Building Trust in Large Language Models: Key to Responsible AI

🚀 5 Common LLM Parameters You Should Know! 🤖 As AI continues to revolutionize industries, understanding how to fine-tune Large Language Models (LLMs) has become a valuable skill. Here are the 🔑 parameters that shape how these models think and respond: 🔥 Temperature – Controls creativity and randomness in responses. 🎯 Top-P (Nucleus Sampling) – Determines how focused or diverse the output should be. 🧠 Max Completion Tokens – Sets the maximum length of generated responses. ✨ Presence Penalty – Encourages the model to introduce new topics and ideas. 🔁 Frequency Penalty – Prevents repetition for more natural and engaging replies. Mastering these parameters helps you craft more precise, creative, and human-like AI outputs — whether it’s for chatbots, content generation, or research. 🌐💡 #AI #MachineLearning #LLM #DataScience #ArtificialIntelligence #PromptEngineering #OpenAI #TechInnovation

🚀 Class Overview: Demystifying Large Language Models (LLMs) & Prompt Engineering I recently conducted an insightful session covering the essentials of Large Language Models (LLMs) and how to interact with them effectively. Here’s a quick summary: ✨ Topics Covered: 1️⃣ What is an LLM? Large Language Models are AI systems trained on massive amounts of text data to understand and generate human-like language. They power chatbots, AI writing assistants, and more! 2️⃣ Prompt & Context Engineering: Techniques to communicate effectively with LLMs. Crafting clear prompts and providing context ensures accurate, relevant, and creative outputs. 3️⃣ Key Parameters in LLMs: Temperature: Controls randomness. Lower values → more predictable, higher → more creative. Top-p (Nucleus Sampling): Chooses from the top probability mass of words to generate responses. Top-k: Limits the model to the top-k probable words for each step of output. 💡 Understanding these fundamentals can help you leverage AI more efficiently, whether for content generation, problem-solving, or AI-driven applications. 📌 Takeaway: LLMs are powerful, but how you interact with them—through well-crafted prompts and the right settings—makes all the difference!

🚫 Stop calling Large Language Models “AI.” It’s time to see them for what they truly are: next-token prediction systems not intelligence. Let’s put this in perspective: - They sense beautifully : they read and absorb language. - They predict skillfully : they estimate what comes next. - But they struggle to decide and act : real intelligence involves reasoning, purpose, and action. Here’s the trap we’re falling into: They mirror patterns in our data, not thoughts in our minds. Fluency doesn’t equal truth. Confidence doesn’t equal reliability. And bigger doesn’t mean smarter. What this view overshadows are the key ingredients of actual intelligence: - Symbolic reasoning and structure - Understanding causality the “why” behind things - Decision-making under real constraints - Embodied cognition and grounded action - Ethics, interpretability, and transparency 🧠 A practical framework to think about intelligence: Sense → Predict → Decide → Act LLMs excel at the first two. The rest require other tools. So rather than making them the entire orchestra, let’s make them great instruments paired with: - Retrieval systems for trusted facts - Knowledge graphs for structure and logic - Optimization modules for guarantees and consistency - Evaluation pipelines for reality checks ✅ Staying balanced means: - Start from real-world constraints (accuracy, safety, cost, speed) - Store truth in data, not prompts - Compose solutions (LLMs + retrieval + logic > LLM alone) - Prefer smaller, more reliable models where possible - Measure not just fluency but real-world impact LLMs transformed how humans interact with computers but remember: the browser isn’t the internet. If we keep calling LLMs “AI,” we risk confusing fluency with intelligence. Let’s zoom out connect language with reasoning, cause with effect, and style with substance. #AI #LLM #MachineLearning #TechLeadership #DigitalTransformation #EthicalAI #ModelArchitecture #TechStrategy #Intelligence

From Language Models to Behavior Models When people think of “Large Language Models,” they imagine systems that predict text. But having worked directly on RLHF (Reinforcement Learning from Human Feedback) and fine-tuning processes for several of today’s leading models, I see something different. These systems no longer just model language — they model behavior. Early models were probability engines. They extended a sequence based on likelihood, with no awareness of purpose or quality. The art was in the prompt: you had to engineer every nuance to get the right output. That changed with human feedback. RLHF introduced a new objective: not to predict the next word, but to produce the most suitable response. Models began learning from preference, context, and alignment — not just data. Today’s systems are no longer optimizing for linguistic accuracy but for response quality. They still learn from text, but what they perform is a form of behavior: interpreting, reasoning, assisting. The base model learns how to write. The fine-tuned model learns how to act. This shift — from predicting text to shaping interaction — is the direct result of years spent refining reward models and translating human judgment into machine alignment. We started by modeling words. We ended up modeling how to respond. 💪 #AI #RLHF #MachineLearning #LanguageModels #AIAlignment #ArtificialIntelligence #FineTuning #BehavioralAI

A recent study by experts from three US universities reveals that large language models (LLMs) suffer a significant decline in reasoning abilities—up to 25%—when trained continuously on unvetted, low-quality data from the web. Contrary to the common belief that ongoing training enhances capabilities, exposure to junk content can cause lasting cognitive damage and increase harmful traits like psychopathy and narcissism within these models. Industry leaders emphasize the need for careful data curation and quality control to prevent such harms. Srikanth Velamakanni, CEO of Fractal.ai, highlights that more data is not always better; instead, focusing on high-quality, curated data after achieving foundational world knowledge is crucial for improving model health and problem-solving capacity. Kanika Rajput, AI entrepreneur and researcher, warns of a risk called "epistemic collapse," where models increasingly rely on synthetic or low-quality data, drifting away from empirical truth and becoming superficially coherent but unreliable. The article also underscores the importance of human-curated data and ongoing interaction with real-world inputs to avoid the development of an "intelligent yet hollow AI monoculture." Companies like OpenAI are beginning to explore data licensing partnerships, such as with Reddit, though the industry lacks consensus on frameworks for these arrangements and how to assess the value of licensed data. The evolving nature of this space signals ongoing challenges for responsibly managing LLM training data. #AI #India https://lnkd.in/gPrWvGbs

🚀 5 Fascinating Facts About #LLMs (Large Language Models) 🧠 1. They don’t know — they predict LLMs don’t store knowledge like humans. They predict the next word using statistical patterns from trillions of tokens. It’s not intelligence in the human sense - it’s pattern precision at scale. ✈️ 2. Training them can emit as much CO₂ as 125 NYC-to-Beijing flights Training one GPT-3–sized model costs millions of dollars and enormous compute energy. It’s a reminder that AI progress = infrastructure + sustainability. 💬 3. Your chat history is their secret weapon Every prompt, correction, and upvote helps fine-tune future generations of LLMs. The “human-in-the-loop” feedback is what transforms them from random text generators into reasoning systems. 🌍 4. They’re surprisingly multilingual Even without explicit language training, LLMs can translate and reason across 50+ languages — an emergent capability that shows how deeply they understand semantic relationships. 💡5. #Tokens are their currency. Every word you type = multiple tokens. For example, "Artificial Intelligence" = 3 tokens. → #Tokenization decides both cost and #context length ⚙️ Wrapping up We’re at the crossroads where prediction meets perception — and it’s transforming into #Agentic AI, where models can not just answer, but act. If you’re exploring LLMs or building agentic systems, remember — “Master the #prompts. Understand the patterns. Build the #agents.” Question for you: 👉 What’s the most surprising thing you’ve learned about LLMs so far? #LLMs #AgenticAI #ArtificialIntelligence #CloudComputing #TechLeadership #MachineLearning #AITrends

Evaluating large language models is critical to building reliable AI systems This post outlines practical ways to measure model quality and safety, and how Mosaic AI Agent Evaluation helps teams track and improve performance from development to production

Evaluating large language models is critical to building reliable AI systems This post outlines practical ways to measure model quality and safety, and how Mosaic AI Agent Evaluation helps teams track and improve performance from development to production

Small Language Models and the Future of Agentic AI I recently came across a fascinating paper titled “Small Language Models Are the Future of Agentic AI” (arXiv link here). Small Language Models (SLMs) are models that can run efficiently on a laptop or within an organization’s own infrastructure. The paper makes the point that they may be better suited for many Agentic or Retrieval-Augmented Generation (RAG) systems. In many use cases, these systems perform repetitive processes or analyze proprietary, internal data. They don’t require the vast general knowledge or scale of a full Large Language Model (LLM). What is needed by these systems is contextual understanding and the ability to respond to the prompts submitted as they already have the data that they need to operate. SLMs can offer more cost effective solutions with easier deployments and should be able to address the majority of the security, confidentiality and data sovereignty issues that an LLM based solution cannot so easily handle, something that may well be attractive for SMEs or even departments in larger organisation that are looking to deploy solutions that are tailored just to them. It is also worthy to note that for organizations in regulated markets, handling personal or sensitive data, or working with proprietary information, SLMs may well prove to be a smarter and safer option than relying on external LLM APIs. The AI industry continues to evolve at pace, and I believe SLMs will play a critical role in the next phase of responsible and practical AI adoption. What do you think — are Small Language Models the way forward for Agentic and RAG solutions? If so, what market segment do you feel that would best play within ? #ArtificialIntelligence #SmallLanguageModels #AI #AgenticAI #RAG #LLMs #EnterpriseAI #AIDeployment #AIEthics #DataSovereignty #MachineLearning #Innovation

"AI sovereignty... requires clear framework conditions and strategies for using AI in an institution. This includes clarifying legal issues and allocating responsibilities and processes for deciding AI-related issues."