AtScale's AI-powered modeling engine adapts to dynamic analytics needs.

The rush to deploy generative AI is in full swing. Everyone’s talking about the latest models, but the real limiter, and the real source of ROI, isn’t the model at all. It’s the data architecture that powers it. Legacy architectures built for BI dashboards were designed for looking backward, batch jobs and historical reporting. Generative AI flips that on its head. It requires real-time responsiveness, semantic search across unstructured data, and pipelines that can transform raw, messy inputs into high-quality context the model can trust. That’s why the “boring” work of ingestion, cleaning, chunking, and indexing matters so much. Every step compounds. A small flaw early in the pipeline cascades into hallucinations, bad answers, and lost user trust. A solid data foundation, on the other hand, turns AI from a flashy demo into a reliable system people actually adopt. Great models get the headlines. But it’s great data architecture that makes them deliver. 👉 Dive into our full breakdown here: https://hubs.la/Q03QNMkY0

𝗺𝘂𝗹𝘁𝗶-𝘀𝘁𝗮𝗴𝗲 𝗿𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 Gen AI 𝗔𝗽𝗽𝘀 using LangChain in Databricks Gen AI Apps not only retrieve & synthesize info, but can also 𝗥𝗘𝗔𝗦𝗢𝗡 it building & managing multi-stage Apps require - composition frameworks - TOOLS (vector store, model serving) from platforms like Databricks dev steps - build each component (retriever, tools) - combine components into 𝗰𝗵𝗮𝗶𝗻𝘀 - combine chains into multi-stage AI system 𝗰𝗼𝗺𝗽𝗼𝘀𝗶𝘁𝗶𝗼𝗻 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀 1) types - composition: LangChain, LlamaIndex, Haystack, DSPy - agents: AutoGPT, AutoGen, LangChain-agents 2) each has different focus, opinions, best ways to solve different problems 𝗟𝗮𝗻𝗴𝗖𝗵𝗮𝗶𝗻 1) 𝗼𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 framework to build Apps using LLMs, Tools, Agents, Chains - extend LLM to be context-aware, reason, interact with external env - provide 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 to create reusable workflow 2) provide building blocks - prompt: structure input & guide LLM (𝗽𝗿𝗼𝗺𝗽𝘁 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴) - chain: connect prompt, tools, LLM together to control FLOW of data - 𝗿𝗲𝘁𝗿𝗶𝗲𝘃𝗲𝗿: connect LLM with EXTERNAL data stores (wikipedia, vector store) - 𝘁𝗼𝗼𝗹𝘀: FUNCTIONS to connect LLM with EXTERNAL systems (APIs, DBs) 𝗟𝗹𝗮𝗺𝗮𝗜𝗻𝗱𝗲𝘅 - 𝗱𝗮𝘁𝗮 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 for creating data-to-LLM pipeline - ingestion, 𝗶𝗻𝗱𝗲𝘅𝗶𝗻𝗴 (structuring), querying (retrieval + response synthesis) - components: indexes, engines (query, retriever) 𝗗𝗦𝗣𝘆 can generate best prompts DYNAMICALLY 𝗳𝗮𝗰𝘁𝗼𝗿𝘀 to consider choosing framework 1) library features (eg: LLM interfaces, integrations with external systems) 2) performance & scalability: handle large volumes of data 3) stability, complexity, control - libraries are evolving & experimenting -> APIs can be unstable - challenges to use Databricks products (managed services) 1) 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 𝗠𝗼𝗱𝗲𝗹 𝗔𝗣𝗜 - instant access to state-of-art LLMs - UNIFIED interface for deploying, governing, serving AI models 2) 𝘃𝗲𝗰𝘁𝗼𝗿 𝘀𝗲𝗮𝗿𝗰𝗵 (vector DB integrated with Lakehouse) - store VECTOR + METADATA on data inside UC (delta table, volume) - accessible via REST API / SDK 3) 𝗠𝗟𝗳𝗹𝗼𝘄 - Tracking Server: LOGGING pipelines during dev - Model Registry - Model Serving (deployment) - Evaluation 4) Lakehouse Monitoring 𝗗𝗲𝗺𝗼 (using Databricks products & LangChain 𝗟𝗖𝗘𝗟 𝘀𝘆𝗻𝘁𝗮𝘅) 1) input: user question - response: answer + recommend youtube videos (based on CONTEXT from vector store) 2) code qa_chain = ({"question": RunnablePassthrough()} | prompt_template | llm) retriever_chain = RunnableLambda(𝘀𝗲𝗮𝗿𝗰𝗵𝗩𝗲𝗰𝘁𝗼𝗿𝗦𝘁𝗼𝗿𝗲) yt_chain = RunnableLambda(𝘀𝗲𝗮𝗿𝗰𝗵𝗬𝗼𝘂𝗧𝘂𝗯𝗲) combine_chain = prompt_template2 | llm multi_chain = {"answer": qa_chain, "𝗰𝗼𝗻𝘁𝗲𝘅𝘁": retriever_chain} | (RunnablePassthrough.assign(videos=itemgetter("context") | yt_chain)) | combine_chain multi_chain.𝗶𝗻𝘃𝗼𝗸𝗲(question) 𝗿𝗲𝗳𝗲𝗿𝗲𝗻𝗰𝗲: databricks-academy #databricks #langchain #llm

Working on AI driven data analytics for the last few months, I am beginning to realize that the data community has done a disservice to semantic metadata. Because software systems till now could only properly express and process structured metadata, we limited ourselves to table/column definitions with some (largely failed) attempts at expressing metrics in a structured format - yaml or json. But that's merely scratching the surface of the vast amount of contextual information that accompanies data. This context is usually embedded in unstructured formats - PDFs, FAQs, internal docs, chats, customer service logs, meeting notes etc. Often, this context covers years of historical understanding of complex domain logic. Data analysts spend months during their onboarding process to acquire this context before they can extract any meaningful value from Data. Sure, you could surface this rich metadata in your favourite MDS data catalog tool but there was no way to actionize this knowledge. Until now, that is. AI agents with tool calls open up a completely different way to utilize this type of unstructured semantic information. For instance, a properly configured AI agent could read a user document (pdf) using RAG (or even a simple document search) to understand the definition of a metric (with all its complex nuances) and then execute a SQL query that incorporates this definition. Agents are no longer bounded by information structure. IMO, we will see a shift from semantic models to gathering and organizing unstructured knowledge for AI analytics. Structured semantic models have their place in this architecture but in a very limited way. Huge isn't even the word that describes the possibilities this unlocks.

In the race to scale enterprise AI, most organizations are betting on a single approach. That’s the suboptimal move. The real competitive advantage lies in orchestrating three complementary technologies: Context-Aware Generation (CAG), Retrieval-Augmented Generation (RAG), and Token-Oriented Object Notation (TOON). The AI Architecture Reality Check: Today’s enterprise AI systems face a trilemma: maintain accuracy without hallucinations, achieve speed without astronomical costs, and ensure scalability without infrastructure nightmare. Single method architectures consistently fail one dimension. CAG preloads knowledge into the context window for blazing-fast inference (9.2× fewer tokens per query post-cache), but struggles with dynamic knowledge bases and GPU overhead. RAG fetches fresh data in real-time, ensuring accuracy for ever-changing information, yet incurs per-query retrieval costs that compound at scale. TOON sits orthogonal to both —not a replacement, but a force multiplier for whatever data you’re passing to your models. Why These Three? The Synergy Principle - The convergence pattern works like this: 1. CAG Handles Stable, High-Frequency Knowledge Our customer support playbooks, compliance guidelines, product specs—information that rarely changes but gets queried constantly. Cache build cost is frontloaded (roughly 1,370 tokens), but the break-even point is just 6 queries. After that, you’re processing knowledge at 10× fewer tokens than RAG. 2. RAG Handles Dynamic, Domain-Specific Knowledge Regulatory updates, market intelligence, real-time customer data—anything that changes faster than your cache refresh cycle. RAG reduces hallucinations by 35-45% when grounded in verified data sources. For compliance-heavy industries, this isn’t optimization; it’s existential. 3. TOON Optimizes the Transport Layer Both CAG and RAG pump structured data to LLMs. TOON compresses that payload by 30-60% compared to JSON, cutting token costs across both approaches. For CAG’s cached context, TOON reduces your cache build footprint. For RAG’s retrieval results, TOON minimizes tokens consumed by integrating external knowledge. The Hybrid Architecture we are experimenting on: Tier 1: Fast-Path (CAG) Frequent queries hitting stable knowledge → cached context (low latency, low cost) Tier 2: Retrieval Path (RAG) Infrequent or dynamic queries → live retrieval (fresh data, auditable sources) Tier 3: Encoding Layer (TOON) All structured inputs/outputs → token-efficient format (cost compression across both paths) This isn’t theoretical, benchmarks show: • Latency: 47ms (CAG) vs. 353ms (RAG alone) for stable queries • Accuracy: 87.4% (TOON-formatted inputs) vs. 83.2% (JSON) on structured reasoning tasks • Cost per 1M tokens: $3-5 (hybrid CAG+RAG+TOON) vs. $8-12 (single-method RAG) • Hallucination rate: 8-13% (RAG grounded + TOON clarity) vs. 26-34% #AgenticAI #AI4Tech Sri Shivananda Ivan D'Souza Rohini Anandan Jack Gibson Alan Torrance Shormi Bhattacharya

Evolution of RAG Architectures — From Naïve Retrieval to Agentic Intelligence Retrieval-Augmented Generation (RAG) has transformed from a simple context-retrieval mechanism into a full cognitive architecture driving modern enterprise AI systems. The image below captures this evolution — from early RAG implementations to emerging Agentic RAG models. => The Core Anatomy of RAG • Embeddings & Vector DBs: Map unstructured text into high-dimensional representations. • Similarity Search: Retrieve semantically close documents to enrich prompts. • LLM Integration: Fuse context + query to generate grounded, domain-aware responses. • Continuous Feedback: Evaluate, retrain, and optimize retrieval pipelines. => The Evolution Path • Naïve RAG: Simple retrieval-and-respond flow using vector search. • HyDE (Hypothetical Document Embedding): Generates synthetic answers to improve retrieval precision. • Corrective RAG: Introduces evaluators and feedback loops to grade responses and re-query data sources. • Multimodal RAG: Combines text, vision, and speech — enabling multimodal understanding. • Graph RAG: Integrates knowledge graphs for relational reasoning across entities. • Hybrid RAG: Blends vector and graph retrieval for contextual depth and logical consistency. • Adaptive RAG: Uses reasoning chains, query analyzers, and dynamic prompt adaptation. • Agentic RAG: Adds autonomous agents, long-term memory, planning, and multi-context tool usage. => Why This Evolution Matters • Moves RAG from retrieval → reasoning → autonomy. • Reduces hallucinations and enhances explainability. • Enables multi-source grounding (documents, APIs, enterprise systems). • Scales to real-time decision support, not just text generation. • Forms the foundation for cognitive copilots that can plan, act, and self-correct. => Key Enterprise Use Cases • Intelligent Knowledge Search: Augmented QA over enterprise data lakes and codebases. • Regulatory & Compliance Assistants: Context-aware retrieval with traceability. • Healthcare & Legal AI Systems: Graph-driven reasoning with domain ontologies. • Developer & Cloud Copilots: Contextual code retrieval + autonomous task planning. • Agentic Analytics: Multi-agent systems connecting LLMs with internal and external data sources. => The Road Ahead — Agentic RAG Agentic RAG unifies: • Memory (short-term + long-term) • Reasoning & Planning (ReAct, CoT, ToT) • Tool & API Integration (search, cloud, vector, graph) • Multi-Agent Collaboration for distributed cognition It’s where RAG evolves from context retrieval to contextual intelligence — the foundation of the next generation of enterprise AI architectures. Follow Rajeshwar D. for more insights on AI/ML. #RAG #AgenticAI #GenerativeAI #LLM #KnowledgeGraphs #VectorDB #AIArchitecture #EnterpriseAI #MLOps #RetrievalAugmentedGeneration

💡 Bridging Data Warehouse Methodology with AI - Turning Raw Data into Intelligent Insights In modern analytics, two worlds are converging: 📊 The structured precision of Data Warehousing 🧠 The contextual intelligence of Large Language Models (LLMs) Traditionally, data engineers have relied on the Bronze-Silver-Gold architecture to build scalable, reliable data pipelines: 🔸 Bronze Layer: Raw, ingested data - semi-structured, messy, often streaming in real-time from multiple sources. 🔸 Silver Layer: Cleaned, conformed, and joined datasets - establishing a single source of truth across domains. 🔸 Gold Layer: Curated, business-ready data models designed for analytics, dashboards, and decision-making. But as organizations adopt AI, a new opportunity emerges: 💡 Integrating LLM-driven insights directly into the DWH lifecycle. Here’s how it works in practice: Augmenting Silver Layer enrichment - LLMs can assist with schema mapping, data classification, and semantic tagging of unstructured sources (text, logs, documents). Enhancing the Gold Layer - Instead of static KPIs, we can embed AI-generated features and contextual summaries that enrich business metrics with narrative explanations. Creating a Feedback Loop - Insights generated by LLMs (for example, anomaly explanations or predictive insights) can be written back into the DWH as structured data - creating a continuously learning system. LLM as a Semantic Layer - Instead of querying only tables and joins, users can query concepts through natural language interfaces connected to the governed warehouse. This convergence transforms the DWH from a passive repository into an intelligent data platform - one that not only stores and reports, but understands, reasons, and learns. True analytical transformation happens when trustworthy data meets the reasoning capabilities of AI. #DataAnalytics #AI #DataWarehouse #LLM #ModernDataStack #DataEngineering #InsightDriven

Most companies only see the “tip of the iceberg” when building an AI product - the API fees, the cloud bill, and the UI development. But the true cost lies underwater: the complex engineering, data pipelines, compliance work, evaluation systems, and ongoing maintenance required to make an AI product reliable, safe, and production-ready. Here’s a breakdown of what really goes into building and scaling an AI product: Visible Costs 1. Model / API Costs Usage fees for LLMs like OpenAI or Anthropic, plus token billing and fine-tuning charges that scale with product adoption. 2. Cloud Infrastructure The servers, storage, and databases needed to run your app reliably and handle growing traffic. 3. UI / Frontend Development Building the web/mobile interface your users interact with, including screens, flows, and basic interactions. Hidden Costs 4. Data Cleaning & Preprocessing Transforming raw, messy data into structured, high-quality inputs that your AI system can understand. 5. Embeddings & Vector Databases Setting up and maintaining vector storage, indexing, and regular embedding updates for semantic search or RAG. 6. RAG Pipeline Engineering Designing retrieval logic, optimizing chunking, and fine-tuning latency to ensure accurate, fast responses. 7. Agent Logic & Tooling Creating the reasoning layer: planning, routing tasks, managing memory, and coordinating multiple agents. 8. Evaluations & Testing Measuring hallucinations, scoring outputs, and running full end-to-end tests to ensure reliability. 9. Observability & Monitoring Tracking prompts, errors, and feedback loops to detect failures and improve model behavior over time. 10. Security & Privacy Compliance Enforcing access controls, handling sensitive data safely, and meeting SOC2/GDPR requirements. 11. Scaling & Performance Optimization Adding caching, batch processing, and fallback systems to keep the product fast and stable. 12. Human-in-the-Loop Reviews Experts reviewing outputs, correcting mistakes, and ensuring accuracy for high-risk use cases. 13. Prompt Engineering & Prompt Maintenance Updating prompts, testing model changes, and versioning workflows to prevent regressions. 14. Continuous Updates Refreshing data, migrating models, and releasing new features as the AI ecosystem evolves. 15. Talent & Team Costs Hiring AI engineers, MLOps specialists, data experts, and infra teams to build and maintain the system. If you found this breakdown helpful, save this post for later and share it Р.С: Rathnakumar Udayakumar, FolIοw Rathnakumar Udayakumar for more content like this ----------------------- ✅𝐋𝐞𝐚𝐫𝐧 𝐀𝐈 𝐨𝐫 𝐋𝐞𝐟𝐭 𝐁𝐞𝐡𝐢𝐧𝐝? Learn with $15,000 worth of ↩️ - 60+ Chapters of ChatGPT Mastery - 38,000+ AI Tools - 600+ AI Courses - 3000+ AI Prompts & More 𝐒𝐮𝐛𝐬𝐜𝐫𝐢𝐛𝐞 👉 aiplanetx.com

AI agents can't reason without semantic structure. ❌ But enforcing that structure at scale? That's where most production systems die. 🧠 Why Agents Need Ontologies: Your agent needs to know that "Knives Out" and "Knives Out 2" are different movies despite same director, same actors, same theme. Or that two people researching a car online are one household, not two sales opportunities. Without explicit entity relationships and constraints, agents guess. With ontologies, they traverse verified knowledge graphs. Ontologies provide the semantic foundation, defining not just what entities exist but how they relate to each other through explicit, queryable relationships. A healthcare ontology might define that a patient has a relationship to a primary physician, and that this relationship carries the constraint that a patient can have at most one primary physician. This semantic structure enables agents to reason about complex scenarios through graph traversal rather than through probabilistic semantic search ⚙️ The Enforcement Problem: You can build the perfect ontology. But if your LLM outputs "score: high" instead of "score: 1", your entire pipeline breaks. This is why structured outputs aren't optional, they constrain token generation at runtime to guarantee schema compliance. Not parse-time validation. Generation-time constraint. 📈 The Scale Challenge: Netflix's case: 1 million entities = hundreds of millions of entity pairs to match. Traditional approaches choke. Their solution: Model entity matching as a classification problem (naturally parallelizable) Partition data across nodes with independent processing 10x speedup from Apache Arrow + parallel writes Zero bottlenecks through structured output contracts between components The breakthrough? When outputs are guaranteed valid, every partition can operate independently. 🏗️ The Architecture Stack: Layer 1: Structured outputs (syntax guarantees) Layer 2: Ontology graphs (semantic foundation) Layer 3: Orchestration logic (business rules + prerequisites) Layer 4: Multi-agent coordination (structured interfaces) Each layer depends on the one below. Skip structured outputs? Your orchestration logic can't enforce "collect skin tone before recommending products." 💰 Real Production Metrics: 90% reduction in custom integration code Process time: hours → 20 seconds Cost per entity pair: slashed through parallelization Debugging: Local restarts with logs vs. opaque JVM failures 🎯 The Technical Insight: Entity disambiguation at scale is an architecture problem. You need: Schemas that map to your domain ontology Constraint-based generation (not hope-based parsing) Graph traversal for multi-hop reasoning Operational tooling for resource tuning and monitoring ⚠️ What This Doesn't Solve: Structured outputs guarantee valid JSON. They don't guarantee factual correctness. You still need evaluation layers, domain expertise, and monitoring.

Reasoning Needs Rules AI reasoning isn’t a prompt-engineering problem - it’s an architecture problem. Ontologies bring semantic clarity. Structured outputs bring syntactic discipline. This combination turns prototypes into scalable, trustworthy AI systems. If you’re building AI systems that reason instead of guess, this article is worth a read.👇 #AIArchitecture #LLMDesign #Ontologies #KnowledgeEngineering #GenerativeAI

"When Systems Don’t Speak the Same Language: The Case for Ontologies in AI" - One of my favorite examples of this confusion in pharma-biotech manufacturing shows up when ERP integrates with MES. What engineers call “inbound” in one system might be “outbound” in the other for the exact same transaction. Take something as simple as an inventory adjustment or batch status update. In theory, both are bi-directional in pharma and biotech manufacturing. Yet without a shared semantic model, each system interprets directionality differently. The result? Integration logic that works syntactically but fails semantically and debugging sessions that never seem to end. Without a unified vocabulary across disparate systems often running on different platforms from multiple vendors even the most sophisticated agent will “hallucinate” relationships. It may infer connections where none exist or miss ones that do, simply because the systems lack shared relationship semantics. If modern LLMs “understand” natural language, why constrain them with ontologies or schemas? 🙂 Because the very selling point of “no-schema, natural language access” collapses when applied to production-grade reasoning. Ontologies introduce the semantic discipline that turns linguistic understanding into deterministic logic. The difficulty here is 80% semantic governance and 20% engineering. Getting data stewards, business SMEs, and platform owners to align on what “Customer,” “Batch,” or “Product” actually means and who owns those definitions is the real bottleneck. If your enterprise already has a mature data catalog, clear domain models, and schema ownership culture, ontology-driven reasoning is absolutely feasible. But how many enterprises truly do? LLMs can speak our language , but they can’t reason about our business until our systems share a language too. #EnterpriseAI #DataSemantics #DigitalManufacturing