Robert Tadjer’s Post

How can organisations improve the reliability of their AI platforms? Why context engineering could help supply platforms with the right information in the right way, delivering better results. Read more: https://lnkd.in/enAVbTma Partner Content by Moody's #AI #bankingtransformation #innovation Moody's Analytics

Will context define the next era of intelligent systems? AI platforms are only as accurate as the data they’re given. But context engineering could help platforms sort data and deliver at scale. Read more: https://lnkd.in/enAVbTma Partner Content by Moody's #AI #bankingtransformation #innovation Moody's Analytics

RAG is a more cost-effective approach to introducing new data to the LLM. It makes generative artificial intelligence (generative AI) technology more broadly accessible and usable. #RAGModel #GenerativeAI #LLMmodel #contentbased #AugumentedGenerativeAI

Agentic AI has already surpassed implementations leveraging large language models (LLM) plus retrieval-augmented generation (RAG). To reach the full capacity of Agentic AI in overseeing warehouse inventories or even altering supply chain levels autonomously, a real-time, detailed stream of information is essential. Edward Funnekotter https://lnkd.in/e9GZXvhB

A must-read for anyone building the next wave of intelligent systems. Great insights from InfoQ latest AI, ML & Data Engineering Trends Report 2025. The report highlights a key shift in the AI landscape. It’s no longer just about building bigger models, but about creating stronger data pipelines that connect structure, context, and meaning. We see this evolution as the foundation of creative intelligence. AI and ML models need more than visual data; they need metadata that helps them understand composition, context, and intent. With over 232 million rights-cleared images, videos, and vectors enriched with structured metadata from a global creator community, 123RF is helping businesses train AI that truly learns. Through our Content Licensing and AI Data Solutions, we provide datasets built for accuracy, scale, and ethical clarity. The future of AI is not just about generation, it’s about creation that’s meaningful, responsible, and intelligently powered by quality data. 🔗 Read the full report on InfoQ: https://lnkd.in/dRSW8rpJ #AI #MachineLearning #DataEngineering #123RFAIML #AIML #InfoQ

GenAI vs AI Agents vs Agentic AI vs ML vs Data Science vs LLM AI has many layers, from data science foundations to intelligent, autonomous systems. Each concept plays a unique role in shaping today’s intelligent technology stack. Let’s break down how these six AI domains connect yet differ at their core : 1. Generative AI – Core Concepts -Focuses on creating new content - text, images, music, or video. -It uses diffusion models, GANs, and transformers to generate outputs from patterns it learns. -Think ChatGPT, Midjourney, or Runway - all powered by creative generation. 2. AI Agents – Core Concepts -AI Agents act autonomously, performing tasks and making decisions. -They use context, reasoning, and environment interaction to execute workflows. -These agents can use APIs, tools, and feedback loops to reach goals intelligently. 3. Agentic AI – Core Concepts -Takes AI agents to the next level — self-improving, reasoning, and planning systems. -It introduces chain-of-thought reasoning, self-reflection, and multi-agent collaboration. -Agentic AI focuses on autonomy, feedback, and human-in-the-loop alignment. 3. Machine Learning – Core Concepts -ML trains models to learn patterns from data and make predictions. -It involves supervised, unsupervised, and reinforcement learning, powered by algorithms like regression and clustering. -The focus: accuracy, feature engineering, and model optimization. 4. Data Science – Core Concepts -The backbone of AI - focused on data collection, analysis, and visualization. -It combines statistics, hypothesis testing, and data ethics to extract insights. -Data science powers every stage — from data cleaning to predictive analytics. 5. Large Language Models (LLMs) – Core Concepts -LLMs are language-based neural networks trained on massive text datasets. -They use transformers, embeddings, and attention mechanisms to understand and generate language. -LLMs like GPT and Gemini form the core engine of today’s AI assistants. In Summary: - Data Science → builds the data foundation. - Machine Learning → finds patterns. - LLMs & GenAI → create outputs. - AI Agents & Agentic AI → take intelligent action. Together, they form the complete AI ecosystem driving automation and intelligence today.

GenAI vs AI Agents vs Agentic AI vs ML vs Data Science vs LLM — What’s the difference? AI isn’t one thing. It’s a full stack — from data to reasoning. Let’s simplify 👇 Data Science (The Analyst) → Collects, cleans & interprets data to extract insights. It’s the foundation of all intelligent systems. Machine Learning (The Learner) → Trains models to find patterns & make predictions. The engine behind AI accuracy. Large Language Models (The Brain) → Understand & generate human language using transformers. Think GPT, Gemini — the core of modern assistants. Generative AI (The Creator) → Uses LLMs & diffusion models to create new text, images, or videos. The creative layer — ChatGPT, Midjourney, Runway. AI Agents (The Automator) → Take action using APIs, context & workflows. They do, not just talk. Agentic AI (The Thinker) → Plans, reasons & self-improves with multi-agent collaboration. They think and act autonomously. In short: Data Science → builds the base ML → learns LLMs & GenAI → create AI Agents & Agentic AI → act intelligently The future isn’t one AI — it’s the stack working together. 👏 👏 👏

GenAI vs AI Agents vs Agentic AI vs ML vs Data Science vs LLM AI has many layers, from data science foundations to intelligent, autonomous systems. Each concept plays a unique role in shaping today’s intelligent technology stack. Let’s break down how these six AI domains connect yet differ at their core : 1. Generative AI – Core Concepts -Focuses on creating new content - text, images, music, or video. -It uses diffusion models, GANs, and transformers to generate outputs from patterns it learns. -Think ChatGPT, Midjourney, or Runway - all powered by creative generation. 2. AI Agents – Core Concepts -AI Agents act autonomously, performing tasks and making decisions. -They use context, reasoning, and environment interaction to execute workflows. -These agents can use APIs, tools, and feedback loops to reach goals intelligently. 3. Agentic AI – Core Concepts -Takes AI agents to the next level — self-improving, reasoning, and planning systems. -It introduces chain-of-thought reasoning, self-reflection, and multi-agent collaboration. -Agentic AI focuses on autonomy, feedback, and human-in-the-loop alignment. 3. Machine Learning – Core Concepts -ML trains models to learn patterns from data and make predictions. -It involves supervised, unsupervised, and reinforcement learning, powered by algorithms like regression and clustering. -The focus: accuracy, feature engineering, and model optimization. 4. Data Science – Core Concepts -The backbone of AI - focused on data collection, analysis, and visualization. -It combines statistics, hypothesis testing, and data ethics to extract insights. -Data science powers every stage — from data cleaning to predictive analytics. 5. Large Language Models (LLMs) – Core Concepts -LLMs are language-based neural networks trained on massive text datasets. -They use transformers, embeddings, and attention mechanisms to understand and generate language. -LLMs like GPT and Gemini form the core engine of today’s AI assistants. In Summary: - Data Science → builds the data foundation. - Machine Learning → finds patterns. - LLMs & GenAI → create outputs. - AI Agents & Agentic AI → take intelligent action. Together, they form the complete AI ecosystem driving automation and intelligence today.

🛑 The AI Workslop Problem is the "One-Size-Fits-All" Mentality 🛑 The promise of AI is brilliance, but the reality for many businesses is AI 'workslop' Why? Because we're trying to use Jack-of-All-Trades Large Language Models (LLMs) for a Master's job General-purpose LLMs are amazing tools, but their broad, multi-billion parameter scope often results in: 🚫 High Inference Costs for repetitive, simple tasks 🚫 Latency issues when scaling real-time applications 🚫 Context-switching errors and 'hallucinations' when deep domain expertise is required So what's the key to efficient, high-quality AI? It's not to train a new model from scratch, but to leverage the broad foundation of a powerful LLM and hyper-specialize it with fine-tuning Think of a general LLM as a brilliant, well-read college graduate 🫡 Amazing to have as an intern, but probably not who you want making actual decisions Whereas a fine-tuned LLM...well that's like having a PhD on call with real world experience 🤓 So why is fine-tuning LLMs the Antidote to Workslop? ✨️ Precision on Unstructured Data Fine-tuning on a small, high-quality dataset of your industries unique documents (think invoices, legal contracts, or medical paperwork) fundamentally adjusts the model's weights This allows it to develop deeper "muscle memory" for tasks like extracting intricate line-item details or classifying industry-specific terminology with far higher accuracy and consistency than a general model ✨️ Consistency & Format Mastery For automation, the model needs to be reliable A fine tuned LLM is the best way to enforce strict output formats (like perfect JSON or YAML) and a consistent tone—eliminating the randomness that plagues "one-size-fits-all" prompting ✨️ Efficiency & Cost Reduction Techniques like Parameter-Efficient Fine-Tuning (PEFT) allow you to inject specialization with minimal data and compute Crucially, a fine-tuned model requires shorter, simpler prompts in production, leading to lower token usage, faster response times, and significant cost savings at scale We need to stop forcing a general-purpose model to be a surgical specialist To remove the workslop of the AI era, it’s time to move beyond the "One-Size-Fits-All" LLM mentality and strategically embrace fine-tuning to create the specialized AI systems our complex data demands #FineTuning #GenerativeAI #DigitalTransformation #Workslop #DataStrategy

Three years ago, I predicted AI would be able to write an entire IC memo by itself. I now know it can, but I also now know it shouldn't. Why? Because having AI write the memo alone (or any meaningful report) misses the point. Grasping this concept is key to understanding how AI will impact us as a society and what life will look like on the other side of the trough of disillusionment. Now, let's back to why the process is more important than the paper. A memo is much more than a report. A memo is just as much about what didn't make the page as it is about what did. A memo is about the person who didn't like how they wrote the last memo and is determined to make it better this time. A memo is about people in a process, perhaps with some automation, but even more importantly, with accountability for 👏EVERY, 👏SINGLE, 👏WORD. I made a video yesterday where I used Engramic to write an entire investment committee memo. This is a huge milestone for me, and while this memo wouldn't come close to being a memo I would submit, it demonstrates a process of using AI to write a memo that I would. Here are the features that make it possible: 1. Thinking Loop. The process is more about human thinking than AI thinking. Thinking loops help you build thoughts on top of previous thoughts and layers of intelligence. 2. Validation. Saving the outcome of a thinking loop involves human validation. AI is far from perfect and since we can't hold AI accountable, the orchestrator of the AI is the responsible party. Your username is included in every artifact you build and share, it's a breadcrumb to track lazy use of AI. 3. Learning & Mentoring. Along the way, you should use automation tools for low value tasks, but you should also use the tools for self-assessment, mentor markup, flash cards, or meeting prep, to solidify your understanding. I believe we are building people, not just memos. 4. Technical Superiority. Engramic is a professional tool and you do get better as you use it. There is less abstraction between you and the AI. That's because providing the LLM with the right information takes skill, the type of skill that semantic search can't deliver. This precision yields superior results from LLMs when compared to semantic search (vector DB, lexical, graph, and reranking). It's a proud day for me. Being able to write a full IC memo was a huge goal. Understanding what's important in the process is the beautiful side effect of jumping on this crazy journey (do you see the parallel?). I hope you take some time to review the video and see a glimpse of where knowledge work is heading.