Clarke Bishop

RAG Was the First Step. It’s Not the Final Architecture for AI Leaders. ​ Most enterprise AI roadmaps today start with Retrieval-Augmented Generation (RAG). It’s practical, it grounds LLMs in enterprise content, and it reduces hallucinations in everyday Q&A. But as we show in our new blog, “From Retrieval to Reasoning: Enhancing HippoRAG with Graph-Based Semantics,” most production RAG is still retrieval‑centric. For AI leaders, that means your current stack often does limited reasoning precisely where your organization needs multi-hop logic, complex relationships, and strict governance. ​ Vector search is built on similarity — not understanding. It surfaces chunks that look relevant in embedding space, but it rarely captures how entities relate, how processes flow, how dependencies propagate, or how conclusions should be logically constructed. That gap becomes a strategic risk when you are accountable for reliability, auditability, and regulatory alignment. This limitation becomes material for AI leaders when: ➡️ Your use cases require multi-step reasoning across many data sources. ➡️ Your data and organizational relationships evolve continuously (entities, ➡️ products, contracts, regulations). ➡️ Regulatory, compliance, or operational accuracy is non‑negotiable. ➡️ The cost of being “confidently wrong” impacts customers, revenue, or brand. In complex enterprise environments, retrieval‑only architectures hit a ceiling. That ceiling is now a board‑level concern. The next phase for AI leadership isn’t abandoning RAG — it’s augmenting it with structured semantics and graph intelligence. In the blog, we show how enhancing HippoRAG with an ontology-based knowledge graph turns unstructured memory into connected knowledge and boosts multi-hop Q&A accuracy. By bringing graph-based semantics into the pipeline, you move from chunk‑matching to relationship‑aware reasoning, where answers are grounded in explicit entities, relationships, and traversable evidence paths. For AI leaders, this is the strategic shift: ➡️ From search to reasoning as a first-class capability. ➡️ From documents to connected knowledge as your enterprise “truth layer.” ➡️ From plausible outputs to trustworthy systems you can defend to regulators, customers, and your board. In this landscape, the LLM is no longer your differentiator. Strong base models are effectively commoditized. Architecture is. If your AI roadmap is still RAG‑only and vector‑only, the question for AI leaders is simple: what happens when retrieval isn’t enough — when your business needs systems that don’t just find information, but can reliably reason over your enterprise knowledge? #AI #EnterpriseAI #RAG #KnowledgeGraphs #GraphAI #AILeadership #GenerativeAI #AIArchitecture #DigitalTransformation #DataStrategy #graphwise https://lnkd.in/daTxZ9wc

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AI isn’t killing IT jobs—it’s redrawing the map. A quick heat-check of today’s data shows the red zones (password resets, OS imaging, L1 SOC triage) are already 30-40 % automated. The green zones—cloud cost governance, DevSecOps policy, responsible-AI bias reviews—still sit at 80-95 % human control. Bottom line: anchor your growth on tasks that require context, creativity and compliance sign-off. Everything else is becoming prompt-filler. #AICareers #CloudSecurity #DevSecOps #FinOps #ResponsibleAI #FutureOfWork

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New from Packt Publishing >> "Building Neo4j-Powered Applications with LLMs: Create LLM-driven search and recommendations applications with Haystack, LangChain4j, and Spring AI" by Ravindranatha Anthapu and Siddhant Agarwal Available at https://amzn.to/4l9lLKO This LLM book is for database developers and data scientists who want to leverage knowledge graphs with Neo4j and its vector search capabilities to build intelligent search and recommendation systems. Working knowledge of Python and Java is essential to follow along. Familiarity with Neo4j, the Cypher query language, and fundamental concepts of databases will come in handy. 𝓦𝓱𝓪𝓽 𝓨𝓸𝓾 𝓦𝓲𝓵𝓵 𝓛𝓮𝓪𝓻𝓷: 🟠Design, populate, and integrate a Neo4j knowledge graph with RAG 🔵Model data for knowledge graphs 🟢Integrate AI-powered search to enhance knowledge exploration 🟠Maintain and monitor your AI search application with Haystack 🔵Use LangChain4j and Spring AI for recommendations and personalization 🟢Seamlessly deploy your applications to Google Cloud Platform

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If you're managing self-hosted large language models in an enterprise setting, you know GPU costs can spiral out of control without smart oversight. ScaleOps' latest AI infrastructure product steps in to automate that, promising 50-70% cost reductions for early adopters by optimizing resource utilization in real time. No code changes needed, just plug it in and let it handle dynamic scaling based on workload demands. A couple of key insights here: First, it eliminates the inefficiencies of overprovisioned GPUs, cutting idle time and improving overall deployment performance. Second, for businesses scaling AI operations, this means faster iteration and lower operational overhead, freeing up teams to focus on innovation rather than babysitting infrastructure. Third, in a landscape where AI budgets are under scrutiny, tools like this could be the difference between sustainable growth and budget blowouts. I've been around enough AI deployments to appreciate when something cuts through the hype with actual efficiency gains. Though I'd love to see long-term data on those savings. What's your biggest pain point with GPU management in AI workloads? Have you tried automated scaling solutions? Let's discuss below. Full article: https://lnkd.in/gVdcHuQ5 #ArtificialIntelligence #AIAutomation #EnterpriseAI #CloudComputing #TechInnovation

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Context engineering shifts LLMs from oracle to analyst—frame the task, curate tokens, and apply reusable patterns (RAG, tool calls, memory) to keep complex systems robust. A must-read for product and Agile teams building trustworthy AI-enabled workflows. Explore Chris Loy's take: https://buff.ly/Ca2GrrO #Product

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🚀 How to Evaluate LLM Applications Large Language Models (LLMs) are powerful, but their real impact depends on how we evaluate them. The right benchmarks and evaluation methods ensure accuracy, fairness, and real-world applicability. 🔍 Key Takeaways from the Framework: ✅ Benchmarks Language Understanding & QA: TruthfulQA, MMLU, DROP Common-sense & Reasoning: ARC, HellaSwag, BIG-Bench Hard Coding: HumanEval, CodeXGLUE Conversation & Chatbots: Chatbot Arena, MT Bench, Stanford HELM ✅ Evaluation Methods Character Level: Levenshtein Distance Word Based: BLEU, ROUGE, METEOR, Perplexity Embedding Based: BERTScore, MoverScore, QAG Score Language Model Based: GPTScore, SelfCheckGPT, G-Eval, Prometheus NLP Models: NLI, BLEURT Frameworks: DeepEval, RAGAS 💡 Whether you’re building chatbots, coding assistants, or reasoning systems, choosing the right benchmark + evaluation method is crucial to measure performance and reliability. 👉 Question for you: Which evaluation method do you think gives the most real-world reflection of LLM performance traditional metrics like BLEU/ROUGE or LLM-based evaluations like GPTScore & SelfCheckGPT? #LLM #AI #MachineLearning #NLP #Evaluation #GenAI #ArtificialIntelligence

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What are Just-In-Time Semantic Models??? My co-founder, Tal Segalov, shared a post now on a new concept Solid is pioneering: Just-In-Time Semantic Models (JIT-SM for short) The idea is simple: 1️⃣ Today, you either have one really big semantic model that you use to help AI access your data, or a lot of smaller ones (as Databricks and Snowflake recommend you do). 2️⃣ Either way, you need to maintain a ton of model information manually, and it gets stale very quickly. 3️⃣ As we announced last week, you can use Solid's platform to generate the model 💪 , but that means there are dozens or hundreds of models constantly maintained in your environment. 💥 So now, Solid can instead generate the semantic model on demand - when it is needed to generate SQL or pull certain data. This way, every request gets its own, highly-curated, model. The model can remain in the cache, or get thrown away after use. No more stale models. No more situations where models don't exactly fit your question, or your question spans multiple models (which isn't supported by the DWH's at the moment). Instead - the model your query needs is generated for it... just, in, time. 🎉 And we're starting with support for Snowflake's semantic models, and its Cortex Analyst capability. Check it out: https://lnkd.in/gJAe-gfq

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A much better set of inference benchmarks for the DGX Spark here including important TTFT values, llama.cpp seems to be the most efficient right now, I would also be interested in trying out LMDeploy. Full Benchmark found here: https://lnkd.in/grRxBbSk

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Execution Governance Framing The real risk with AI isn’t that it writes bad code. It’s that it writes convincing code that fails at fundamentals — and fails at execution time, not review time. We’re watching a shift most teams haven’t fully internalized yet: - AI has removed writing speed as a constraint - Review capacity is now the bottleneck - Execution risk is the new failure mode A system that can compile Linux but fails “Hello World” isn’t broken — it’s architecturally incomplete. It optimized for fluency, not admissibility. In regulated or high-risk environments, review alone does not scale: - Checklists don’t prevent unauthorized execution - Confidence doesn’t prove authority - Test coverage doesn’t equal consent What’s missing is execution governance. Not policy. Not prompts. Not post-hoc audits. Governance that is mechanically enforced at the moment of execution: - Is authority valid right now? - Is consent explicit, scoped, and current? - Is identity continuous and accountable? - If any answer is ambiguous — does the system refuse to act? This is the role of execution-time governance layers like Sopranus: human-speed interfaces that bind authority before irreversible actions occur, independent of how intelligent or confident the system appears. AI can build cathedrals overnight. Enterprises still own the doors, the locks, and the liability. The teams that win won’t be the ones who generate fastest — they’ll be the ones whose systems know when not to execute. #AIGovernance #EnterpriseAI #ExecutionRisk #AgenticAI #AIArchitecture #ResponsibleAI

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