Advanced AI Training

If you're feeling overwhelmed with how fast AI is evolving, you're not alone. Every day there’s a new paper, a new framework, a new agent loop, and it’s easy to feel like you’re falling behind. But the good news is that you don’t need to learn everything all at once. What you need is structure. So I put together a 10-level AI Agents Learning Roadmap that takes you from foundations to production, layering your learning in a way that’s actually doable. 💡My recommendation: spend 2–3 weeks on each level. Learn the concepts, implement small projects, and build your intuition. If you're moving faster or slower based on time or experience, that’s okay too. And when something new drops? That can be your Level 11. Don’t let “newness” derail your plan. Just start here. 👇 Here’s the roadmap: 🔖 Level 1: GenAI & Transformer Foundations Tokens, embeddings, transformers, decoding, and inference with open-weight models. 🔖 Level 2: Prompting & Language Model Behavior Prompt types (CoT, ReAct, ToT), decoding strategies, context design, and adversarial prompting. 🔖 Level 3: Retrieval-Augmented Generation (RAG) Chunking, embeddings, vector DBs, RAG pipelines, and RAG evaluation. 🔖 Level 4: LLMOps & Tools LangChain, LangGraph, Dust, CrewAI, tool use, function calling, and synthetic data. 🔖 Level 5: Agents & Agent Frameworks Agent types, memory, planning, LangChain agents, LangGraph loops, and evaluation. 🔖 Level 6: Memory, State & Orchestration Vector and symbolic memory, episodic vs persistent state, memory compression. 🔖 Level 7: Multi-Agent Systems Hub-and-spoke vs decentralized, message passing, collaborative agents, agent teams. 🔖 Level 8: Evaluation & Reinforcement Learning LLM-as-a-Judge, RLHF, RLVR, reward modeling, and self-correcting loops. 🔖 Level 9: Protocols & Safety MCP, A2A, safety alignment, guardrails, traceability, and autonomous policy updates. 🔖 Level 10: Build & Deploy FastAPI, Streamlit, GGUF, QLoRA, caching, monitoring with LangSmith, Arize, Trulens. 📌 Bookmark this. 🛠️ Build something after every level. And if you're wondering what tools to explore along the way → Start with Hugging Face (to explore LLMs and SLMs), you can use Ollama (to run SLMs on your laptop, like Phi-4, TinyLlama), or Fireworks AI (to run LLMs via endpoint, like Qwen 3, Kimi K2, DeepSeek R1), then explore LangChain & LangGraph (these two tools will teach you a lot), then you can move into learning Agentic frameworks like CrewAI, AutoGen. 💻 Pro-tip: Start with cookbooks! 〰️〰️〰️ Follow me (Aishwarya Srinivasan) for more AI insight and subscribe to my Substack to find more in-depth blogs and weekly updates in AI: https://lnkd.in/dpBNr6Jg

Many people often ask me how to learn Agentic AI and where to start. My answer keeps evolving — because the field itself is changing every few months. What I shared six months ago helped many people get started. But today, with newer frameworks, deeper integrations, and more real-world use cases, that learning path looks different. So I’ve put together this updated AI Agents Learning Map — a structured view of how I now see this space progressing. Level 1 – Foundations This is where every learner should begin. The goal is to understand how intelligent systems are built and connected. • Large Language Models – Core models that generate and understand natural language. • Embeddings and Vector Databases – Represent meaning and context for better search and reasoning. • Prompt Engineering – Techniques to guide model responses effectively. • APIs and External Data Access – Allow models to connect to external systems and data sources. At this level, focus on understanding how LLMs interact with structured and unstructured data. Level 2 – System Capabilities At this stage, models evolve into systems. You begin combining memory, context, and reasoning to build early agent behaviors. • Context Management – Managing dialogue and maintaining state across interactions. • Memory and Retrieval – Implementing persistent storage for short- and long-term information. • Function Calling and Tool Use – Letting AI take real actions beyond text generation. • Multi-step Reasoning – Enabling sequential decision-making and logical flow. • Agent Frameworks – Using orchestration tools like LangGraph, CrewAI, and Microsoft AutoGen. This level is where isolated models start becoming intelligent systems. Level 3 – Advanced Autonomy Here, agents collaborate, plan, and execute tasks independently. This is where agentic AI truly begins. • Multi-Agent Collaboration – Building systems where agents work together with defined roles. • Agentic Workflows – Structuring processes that allow autonomous execution. • Planning and Decision-Making – Defining goals, evaluating options, and acting without human prompts. • Reinforcement Learning and Fine-tuning – Improving outcomes based on feedback and experience. • Self-Learning AI – Systems that evolve continuously as they operate. At this level, AI transitions from reactive systems to proactive problem-solvers. Why this learning map matters This map is not about tools or frameworks. It’s about progression — how engineers and organizations move from using AI to building intelligence. Mastering each level leads to better design decisions, deeper understanding, and ultimately, the ability to create autonomous, adaptive systems. Where would you place your current AI understanding on this map?

Stop treating AI like a fancy calculator. Use it to lift your team, not just automate your to do list. Here are 12 ways to help others succeed at work with AI. 1. Use AI to help colleagues get a second chance ↳ Turn feedback into coaching. Use ChatGPT to reframe past mistakes into growth stories. 2. Offer better feedback with AI powered clarity ↳ Tools like Grammarly or ChatGPT can help you say “this needs work” without sounding like a Bond villain. 3. Help prep someone for an interview using AI mock questions ↳ Feed the job description into ChatGPT and get tailored practice questions. Add pressure by staring at them. 4. Use reminders and AI assistants to follow through on your promises ↳ Microsoft Copilot can remember what you’ve forgotten. Including that email from three days ago. 5. Use AI to know when to say NO ↳ Your calendar is a data source. Use AI to spot overload and prevent burnout (yours and everyone else’s). 6. Turn praise into impact using AI sentiment tools ↳ Give feedback that lands. AI can help you strike the right tone and hit ‘send’ faster. 7. Use AI to summarise meetings, so you only invite who needs to be there ↳ Record it, run it through Copilot or similar. Give people their time back. 8. Share your knowledge by building an internal AI knowledge base ↳ Upload decks, docs, FAQs. Then let people ask questions without pinging you at 9 PM. 9. Help others solve problems by creating reusable AI prompts ↳ Faced that challenge before? Share your best prompt and save someone hours. 10. Use AI to scout opportunities and share them ↳ Set up alerts or use AI to scan for job openings, speaking gigs, awards. Pass them on. 11. Automate your admin so you can focus on high impact work ↳ AI shouldn’t replace effort. Just the tedious bits. 12. Use AI to improve listening. Not replace it ↳ Meeting transcripts help you revisit what was said. But you still need to care. Being an AI enabled leader isn’t about offloading everything. It’s about using tech to amplify your impact. So you can help more people, more effectively. Success isn’t just about productivity. It’s about how many others you bring with you. 💬 Which of these are you already doing? ♻️ Repost to help more leaders lead with AI, not just around it. And follow Edward Frank Morris for more content like this!

Just co-led an AI training for 20 execs from one of the largest financial institutions in the world. We covered a lot of ground from how LLMs work to what an agent is and how to transform your work, but here were the biggest aha moments for the group. 1. Historically, leaders had to think about allocating budget for headcount and software. Now, token budget must be a serious consideration. Do you set limits per employee? How do you measure the ROI on your token budget? How much budget do you allocate to which employees? How do employees know which models to use to most efficiently spend tokens? 2. The models are good enough now, where most bad output is user error not technology error. And a major contributor to user error is bad context management. To have clean context hygiene you need to understand what the context window is, why separation of concerns is important, and how tactically to treat context like a precious resource. 3. A skill is a scary word. It’s nothing more than an SOP. If you asked an intern to write a one page document breaking down their step-by-step process for building a great deck, you’ve created a skill. A skill is a long prompt that codifies a repeatable process, can be edited as you see fit, and helps to generate more predictable output that meets your standards. 4. Saying you use AI gets you style points. But the real unlock is in process mapping. During the workshop, we asked every exec to write out one of their/their teams key processes step by step and place an E (eliminate), A (automate), or D (delegate) next to each step. The exercise not only revealed opportunities for AI, but also existing inefficiencies that have gone undetected. 5. Claude Cowork opened their eyes. Many people still see AI as a chat-based supercharged Google. They may have heard the phrase “agent,” but they assume it’s sophisticated tech reserved for engineers. Cowork is the layperson’s gateway drug to agents, showing the possibility of building web apps, ai workflows, and live artifacts in a single place. 6. Every company talks about the “bad guys”slowing down AI transformation: legal, compliance, and IT/security. One of the most powerful choices leadership can make is flipping the script and making them the heroes of the transformation story. Help them understand the risks of doing nothing, work with them to make calculated bets, and celebrate them publicly when they partner to drive transformation with speed. 7. Getting an AI system to perform requires four things to go right: picking the right model, teaching it the right process, providing the right context, and establishing the right guardrails. Most people assume the right model is 99% of it, they don’t think enough about giving the right context, and they don’t realize the right guardrails make things like hallucinations and mistakes less dangerous.

As AI workloads scale to thousands of GPUs, the constraint isn’t just compute, it’s the network. Synchronized training jobs only move as fast as their slowest path, and traditional networking approaches weren’t built for this. That’s why we worked closely with OpenAI, NVIDIA, and Arista Networks to deploy and scale Multipath Reliable Connection (MRC) across OCI’s cluster network in Abilene, Texas. MRC extends reliable connections across multiple network paths, helping improve throughput, resilience, and tail latency at scale. Paired with Oracle Acceleron’s Multiplanar Network, it helps deliver the predictable performance large-scale AI workloads require. This is what it takes to run AI systems reliably at scale. https://lnkd.in/gJKWzYmK

Researchers from Oxford University just achieved a 14% performance boost in mathematical reasoning by making LLMs work together like specialists in a company. In their new MALT (Multi-Agent LLM Training) paper, they introduced a novel approach where three specialized LLMs - a generator, verifier, and refinement model - collaborate to solve complex problems, similar to how a programmer, tester, and supervisor work together. The breakthrough lies in their training method: (1) Tree-based exploration - generating thousands of reasoning trajectories by having models interact (2) Credit attribution - identifying which model is responsible for successes or failures (3) Specialized training - using both correct and incorrect examples to train each model for its specific role Using this approach on 8B parameter models, MALT achieved relative improvements of 14% on the MATH dataset, 9% on CommonsenseQA, and 7% on GSM8K. This represents a significant step toward more efficient and capable AI systems, showing that well-coordinated smaller models can match the performance of much larger ones. Paper https://lnkd.in/g6ag9rP4 — Join thousands of world-class researchers and engineers from Google, Stanford, OpenAI, and Meta staying ahead on AI http://aitidbits.ai

A clear path into AI engineering using 10 GitHub repos Step-by-step plan you can follow and show as proof of work Foundations 1. Learn the basics of machine learning and deep learning • ML for Beginners, AI for Beginners Output: 3 small projects with short READMEs that explain the goal, data, and result. Go deeper 2) Build neural nets from scratch • Neural Networks: Zero to Hero Output: a tiny GPT trained on a toy dataset, plus notes on what you changed and why. Read papers in code 3) Study real architectures by walking through annotated implementations • DL Paper Implementations Output: pick one model and re-implement a minimal version. Write what you simplified. Ship real software 4) Move from notebooks to apps and services • Made With ML Output: refactor one project with a simple API, tests, and a one-click run script. Work with LLMs 5) Learn the core pieces end to end • Hands-on LLMs Output: a basic RAG app (retrieval augmented generation) that answers questions on a small knowledge base. Make RAG better 6) Compare advanced techniques • Advanced RAG Techniques Output: run A/B tests on 3 settings and report latency, accuracy, and cost in a table. Learn agents 7) Build simple agents that take steps toward a goal • AI Agents for Beginners Output: an agent that checks a site, writes a summary, and files a ticket. Take agents toward production 8) Add memory, orchestration, and basic security • Agents Towards Production Output: logging, retry logic, and input checks. Note what fails and how you fixed it. Round out your portfolio 9) Adapt working examples • AI Engineering Hub Output: 2 more apps that solve real tasks, each with a clear demo and setup guide. How to pace this • One repo per week is a good rhythm. • Keep a single repo called “ai-engineering-journey” with subfolders per step. • After each step, post a short write-up with a 30-second screen recording. What hiring managers look for • Working code that runs on first try. • Clear README, data source, and limits. • Small tests and a simple eval, even if manual. • Changelog that shows steady progress. Save this and start with step 1 today. Repos and links 1. ML for Beginners — https://lnkd.in/dQ6nAJRC 2. AI for Beginners — https://lnkd.in/dXwJJjMm 3. Neural Networks: Zero to Hero — https://lnkd.in/dagQ3kmA 4. DL Paper Implementations — https://lnkd.in/dyw54m73 5. Made With ML — https://lnkd.in/duHjr2CY 6. Hands-On Large Language Models — https://lnkd.in/dxEGzsgc 7. Advanced RAG Techniques — https://lnkd.in/dd2TKA5P 8. AI Agents for Beginners — https://lnkd.in/deznrHdf 9. Agents Towards Production — https://lnkd.in/dz-WgU-3 10. AI Engineering Hub — https://lnkd.in/d9cNqy7c

🤖 𝗔𝗜 𝗶𝗻 𝗗𝗶𝗴𝗶𝘁𝗮𝗹 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁: 𝗵𝘆𝗽𝗲, 𝗵𝗲𝗹𝗽 — 𝗼𝗿 𝗯𝗼𝘁𝗵? AI training tool ads pop up in my LinkedIn feed daily. eLearnings done in minutes! Content created with a finger tip! Super fast, super creative… Our Infineon Technologies internal Digital Learning Solutions team in Porto (30 ‘human’ experts!) ran a pilot using current AI tools to create training videos. Here’s what we found: ✅ AI speeds up repetitive tasks such as audio synchronization, implementation of adjustments, creation of captions and helps generate quick, standardized content. ❌ When it comes to customization, creativity, and complex topics, including compliance and data sensitive contents, the results fall short. For training videos longer than three minutes, we found that the visuals and language become repetitive and generic. 🔑 The real value? Using AI selectively. For short, “commodity” trainings: yes. It’s a true game changer! For anything with broader interactivity, depth, need for internal context and culture ‘fit’: human expertise remains key. But it’s not either or – we consult our customers on both: pros and cons of using AI and discuss suitable use cases, when designing content. 📢 I’m curious: Have you tested AI in training development? What worked for you — and what didn’t? Julia Tronsberg António Sarmento Dias Liliana Pereira Jorge Botelho #digitallearning #learningsolutions #training #elearning #ai

AI mastery isn’t about learning everything. It’s about knowing what to learn next. Jumping into advanced models without foundations slows you down. Staying in basics too long keeps you stuck. The real progress comes from moving through the right layers at the right time. That’s what separates experimentation from mastery. Here’s a complete roadmap to mastering AI in 2026 - Foundations Start with Python, data structures, math, and statistics to build real understanding. - Machine learning loop Learn core ML concepts, evaluation techniques, and how to iterate on models. - Deep learning Understand neural networks, CNNs, RNNs, transformers, and modern architectures. - Generative AI Work with LLMs, prompt engineering, RAG, embeddings, and multimodal systems. - Applied AI Build real use cases across domains like NLP, vision, recommendation systems, and forecasting. - Tooling and deployment Move models to production with MLOps, APIs, cloud deployment, and monitoring. - Ethics and safety Design systems that are fair, explainable, secure, and aligned with regulations. - Career and ecosystem Turn skills into impact through projects, open source, portfolios, and real opportunities. AI isn’t one skill. It’s a stack. And each layer unlocks the next. Skip layers, and things don’t work. Build them right, and everything compounds. Where are you currently in this roadmap?