Adaptation ↳ Evaluation and Tracing ↳ Finally, learn about compliance and ethnics in agents 📌 Learn about popular Agent frameworks: ↳ Google ADK Agents: https://lnkd.in/gvCS7S2s ↳ Microsoft Magentic-One: https://lnkd.in/gD5SYhj9 ↳ LangChain Agents: https://lnkd.in/gFSUFynP ↳ Microsoft Semantic Kernel: https://lnkd.in/gXyXykhF ↳ IBM Bee: https://lnkd.in/gsSzsgai ↳ OpenAI Agent SDK: https://lnkd.in/giafrGBX ↳ CrewAI: https://learn.crewai.com/ ↳ Pydantic Agents: https://lnkd.in/gz-egBv4 This is not an exhaustive list, but it will surely help you move faster. 📌 Start building! Begin with simple task-specific agents, then work your way up to autonomous systems that can: ↳ Chain multiple tools together ↳ Maintain long-term memory ↳ Self-improve through feedback ↳ Collaborate with other agents ↳ Generates accurate agentic responses 📌 You can also utilize a few well-known coding agents to build faster ↳ Cursor AI: https://www.cursor.com/ ↳ Windsurf: https://lnkd.in/gGMeHsm6 ↳ Claude Code: https://lnkd.in/g2pmqcUm ↳ OpenAI Codex: https://lnkd.in/gmw9mTcc 📌 Utilize protocols to scale your agentic systems ↳ MCP: https://lnkd.in/g586baCv ↳ A2A: https://lnkd.in/gT-cCbX9 ↳ ANP: https://lnkd.in/g5RnZtB9 ↳ ACP: https://lnkd.in/gfcjQnXU ↳ AGORA: https://lnkd.in/g7qvtYhP Just to name a few. Save 💾 ➞ React 👍 ➞ Share ♻️ & follow for everything related to AI Agents

AI Agents are not just tools like ChatGPT or Copilot — they’re autonomous systems that reason, integrate, and execute. 🚀 #AI #ML

Getting started with AI Agents can be very overwhelming Here's a simple guide to help you move faster.... If you checked my post on the CB Insights report, 2025 leads with major AI Agent investments. So this is the right time to start learning more about them Start with the fundamentals of Agent architectures and decision-making frameworks. and understand the basics of LLMs and their role as the "brains" of your agents. 📌 Master the core components of Agent systems: ↳ Planning mechanisms ↳ Memory systems ↳ Tool use & function calling ↳ Add MCP to make the Tool use easier ↳ Environment interaction ↳ Multi-Agent collaboration ↳ Core agent orchestration ↳ Data Bias and Recovery ↳ Workflow Adaptation ↳ Evaluation and Tracing ↳ Finally, learn about compliance and ethnics in agents 📌 Learn about popular Agent frameworks: ↳ Google ADK Agents: https://lnkd.in/gvCS7S2s ↳ Microsoft Magentic-One: https://lnkd.in/gD5SYhj9 ↳ LangChain Agents: https://lnkd.in/gFSUFynP ↳ Microsoft Semantic Kernel: https://lnkd.in/gXyXykhF ↳ IBM Bee: https://lnkd.in/gsSzsgai ↳ OpenAI Agent SDK: https://lnkd.in/giafrGBX ↳ CrewAI: https://learn.crewai.com/ ↳ Pydantic Agents: https://lnkd.in/gz-egBv4 This is not an exhaustive list, but it will surely help you move faster. 📌 Start building! Begin with simple task-specific agents, then work your way up to autonomous systems that can: ↳ Chain multiple tools together ↳ Maintain long-term memory ↳ Self-improve through feedback ↳ Collaborate with other agents ↳ Generates accurate agentic responses 📌 You can also utilize a few well-known coding agents to build faster ↳ Cursor AI: https://www.cursor.com/ ↳ Windsurf: https://lnkd.in/gGMeHsm6 ↳ Claude Code: https://lnkd.in/g2pmqcUm ↳ OpenAI Codex: https://lnkd.in/gmw9mTcc 📌 Utilize protocols to scale your agentic systems ↳ MCP: https://lnkd.in/g586baCv ↳ A2A: https://lnkd.in/gT-cCbX9 ↳ ANP: https://lnkd.in/g5RnZtB9 ↳ ACP: https://lnkd.in/gfcjQnXU ↳ AGORA: https://lnkd.in/g7qvtYhP Just to name a few. Save 💾 ➞ React 👍 ➞ Share ♻️ & follow for everything related to AI Agents

Getting started with AI Agents can be very overwhelming Here's a simple guide to help you move faster.... If you checked my post on the CB Insights report, 2025 leads with major AI Agent investments. So this is the right time to start learning more about them Start with the fundamentals of Agent architectures and decision-making frameworks. and understand the basics of LLMs and their role as the "brains" of your agents. 📌 Master the core components of Agent systems: ↳ Planning mechanisms ↳ Memory systems ↳ Tool use & function calling ↳ Add MCP to make the Tool use easier ↳ Environment interaction ↳ Multi-Agent collaboration ↳ Core agent orchestration ↳ Data Bias and Recovery ↳ Workflow Adaptation ↳ Evaluation and Tracing ↳ Finally, learn about compliance and ethnics in agents 📌 Learn about popular Agent frameworks: ↳ Google ADK Agents: https://lnkd.in/gvCS7S2s ↳ Microsoft Magentic-One: https://lnkd.in/gD5SYhj9 ↳ LangChain Agents: https://lnkd.in/gFSUFynP ↳ Microsoft Semantic Kernel: https://lnkd.in/gXyXykhF ↳ IBM Bee: https://lnkd.in/gsSzsgai ↳ OpenAI Agent SDK: https://lnkd.in/giafrGBX ↳ CrewAI: https://learn.crewai.com/ ↳ Pydantic Agents: https://lnkd.in/gz-egBv4 This is not an exhaustive list, but it will surely help you move faster. 📌 Start building! Begin with simple task-specific agents, then work your way up to autonomous systems that can: ↳ Chain multiple tools together ↳ Maintain long-term memory ↳ Self-improve through feedback ↳ Collaborate with other agents ↳ Generates accurate agentic responses 📌 You can also utilize a few well-known coding agents to build faster ↳ Cursor AI: https://www.cursor.com/ ↳ Windsurf: https://lnkd.in/gGMeHsm6 ↳ Claude Code: https://lnkd.in/g2pmqcUm ↳ OpenAI Codex: https://lnkd.in/gmw9mTcc 📌 Utilize protocols to scale your agentic systems ↳ MCP: https://lnkd.in/g586baCv ↳ A2A: https://lnkd.in/gT-cCbX9 ↳ ANP: https://lnkd.in/g5RnZtB9 ↳ ACP: https://lnkd.in/gfcjQnXU ↳ AGORA: https://lnkd.in/g7qvtYhP Just to name a few. Save 💾 ➞ React 👍 ➞ Share ♻️ & follow for everything related to AI Agents