Martin Rasumoff

Gemini 3 is set to revolutionize Retrieval-Augmented Generation (RAG) with several key advancements: Deep Think = Autonomous Self-Correction: Gemini 3 evaluates retrieved data internally before providing answers. If the results are subpar, it automatically adjusts by re-ranking, refining queries, or switching strategies, which significantly enhances the robustness of RAG pipelines. No More Multi-Turn Retrieval Amnesia: The introduction of encrypted reasoning states allows the model to “remember” its plan across API calls. This capability supports long-horizon tasks, such as 20-step research, without the need for extensive prompts or the risk of losing context. Native Multimodal Backbone: Gemini 3 integrates text, image, and video within a single token space. This allows for the retrieval of raw media and reasoning across different formats without the need for OCR or transcription, greatly simplifying multimodal RAG. Just-in-Time RAG Pattern: With context windows exceeding 1 million tokens, agents can bypass heavy chunking. They dynamically load full documents as needed, combining deep analysis accuracy with cost efficiency. Agentic workflow: The inherent Agentic workflow in Gemini 3 will autonomously orchestrate the planning of math operations in RAG, automatically triggering tools for execution to guarantee accuracy without requiring manual developer intervention. Karthikeyan Navaneethan Efstratios Tsougenis, PhD Venkata K. Siqi Zhang Lini Elizabeth Varghese

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New spec to bridge semantic definitions between AI and BI. And a great list of partners in the mix.

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What if you need reliable data versioning, but full-blown tools like DVC or Pachyderm feel like overkill? Most data versioning tools assume a production-level MLOps pipeline. But for solo researchers, small teams, or prototyping workflows, they introduce unnecessary complexity - config overhead, remote setup, and Git noise. A clean alternative is to build a minimal, Python-native data versioning utility that tracks datasets like code - but without depending on Git LFS or external servers. Key components of such a utility could include: - Content-based hashing (e.g., SHA256 on files or folders) to track data integrity - A local manifest file (YAML or JSON) to record version metadata - Lightweight snapshotting of datasets with automatic diff logging - Optional integration with cloud storage (like S3 or GDrive) via a plug-in system - CLI and Python APIs to register, retrieve, and compare data versions This approach ensures: - Transparent version control of CSVs, images, or model artifacts - Clean separation from code repos - Easy auditability during experiments or model iterations - Seamless integration into existing Python pipelines It won’t replace DVC at scale, but in many real-world projects, it’s more important to have versioning that’s simple enough to use consistently. Version control shouldn’t be a blocker. Sometimes, less is more. #DataVersioning #MLOps #AIEngineering #PythonTools #ExperimentTracking #MachineLearning #MLWorkflow #DataEngineering #OpenSource #PythonForAI

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What is RAG, and why does everyone talk about it? One thing I’ve learned while working with GenAI systems is this: LLMs can sound confident and still be completely wrong. That’s where RAG (Retrieval-Augmented Generation) comes in. Instead of asking a model to answer purely from what it learned during training, RAG makes it look up relevant information first, then generate an answer grounded in that data. The image shows a typical RAG pipeline in practice: •Files are uploaded and text is extracted •Content is split into meaningful chunks •Those chunks are converted into embeddings •Embeddings are stored in a vector database When a user asks a question: •The query is embedded •Similar content is retrieved from the vector database •A prompt is constructed using that retrieved context •The LLM generates an answer based on real data, not guesses The key idea is simple: 👉 The model doesn’t know your data. 👉 It retrieves it first, then reasons on top of it. This is why RAG has become the foundation for: • Chatbots over documents • Internal knowledge bases • Enterprise search • Safer, more accurate AI systems RAG isn’t magic. It’s good data, good retrieval, and good system design working together. If you’re building with GenAI, understanding this pipeline is no longer optional. #RAG #GenerativeAI #LLMs #AIEngineering #VectorDatabases #Embeddings

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Most enterprise AI failures trace to a single architectural mistake: letting probabilistic outputs and nondeterministic model generations mutate transactional state directly. The pattern appears across claims engines, underwriting platforms, and policy administration. Teams grant LLMs write authority to legacy cores and then discover non-idempotent operations, broken audit trails, and irreproducible decisions. Granting autonomous systems direct write authority to a system of record guarantees unresolvable rollback failures. The failure stems from conflating decision intelligence with state authority. Two failure modes dominate this integration phase: ↳ Direct coupling: engineers wire LLMs into brittle workflows via API shortcuts. When the model hallucinates a state transition, the transactional core inherits the corruption. ↳ Shadow isolation: teams replicate read-only data to sandbox environments where agents analyze but cannot execute. Operational impact nears zero while compute costs increase. Both approaches treat AI as a command engine. The architecture must treat it as a proposal engine. Deterministic decoupling isolates AI outputs behind strict execution boundaries. A proper control plane requires four mechanisms: ↳ Schema gateways: reject unstructured payloads before core interaction occurs. ↳ Rule enforcement layers: validate proposals deterministically against business invariants, replacing prompt-based guardrails. ↳ Event-driven buffers: ensure idempotency, safe retries, and audit completeness. ↳ Segregated commit logic: enforce role separation; the AI recommends, legacy systems execute Intelligence is cheap. The durable advantage is an architecture that enables safe, scalable deployment. Engineering leaders must evaluate platforms against one verification constraint: can a probabilistic output directly change a system-of-record state? If the architecture permits this scenario, the system imports risk rather than enabling automation. #SystemsArchitecture #EnterpriseArchitecture #AIGovernance #FinancialServices

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After months of exploring AI implementation at Assignar, I’ve realised something crucial that most businesses miss: having access to AI tools like ChatGPT or Claude isn’t enough. Here’s the reality - while these tools are fantastic for one-off questions, they become truly transformative only when they understand YOUR business. Your most valuable employee isn’t the one who just started yesterday. It’s the person who knows your customers, understands your processes, and remembers that support ticket from six months ago that solved a similar problem. The same applies to AI. For AI to deliver real value in an enterprise setting, it needs access to: • Your support ticket history • Product usage patterns • Internal documentation • Customer interactions This is where things get technical (but stay with me). All this knowledge needs to be organized in what’s called a RAG database - essentially a smart filing system that AI can search through. Then you need a way for the AI to actually access this information. Here’s what surprised me: Currently, Claude.ai is the only mainstream chat interface that has built-in MCP (Model Context Protocol) - think of it as a universal adapter that lets AI tools connect to your company’s data securely. This means that if you use Claude.ai as your corporate LLM interface tool, you don’t need to build and maintain your own. The bottom line for business leaders: If you’re serious about AI transformation, don’t just think about which AI tool to use. Think about how to give that tool access to your institutional knowledge. The companies that figure this out will have a massive competitive advantage. We’re still early in this journey at Assignar, but the potential is clear. AI without context is just a smart calculator. AI with your company’s full context? That’s a game-changer.

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AI is shifting from single models to ecosystems of specialized agents. This research on Reinforced Agent Merging (RAM) highlights a critical reality:  If you merge RL-trained agents the same way we merged fine-tuned models, you destroy value. Why it matters:  • RL agents learn sparse, task-specific intelligence  • Naive averaging dilutes the very behaviors you trained them for  • “One model to rule them all” is no longer viable What RAM shows instead:  • Share common intelligence  • Preserve and amplify unique capabilities  • Combined systems can outperform individual specialists Impact for leaders and builders:  • AI platforms will be agent-based, not model-centric  • Competitive advantage shifts to orchestration and integration  • The hardest problem is no longer training models, but merging intelligence without loss This is where AI strategy meets product leadership. If you are still designing around a single assistant, you are already behind. #AI #ArtificialIntelligence #AgenticAI #ReinforcementLearning #GenAI #AIArchitecture #AIProductManagement #AILeadership #EnterpriseAI https://lnkd.in/ed7pTrsD

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