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?

My biggest takeaways from Fei-Fei Li: 1. Just nine years ago, calling yourself an AI company was considered bad for business. Nobody believed the technology would work back in 2016. By 2017, companies started embracing the term. Today, virtually every company calls itself an AI company. 2. The modern AI revolution started with a simple but overlooked insight from Fei Fei: AI models needed large amounts of labeled data. While researchers focused on sophisticated mathematical models and algorithms, she realized the missing ingredient was data. Her team spent three years working with tens of thousands of people across more than 100 countries to label 15 million images, creating ImageNet. This dataset became the foundation for today’s AI systems. 3. The human brain’s efficiency vastly exceeds current AI systems. Humans operate on about 20 watts of power—less than any lightbulb—yet accomplish tasks that require AI systems to use massive computing resources. Current AI still can’t do things elementary school children find easy. 4. Simply scaling current approaches won’t be enough. While adding more data, computing power, and bigger models will continue advancing AI, fundamental innovations are still needed. Throughout AI history, simpler approaches combined with enormous datasets consistently outperformed sophisticated algorithms with limited data. 5. Breakthrough technologies often start as toys or fun experiments before changing the world. ChatGPT was tweeted by Sam Altman as “Here’s a cool thing we’re playing with” and became the fastest-growing product in history. What seems like play today might transform civilization tomorrow. 6. Spatial intelligence is as crucial as language for real-world applications. In emergency situations like fires or natural disasters, first responders organize rescue efforts through spatial awareness, movement coordination, and understanding physical environments—not primarily through language. This is why world models that understand three-dimensional space represent the next frontier beyond text-based chatbots. 7. Physical robots face much harder challenges than self-driving cars, which took 20 years from prototype to street deployment and still aren’t finished. Self-driving cars are metal boxes moving on flat surfaces, trying not to touch anything. Robots are three-dimensional objects moving in three-dimensional spaces, specifically trying to touch and manipulate things. This makes robotics far harder than creating chatbots. 8. Everyone has a role in AI’s future, regardless of profession. Whether you’re an artist using AI tools to tell unique stories, a farmer participating in community decisions about AI deployment, or a nurse who could benefit from AI assistance in an overworked health-care system, you can and should engage with this technology. AI should augment human dignity and agency, not replace it—which means both using AI as a tool and having a voice in how it’s governed.

Common Sense Media recently released a comprehensive risk assessment of AI teacher assistants/lesson planning tools. Their findings reveal that while these tools promise increased productivity and creative support, they're also creating "invisible influencers" that could fundamentally undermine educational quality. Unlike GenAI foundation model chatbots, these tools are specifically designed for instructional planning and classroom use and are rapidly being adopted across districts. Key Concerns from their report: • "Invisible Influencers" in Student Learning: AI-generated content directly shapes what students learn through potentially biased perspectives and historical inaccuracies that teachers may miss; evidence also shows these tools suggest different approaches and responses based on student race/gender • “Outsourced Thinking" Problem: Tools make it dangerously easy to push unreviewed AI instructional content straight to classrooms, while novice teachers lack experience to spot subtle errors and biasses • High-Stakes Outputs: IEP and behavior plan generators create official-looking documents that could impact student educational trajectories even though these plans should be human-generated (and in the case of IEP goals are mandated to be human generated) • Undermining High-Quality Instructional Materials: Without proper integration, these tools fragment learning and can undermine coherent, research-backed curricula Recommendations from the report: • Experienced educator oversight required for all AI-generated educational content • Clear district policies and guidelines for AI teacher assistant implementation • Integration with existing high-quality curricula rather than replacement of established materials • Robust teacher training on identifying bias and evaluating AI outputs • Careful oversight of real-time AI feedback tools that interact directly with students We'd also recommend foundational AI literacy for teachers before they begin using GenAI teacher assistants, so that they are aware of the potential limitations. While AI teacher assistants aren't inherently problematic, they require the same careful implementation and oversight we'd expect for any tool that directly impacts student learning. The potential for enhanced productivity is real, but so are the risks to educational equity and quality. This report underscores the urgent need for GenAI EdTech tool makers to provide evidence of how their tools mitigate these issues along with evidence-based policies and professional development to help educators navigate AI tools responsibly. All of which underline how important AI Literacy is for the 2025-2026 school year. Link in the comments to check out the full report. Also check out our 5 Questions to Ask GenAI EdTech Providers resource in the comments if you are planning to implement any of these tools in your school or district. #AIinEducation #ailiteracy #Education #K12 AI for Education

This Stanford study examined how six major AI companies (Anthropic, OpenAI, Google, Meta, Microsoft, and Amazon) handle user data from chatbot conversations.  Here are the main privacy concerns. 👀 All six companies use chat data for training by default, though some allow opt-out 👀 Data retention is often indefinite, with personal information stored long-term 👀 Cross-platform data merging occurs at multi-product companies (Google, Meta, Microsoft, Amazon) 👀 Children's data is handled inconsistently, with most companies not adequately protecting minors 👀 Limited transparency in privacy policies, which are complex and hard to understand and often lack crucial details about actual practices Practical Takeaways for Acceptable Use Policy and Training for nonprofits in using generative AI: ✅ Assume anything you share will be used for training - sensitive information, uploaded files, health details, biometric data, etc. ✅ Opt out when possible - proactively disable data collection for training (Meta is the one where you cannot) ✅ Information cascades through ecosystems - your inputs can lead to inferences that affect ads, recommendations, and potentially insurance or other third parties ✅ Special concern for children's data - age verification and consent protections are inconsistent Some questions to consider in acceptable use policies and to incorporate in any training. ❓ What types of sensitive information might your nonprofit staff  share with generative AI?  ❓ Does your nonprofit currently specifically identify what is considered “sensitive information” (beyond PID) and should not be shared with GenerativeAI ? Is this incorporated into training? ❓ Are you working with children, people with health conditions, or others whose data could be particularly harmful if leaked or misused? ❓ What would be the consequences if sensitive information or strategic organizational data ended up being used to train AI models? How might this affect trust, compliance, or your mission? How is this communicated in training and policy? Across the board, the Stanford research points that developers’ privacy policies lack essential information about their practices. They recommend policymakers and developers address data privacy challenges posed by LLM-powered chatbots through comprehensive federal privacy regulation, affirmative opt-in for model training, and filtering personal information from chat inputs by default. “We need to promote innovation in privacy-preserving AI, so that user privacy isn’t an afterthought." How are you advocating for privacy-preserving AI? How are you educating your staff to navigate this challenge? https://lnkd.in/g3RmbEwD

One-pixel attacks are a specific type of adversarial attack where a neural network's prediction can be drastically altered by changing just one pixel in the image. I came across a post where the Author didn’t share solution on how to handle this thought to share my take and learning on this. This kind of attack highlights the vulnerabilities in neural networks where a seemingly insignificant modification can lead to major misclassifications, such as an image of a frog being predicted as a cat with high confidence. To mitigate the effects of one-pixel attacks, one promising approach involves increasing the neural network's bias towards recognizing shapes rather than textures. A paper titled "ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness" explores this idea. The study found that CNNs trained on ImageNet tend to rely heavily on textures for object recognition, unlike humans who focus more on shapes. By training CNNs on a modified version of ImageNet called Stylized-ImageNet, which emphasizes shape over texture, the networks can be made more robust against adversarial attacks. This approach shifts the neural network's focus from texture-based to shape-based recognition, thereby reducing the impact of texture-specific adversarial modifications, such as one-pixel attacks. This shift not only improves the network's robustness but also aligns its behavior more closely with human perception, resulting in better overall performance and resilience against a variety of image distortions. In summary, combating one-pixel attacks involves leveraging techniques like style transfer to enhance shape bias in CNNs, thereby minimizing the effect of texture-specific adversarial modifications and improving the network's robustness and accuracy.

When I have a conversation about AI with a layperson, reactions range from apocalyptic fears to unrestrained enthusiasm. Similarly, with the topic of whether to use synthetic data in corporate settings, perspectives among leaders vary widely. We're all cognizant that AI systems rely fundamentally on data. While most organizations possess vast data repositories, the challenge often lies in the quality rather than the quantity. A foundational data estate is a 21st century competitive advantage, and synthetic data has emerged as an increasingly compelling solution to address data quality in that estate. However, it raises another question. Can I trust synthetic more or less than experiential data? Inconveniently, it depends on context. High-quality data is accurate, complete, and relevant to the purpose for which its being used. Synthetic data can be generated to meet these criteria, but it must be done carefully to avoid introducing biases or inaccuracies, both of which are likely to occur to some measure in experiential data. Bottom line, there is no inherent hierarchical advantage between experiential data (what we might call natural data) and synthetic data—there are simply different characteristics and applications. What proves most trustworthy depends entirely on the specific context and intended purpose. I believe both forms of data deliver optimal value when employed with clarity about desired outcomes. Models trained on high-quality data deliver more reliable judgments on high impact topics like credit worthiness, healthcare treatments, and employment opportunities, thereby strengthening an organization's regulatory, reputational, and financial standing. For instance, in a recent visit a customer was grappling with a relatively modest dataset. They wanted to discern meaningful patterns within their limited data, concerned that an underrepresented data attribute or pattern might be critical to their analysis. A reasonable way of revealing potential patterns is to augment their dataset synthetically. The data set would maintain statistical integrity (the synthetic mimics the statistical properties and relationships of the original data) allowing any obscure patterns to emerge with clarity. We’re finding this method particularly useful for preserving privacy, identifying rare diseases or detecting sophisticated fraud. As we continue to proliferate AI across sectors, senior leaders must know it's not all "upside." Proper oversight mechanisms to verify that synthetic data accurately represents real-world conditions without introducing new distortions is a must. However, when approached with "responsible innovation" in mind, synthetic data offers a powerful tool for enabling organizations to augment limited datasets, test for bias, and enhance privacy protections, making synthetic data a competitive differentiator. #TrustworthyAI #ResponsibleInnovation #SyntheticData

🚨 OpenAI and most AI companies ignore privacy by design. Here's what you can do to stay as private as possible while using AI: Something that's been clear since the beginning of the generative AI wave is that AI companies like OpenAI expressly ignore privacy by design. They often build AI tools and features that can put users’ privacy at risk. I wrote back in April 2023 that OpenAI chose not to implement privacy by design; instead, they followed a Privacy-by-Pressure approach. Two years have passed, and this, sadly, remains true. In today's edition, I highlight essential privacy settings in general-purpose AI systems like ChatGPT, which companies often obfuscate, and most people are unaware of. I also discuss privacy-preserving behavior while using AI, some new privacy-invasive capabilities and trends everyone should be aware of, and my thoughts on the future of privacy UX and responsible AI design. *A reminder: staying as private as possible while using AI requires multiple steps and sometimes behavioral changes. Why? 1. Privacy-invasive features are often ON by default. 2. The design of AI systems is usually not optimized for privacy protection, transparency, or awareness. Opting Out of AI Training Most data used to train AI comes from web scraping, where no express user consent is involved. With that in mind, if you post personal or sensitive content online, I recommend checking their AI policies and considering opting out of AI training. Unfortunately, it's becoming harder to opt out. As an example, last week, Meta announced they will also use data from EU-based users to train AI. Even though these users are covered by the GDPR, the announcement doesn't mention opt out. Turning Off Model Training Many people don't realize that while using AI systems like AI chatbots, model training is often “ON” by default. This means that all the data you input (your prompts) is used to train the underlying AI model. Since most people will likely forget to be extremely cautious with the information they input into the system, I generally recommend deactivating model training. Deactivating Memory A feature recently introduced by OpenAI, now copied by xAI and others, is “memory.” When enabled, the AI chatbot will “remember” past conversations to make future interactions more personalized. Given the privacy risks of allowing an automated system to store large amounts of personal data (including the potential for data leakage or adversarial techniques), I recommend deactivating the memory feature as well. Privacy Tricks Even if you have turned off model training, memory, and other privacy-invasive features, privacy-aware behavior while using AI remains essential, but most people fail at it. Why? (...) 👉 Continue reading my article & join our 59,000+ strong AI governance community here: https://lnkd.in/e7eQkfC7 #AI #AIGovernance #PrivacyByDesign #Privacy #OpenAI

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.

For years, AI progress has been measured by size: more parameters, more data, more compute. However, we have started to see a trend towards Small Language Models as the costs of scaling become apparent. With the web increasingly saturated by bot-generated content, everyone is searching for innovative ways to access quality data.   In today’s AI Atlas, I revisit a particularly interesting example of this shift with Microsoft’s Phi series. Rather than relying on massive, unfocused datasets, their newest model Phi-4 is trained on carefully curated and synthetic data designed to strengthen reasoning. This approach shows how smaller, more efficient models can achieve impressive performance without the heavy infrastructure costs inherent to larger counterparts.   There is a clear lesson here for enterprise leaders. The future of AI is not being defined solely by size, but by strategy. Models like Phi-4 continue to highlight how targeted, high-quality training can unlock specialized capabilities that are cost-effective and practical to deploy and are more aligned with business needs.