Understanding Model Frameworks

For the last couple of years, Large Language Models (LLMs) have dominated AI, driving advancements in text generation, search, and automation. But 2025 marks a shift—one that moves beyond token-based predictions to a deeper, more structured understanding of language.  Meta’s Large Concept Models (LCMs), launched in December 2024, redefine AI’s ability to reason, generate, and interact by focusing on concepts rather than individual words.  Unlike LLMs, which rely on token-by-token generation, LCMs operate at a higher abstraction level, processing entire sentences and ideas as unified concepts. This shift enables AI to grasp deeper meaning, maintain coherence over longer contexts, and produce more structured outputs.  Attached is a fantastic graphic created by Manthan Patel How LCMs Work:  🔹 Conceptual Processing – Instead of breaking sentences into discrete words, LCMs encode entire ideas, allowing for higher-level reasoning and contextual depth.  🔹 SONAR Embeddings – A breakthrough in representation learning, SONAR embeddings capture the essence of a sentence rather than just its words, making AI more context-aware and language-agnostic.  🔹 Diffusion Techniques – Borrowing from the success of generative diffusion models, LCMs stabilize text generation, reducing hallucinations and improving reliability.  🔹 Quantization Methods – By refining how AI processes variations in input, LCMs improve robustness and minimize errors from small perturbations in phrasing.  🔹 Multimodal Integration – Unlike traditional LLMs that primarily process text, LCMs seamlessly integrate text, speech, and other data types, enabling more intuitive, cross-lingual AI interactions.  Why LCMs Are a Paradigm Shift:  ✔️ Deeper Understanding: LCMs go beyond word prediction to grasp the underlying intent and meaning behind a sentence.  ✔️ More Structured Outputs: Instead of just generating fluent text, LCMs organize thoughts logically, making them more useful for technical documentation, legal analysis, and complex reports.  ✔️ Improved Reasoning & Coherence: LLMs often lose track of long-range dependencies in text. LCMs, by processing entire ideas, maintain context better across long conversations and documents.  ✔️ Cross-Domain Applications: From research and enterprise AI to multilingual customer interactions, LCMs unlock new possibilities where traditional LLMs struggle.  LCMs vs. LLMs: The Key Differences  🔹 LLMs predict text at the token level, often leading to word-by-word optimizations rather than holistic comprehension.  🔹 LCMs process entire concepts, allowing for abstract reasoning and structured thought representation.  🔹 LLMs may struggle with context loss in long texts, while LCMs excel in maintaining coherence across extended interactions.  🔹 LCMs are more resistant to adversarial input variations, making them more reliable in critical applications like legal tech, enterprise AI, and scientific research.  

These six educational theorists laid the foundation for how we understand learning, development, and instruction today. Jean Piaget revolutionized education by mapping out cognitive stages, helping educators tailor instruction to children’s mental readiness. Lev Vygotsky emphasized the power of social interaction and scaffolding, introducing the Zone of Proximal Development as a guide for meaningful support. John Dewey championed experiential learning, advocating for classrooms that mirror real-life problem solving and democratic participation. Howard Gardner expanded our view of intelligence, validating diverse learner strengths and inspiring inclusive, differentiated instruction. B.F. Skinner’s behaviorist principles shaped classroom management and reinforcement strategies, while Benjamin Bloom’s taxonomy offered a clear framework for designing objectives and assessing depth of understanding. Together, their theories continue to shape curriculum design, instructional methods, and inclusive practices across the globe. #FoundationsOfLearning

How to Build a Conceptual Theoretical Framework Your PhD Supervisor Can’t Tear Apart   A weak conceptual framework won’t just raise the blood pressure of your most loving supervisor; it will jeopardize your proposal or entire thesis.   BTW… a conceptual framework is not a diagram you sketch out at 1am because your supervisor said you need one. It’s the logic engine of your entire thesis, it indicates you really know what you’re doing.   For mixed methods, your conceptual theoretical framework is even more important. It explains how your qualitative insights and quantitative results speak and relate to each other.   Here’s what I often share with my registered students…   1) Anchor your framework in real theory, not “it sounds right”. a] Don’t construct a framework based on your preferences. b] Select theories that actually match your topic, variables, and context. c] For example: If your topic is about the importance of a technology, such as using TAM in the framework, explain how people accept or reject technology. d] Likewise, if about JD-R as a framework to examine staff retention issues, articulate about burnout, stress, and staff engagement. e] Similarly, we use TPB to describe how attitudes, norms, and control shape behaviour. f] If you can’t explain why a theory belongs in your framework, bin it.   2) Why these theories belong in your study a] Your supervisor will want to know… Why THIS theory for THIS study in THIS context? b] Be explicit… c] What does the theory help you measure, explain, or predict? d] What gaps does it fill? e] How does it help you understand your variables? f] Use logic. Weak logic = a framework that collapses under scrutiny.   3) Map your variables like a researcher, not a graphic designer a] A solid conceptual framework clearly indicates… b] Independent variables. c] Dependent variables. d] Mediators/moderators (only if they're tested). e] Theoretical relationships. f] If mixed methods: How the qualitative and quantitative phases connect. g] If your conceptual pathways look like a bowl of noodles and create more questions than answers, start again.   4) Connect theory with the method with the analysis (triangulate) a] The best frameworks show alignment all the way through. Why? Because… b] Theory informs your variables. c] Variables shape your research questions. d] RQs shape your methodology. e] Methodology shapes your instruments. f] Instruments shape your analysis. g] This pathway may convince your most loving supervisor that you may know what you’re doing.   5) End with one powerful sentence a] A strong conceptual framework often looks something like… b] This framework integrates X and Y theories to explain how A influences B within Z context, guiding both the qualitative and quantitative phases of this mixed-methods study. c] That one sentence alone will inform your most loving supervisor… d] Your entire study is coherent, not a random collection of frameworks or chapters.   Need help? Check my comments below…

All new models come with a larger Context Window...but do you know what it is? Here's my quick guide: The Definition - Context window = amount of text an AI model can process at once - Larger windows allow AI to handle more information simultaneously - For instance, if the context window is 1024 tokens, the model can utilize up to 1024 tokens of prior text to understand and generate a response. Why It Matters - Enhanced Understanding: Larger context windows allow the model to retain more information from the ongoing conversation or document, leading to more coherent and contextual responses. - Complex Tasks: With a bigger context, models can tackle more complex tasks like long-form document analysis, multi-turn conversations, or summarizing lengthy articles without losing track. Reduced Fragmentation: A larger context window reduces the need to break down input into smaller chunks, leading to more natural and uninterrupted interactions. What to Expect - More Insightful Outputs: As AI models continue to evolve, expect richer and more insightful outputs, especially in applications like content generation, chatbots, and customer support. - Increased Productivity: Businesses leveraging these models can achieve higher productivity by allowing AI to handle more sophisticated tasks with less human intervention. Alternative to Large Context Windows: 1. Chunking: Breaks large text into smaller chunks, processing them independently. - Pros: Memory efficient, scalable. - Cons: Risk of losing context, complex to stitch results together. 2. RAG: Retrieves relevant information from external sources during generation. - Pros: Accesses vast knowledge, improves accuracy, works with smaller context windows. - Cons: Complex to set up, potential latency, depends on data quality. Things to Be Careful With: - Context Loss: Whether chunking or using RAG, losing the overall context is a risk. Ensuring that each chunk or retrieved information is relevant and seamlessly integrated is crucial. - Latency: Larger context windows and RAG systems can increase processing time, affecting real-time applications like chatbots or live interactions. - Memory and Computational Overhead: Larger context windows demand more memory and computational power, which can be a limitation for some systems. - Complexity of Implementation: Both alternatives, especially RAG, require a more complex setup, including retrieval systems and databases. This can increase the cost and time needed for development. - Data Relevance: In RAG, the quality of the generated output is highly dependent on the relevance and accuracy of the retrieved data. Ensuring the retrieval system is well-tuned, and the knowledge base is up-to-date is essential. Choose the right approach based on your specific use case!

Leaders reference the People, Process & Technology framework, but never use it correctly Moreover, nobody considers the Data component, which is irresponsible today So I thought I would rethink the framework and come up with some new key questions: 𝐏𝐞𝐨𝐩𝐥𝐞👥 1. What people do we need to deliver against our goals? 2. What skills should they have? 3. How do we foster a data-focused culture? 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐞𝐬 🔄 1. What processes will allow us to optimise our workflows? 2. What governance structure can help facilitate success? 3. How do we embed data into our decisioning and ways of working? 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐲 🛠️ 1. What is the role of technology in our strategy and org direction? 2. What tools and technologies do we need to run our business? What do we need to gather insight? 3. How can we optimise technology adoption and usage? 𝐃𝐚𝐭𝐚 📊 1. How does data help us achieve the organisational goals? 2. What data do we need to drive better insights? 3. What is our strategy to use data and ensure it is of high quality? Use these questions to get you started to thinking more holistically about crucial topics, especially about data and data change I wrote about People, Process, Tech and Data last summer, so check out the article on it and how to think more strategically about this important topic!

Two years ago, "context layer" wasn't on any market map. We were building it anyway. This month, Gartner defined it as foundational for AI success. Gartner breaks the context layer into three foundations: semantics, operational state, and provenance. Here's how we've seen those play out in practice. Take one question: "Who are our top customers this quarter?" Before your AI can answer that, it needs to know who's asking. Is it sales or CX? What does "customer" mean at your company? What does "top" mean? How do you actually calculate revenue? Four questions underneath one question. That's the semantics problem. The operational state problem is that even when teams had definitions documented, the AI still needed right-time awareness of the business. Is this customer still active? Has the quarter closed? Did the revenue number change this morning? Without current state, the model answers yesterday’s question with yesterday’s truth. And provenance? Teams needed to trace where an answer came from, which definition it used, and whether that logic was still valid. When the revenue definition changed, they needed to know which agents, outputs, and decisions were still relying on the old one. One line from the report stood out: "The context layer cannot be bought off the shelf; it must be engineered to fit your organization’s unique needs." We're living this in real time in context engineering sprints with customers — starting with high-value use cases, not the whole layer at once. Gartner estimates that prioritizing a semantic-rich context layer can increase AI accuracy by up to 80% and reduce costs by up to 60% by 2027. One more thing worth paying attention to: be wary of vendors using "context graph" and "context layer" interchangeably. A context graph is one component. The context layer is the whole architecture, and governance and data readiness are prerequisites, not afterthoughts. The market has gotten noisy. If a vendor can't show you all three foundations, they're not selling a context layer. Building this was not on our roadmap two years ago. We built it alongside customers because we kept hitting the same roadblocks getting trustworthy agents into production. It's energizing to finally have the map. We're going deeper on all of this at Activate on April 29 — live demos, real architectures, the context layer in production. Link in comments.

When leaders brainstorm over trackers instead of architectures 😅 If only pipelines ran as smoothly as the meetings about how to track them.. As funny as it sounds, this happens way too often in data teams — hours spent debating Jira structures, story points, epics, and subtasks… Meanwhile a pipeline is quietly failing in production. But behind the humor lies an important reminder: → 𝐺𝑟𝑒𝑎𝑡 𝑑𝑎𝑡𝑎 𝑒𝑛𝑔𝑖𝑛𝑒𝑒𝑟𝑖𝑛𝑔 𝑖𝑠𝑛'𝑡 𝑎𝑏𝑜𝑢𝑡 𝑝𝑒𝑟𝑓𝑒𝑐𝑡 𝑡𝑟𝑎𝑐𝑘𝑒𝑟𝑠—𝑖𝑡'𝑠 𝑎𝑏𝑜𝑢𝑡 𝑡ℎ𝑒 𝑟𝑖𝑔ℎ𝑡 𝑡ℎ𝑖𝑛𝑘𝑖𝑛𝑔. Over the years, one pattern stands out: Teams that obsess over tools often under-invest in architecture. And teams that anchor on architecture naturally simplify everything else—tooling, tracking, delivery. Sharing few learnings that have made difference in my data engineering journey to build robust data systems: 1. Think in systems, not tasks Before assigning story points, ask: → What domain does this belong to? → What data contracts govern it? → Is this transformation even necessary? Clear system thinking > endless subtasks. 2. Architecture over trackers A well-defined: → Data model → Lineage flow → Orchestration pattern → and error strategy removes 80% of ticket back-and-forth. Your Jira gets simpler because your architecture is clearer 3. Invest in observability early Strong quality checks, lineage, and alerts mean: → Faster debugging → Better collaboration → No 2 AM firefighting Observability is invisible until you desperately need it. 4. Document why, not just what Trackers show what you did. Architecture docs explain why. Future you will thank present you. 5. Reduce cognitive load → Simplified schemas. → Modular pipelines. → Automated steps. Less time deciphering = less time debating story points. Maturity isn't measured by tracker maintenance — it's measured by systems that don't require constant firefighting. Here's what separates good data engineers from great ones- → Ask "what breaks if this fails?" before writing code → Think in layers, not monoliths → Build systems their junior teammates can debug → Optimize for the team inheriting their work, not just shipping fast → Know when NOT to over-engineer, Right-sizing matters more than resume-driven development → Understand that 99% vs 99.9% uptime isn't a rounding error—it's millions in cost 👉 Remember: Your Jira board doesn't run your pipelines. Your architecture does. Spend your energy accordingly. 𝗕𝘂𝗶𝗹𝗱 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝘁𝗵𝗮𝘁 𝘀𝗰𝗮𝗹𝗲𝘀, 𝗻𝗼𝘁 𝗲𝗻𝗱𝗹𝗲𝘀𝘀 𝗺𝗲𝗲𝘁𝗶𝗻𝗴 𝘁𝗮𝗹𝗲𝘀.

🔹 From Storage to Strategy: Rethinking the System of Record. In EA 4.0, the System of Record isn’t just a source of truth. It’s the memory of a thinking enterprise. In traditional enterprise architecture, the System of Record was an afterthought: 📁 A static datastore. 📊 A reporting foundation. 🛠️ A compliance checkbox. But in Enterprise Architecture 4.0, the role of the System of Record is transformed. Why? Because in a world of intelligent systems and autonomous agents, data isn’t just queried— It’s interpreted, reasoned with, and acted upon. 👉 That means the System of Record must now be designed as cognitive infrastructure: Governed not just for quality, but for meaning and trust Modeled not just for storage, but for agentic decision-making Integrated not just by pipelines, but by semantic context EA 4.0 architects don’t just catalog data—they design the conditions for memory. If your enterprise can’t remember reliably, it can’t think responsibly. 💬 How are you rethinking your architecture for the age of autonomy? Are you using the System of Record? Have you evolved its traditional meaning into something that supports AI? Please share your insights and experiences below. 🙏. #EnterpriseArchitecture40 #EA40 #SystemOfRecord #DataGovernance

What’s the point of a massive context window if using over 5% of it causes the model to melt down? Bigger windows are great for demos. They crumble in production. When we stuff prompts with pages of maybe-relevant text and hope for the best, we pay in three ways: 1️⃣ Quality: attention gets diluted, and the model hedges, contradicts, or hallucinates. 2️⃣ Latency & cost: every extra token slows you down, and costs rise rapidly. 3️⃣ Governance: no provenance, no trust, no way to debug and resolve issues. A better approach is a knowledge graph + GraphRAG pipeline that feeds the model the most relevant data with context instead of all the things it might need with no top-level organization. ✅ How it works at a high level: Model your world: extract entities (people, products, accounts, APIs) and typed relationships (owns, depends on, complies with) from docs, code, tickets, CRM, and wikis. GraphRAG retrieval: traverse the graph to pull a minimal subgraph with facts, paths, and citations, directly tied to the question. Compact context, rich signal: summarize those nodes and edges with provenance, then prompt. The model reasons over structure instead of slogging through sludge. Closed loop: capture new facts from interactions and update the graph so the system gets sharper over time. ✅ A 30-day path to validate it for your use cases: Week 1: define a lightweight ontology for 10–15 core entities/relations built around a high-value workflow. Week 2: build extractors (rules + LLMs) and load into a graph store. Week 3: wire GraphRAG (graph traversal → summarization → prompt). Week 4: run head-to-head tasks against your current RAG; compare accuracy, tokens, latency, and provenance coverage. Large context windows drive cool headlines and demos. Knowledge graphs + GraphRAG work in production, even for customer-facing use cases.

If you are an AI Engineer building production-grade GenAI systems, RAG should be in your toolkit. LLMs are powerful for information generation, but: → They hallucinate → They don’t know anything post-training → They struggle with out-of-distribution queries RAG solves this by injecting external knowledge at inference time. But basic RAG (retrieval + generation) isn’t enough for complex use cases. You need advanced techniques to make it reliable in production. Let’s break it down 👇 🧠 Basic RAG = Retrieval → Generation You ask a question. → The retriever fetches top-k documents (via vector search, BM25, etc.) → The LLM answers based on the query + retrieved context But, this naive setup fails quickly in the wild. You need to address two hard problems: 1. Are we retrieving the right documents? 2. Is the generator actually using them faithfully? ⚙️ Advanced RAG = Engineering Both Ends To improve retrieval, we have techniques like: → Chunk size tuning (fixed vs. recursive splitting) → Sliding window chunking (for dense docs) → Structured data retrieval (tables, graphs, SQL) → Metadata-aware search (filtering by author/date/type) → Mixed retrieval (hybrid keyword + dense) → Embedding fine-tuning (aligning to domain-specific semantics) → Question rewriting (to improve recall) To improve generation, options include: → Compressing retrieved docs (summarization, reranking) → Generator fine-tuning (rewarding citation usage and reasoning) → Re-ranking outputs (scoring factuality or domain accuracy) → Plug-and-play adapters (LoRA, QLoRA, etc.) 🧪 Beyond Modular: Joint Optimization Some of the most promising work goes further: → Fine-tuning retriever + generator end-to-end → Retrieval training via generation loss (REACT, RETRO-style) → Generator-enhanced search (LLM reformulates the query for better retrieval) This is where RAG starts to feel less like a bolt-on patch and more like a full-stack system. 📏 How Do You Know It's Working? Key metrics to track: → Context Relevance (Are the right docs retrieved?) → Answer Faithfulness (Did the LLM stay grounded?) → Negative Rejection (Does it avoid answering when nothing relevant is retrieved?) → Tools: RAGAS, FaithfulQA, nDCG, Recall@k 🛠️ Arvind and I are kicking off a hands-on workshop on RAG This first session is designed for beginner to intermediate practitioners who want to move beyond theory and actually build. Here’s what you’ll learn: → How RAG enhances LLMs with real-time, contextual data → Core concepts: vector DBs, indexing, reranking, fusion → Build a working RAG pipeline using LangChain + Pinecone → Explore no-code/low-code setups and real-world use cases If you're serious about building with LLMs, this is where you start. 📅 Save your seat and join us live: https://lnkd.in/gS_B7_7d Image source: LlamaIndex