Ravi Rajamiyer

Struggling to ensure your LLM performs reliably in production? Here is an interesting article from my wife Sonakshi Singh This framework turns SME-labeled ground truth into a scalable evaluation pipeline. Goal Classify Q&A pairs into Correct, Incorrect, and Partially Correct in a live, production-grade system. Challenges 1.SMEs have limited availability and often provide only partial ground truth, without offering rationale for every record. 2.Understanding the rationale behind SME responses at scale is difficult. 3.Once we understand these rationales, the challenge is to use them to classify new Q&A pairs into Correct, Incorrect, or Partially Correct. Multistep Solution Step 1 – Use LLMs to extract the rationale behind SME responses. The LLM will classify them into the top 5 rationales (labels) with confidence scores. To achieve this, we provide all the available ground truth to the LLM and ask it to derive the top 5 rationales with confidence scores that illustrate why SMEs classified the pairs as they did. Step 2 – For each new Q&A pair, pass these rationales with confidence scores to another LLM and ask it to classify the pair into Correct, Incorrect, or Partially Correct. This, however, is a simplified view—using only 5 rationales and associated confidence scores per Q&A pair. In reality, a production-grade system would involve many more rationales and millions of Q&A pairs, resulting in very high token consumption and cost. Step 3 – To reduce dependency on an LLM-based classifier, we could build an in-house multi-class classifier. By combining the ground truth with LLM-generated labels and confidence scores, we can train this classifier to predict Correct, Incorrect, or Partially Correct. Step 4 – For each new live Q&A pair, use the LLM only to generate rationales (mapped to the predefined 5 labels) along with confidence scores. Step 5 – Pass this enriched dataset as input to the in-house multi-class classifier, which will then predict Correct, Incorrect, or Partially Correct. Conclusion With this approach, combining LLMs (to generate rationales) and a traditional ML classifier (to perform scalable classification), we can efficiently classify outputs into Correct, Incorrect, and Partially Correct at scale, while also creating a feedback loop to continuously improve performance. Closing the Gap: Production-Grade Evaluation Frameworks for RAG https://lnkd.in/eMJv8JNU

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🚀 𝗣𝗿𝗼𝗴𝗿𝗲𝘀𝘀 𝗨𝗽𝗱𝗮𝘁𝗲: llm-d 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 𝗶𝗻 KServe 🚀 Scaling LLM serving brings challenges far beyond a single deployment or Kubernetes’ default autoscaling. That’s where llm-d comes in — a Kubernetes-native inference framework purpose-built for 𝗹𝗮𝗿𝗴𝗲-𝘀𝗰𝗮𝗹𝗲 𝗟𝗟𝗠 𝘄𝗼𝗿𝗸𝗹𝗼𝗮𝗱𝘀. Our team at 𝗥𝗲𝗱 𝗛𝗮𝘁 𝗔𝗜 have been working closely with both the KServe and llm-d communities to introduce a new 𝗟𝗟𝗠𝗜𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲 CRD in KServe — a unified API that delivers a consistent serving experience across use cases and maturity levels. This will help enterprise users navigate their generative AI journey with less friction and more scalability. 𝗦𝘂𝗯𝘀𝗰𝗿𝗶𝗯𝗲 𝘁𝗼 𝗼𝘂𝗿 𝗻𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿: https://lnkd.in/g64Pz8KZ 𝗥𝗲𝗰𝗲𝗻𝘁 𝗽𝗿𝗼𝗴𝗿𝗲𝘀𝘀 𝗶𝗻𝗰𝗹𝘂𝗱𝗲𝘀: ✅ Reconciliation logic for single-node deployments ✅ Support for reconciling managed HTTPRoute resources ✅ Unified configuration management via LLMInferenceServiceConfig merge logic ✅ Base configurations for llm-d inference services ✅ Multiple storage backend support (S3, PVC, OCI) for single-node scenarios ✅ Production-ready deployment with GIE EPP & InferencePool ✅ Model discoverability in LLMInferenceService for better automation & observability ✅ End-to-end encryption for all key llm-d traffic pathways 💡 The result: a more robust, production-grade LLM serving experience that bridges KServe’s rich ecosystem with llm-d’s scalability-first design. Big thanks to everyone contributing code, reviews, and ideas — this integration is shaping up to be a game-changer for 𝗞𝘂𝗯𝗲𝗿𝗻𝗲𝘁𝗲𝘀-𝗻𝗮𝘁𝗶𝘃𝗲 𝗟𝗟𝗠 𝘀𝗲𝗿𝘃𝗶𝗻𝗴. Stay tuned for next release! #KServe #llmd #GenerativeAI #MLOps #Kubernetes #ModelServing #AIInfrastructure

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Stop defaulting to REST for every microservice you build. 🛑 6 foundational API architectures govern the modern web. Choosing the wrong style doesn't just impact latency; it severely cripples system scalability. Here is exactly when to use each architecture to optimize performance and prevent system bottlenecks: 👇 1️⃣ REST (Representational State Transfer) 🌐 The undisputed, ubiquitous backbone of web services. ▪️ Mechanism: A stateless, resource-oriented design utilizing standard HTTP verbs (GET, POST, PUT, DELETE). ▪️ Best For: Public-facing APIs, standard CRUD operations, and any application requiring native HTTP caching at the edge. 2️⃣ GraphQL 🎯 The definitive cure for over-fetching and under-fetching. ▪️ Mechanism: A highly flexible query language allowing clients to request the exact data schema they need from a single, unified endpoint. ▪️ Best For: Mobile applications and complex UI dashboards where payload precision and reduced network bandwidth are absolutely paramount. 3️⃣ gRPC ⚡ The powerhouse protocol for low-latency, high-throughput systems. ▪️ Mechanism: Extremely efficient binary data serialization via Protocol Buffers, transmitted over an HTTP/2 multiplexed connection. ▪️ Best For: Internal microservice communication, real-time streaming, and highly distributed systems requiring strict data contracts. 4️⃣ WebSockets 🔄 The standard for true real-time, bi-directional interactivity. ▪️ Mechanism: Establishes a persistent, full-duplex TCP connection between the client and server after an initial HTTP handshake. ▪️ Best For: Live chat platforms, high-frequency financial trading dashboards, and multiplayer online gaming where instant data updates are required. 5️⃣ Webhooks 🎣 The "Reverse API" powering modern event-driven architecture. ▪️ Mechanism: The server automatically pushes data via an HTTP POST request the moment a specific event occurs, completely eliminating inefficient client polling. ▪️ Best For: CI/CD pipelines, third-party payment notifications, and seamless system automation. 6️⃣ SOAP (Simple Object Access Protocol) 🛡️ The legacy heavyweight of enterprise-grade security and reliability. ▪️ Mechanism: A strict, standardized XML-based protocol requiring rigidly defined contracts (WSDL). ▪️ Best For: Highly regulated environments (core banking, healthcare) requiring advanced security mechanisms (WS-Security) and ACID-compliant transactional integrity. 💡 Architectural Takeaway: Robust modern systems rarely rely on a single style. An enterprise platform might utilize REST for its public gateway, gRPC for lightning-fast internal microservice chatter, and Webhooks for asynchronous third-party billing integration. Which API architecture is currently dominating your tech stack? Are you migrating toward gRPC or sticking with the reliability of REST?💬👇 #SoftwareEngineering #APIArchitecture #SystemDesign #Microservices #TechTrends

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If agentic systems can generate code (or content, workflows, configs) at near-zero marginal cost, then “more output” cannot be the north star. The stock(https://lnkd.in/gXYbUxTr) will explode. The real design question becomes: how do we build agentic systems where stock health — not inflow velocity — is the primary objective? Here’s a simple framework. First, separate Flow Optimization from Stock Optimization. Most agents today are optimized for flow: number of PRs, tasks closed, tokens generated, features shipped. A stock-health system explicitly measures the condition of the accumulated asset — cohesion, complexity, defect density, infra cost, latency, security exposure, change failure rate. If it’s code, track architectural entropy and long-term maintainability. If it’s data, track lineage integrity and semantic consistency. What gets measured becomes the system’s gravity. Second, embed a Reflexive Feedback Loop. Every act of generation must create a shadow of evaluation. Agents shouldn’t just write — they should simulate impact: dependency ripple, test coverage delta, performance implications, cost drift. In systems terms, generation must automatically trigger balancing signals. If complexity crosses a threshold, refactoring tasks are auto-created. If coupling increases, modularization is incentivized. The agent is rewarded not just for adding, but for simplifying. Third, align incentives around Net Stock Quality, not Gross Output. Reward deletion. Reward consolidation. Reward reduction in cognitive load. An agent that removes 1,000 lines of unnecessary code should score higher than one that adds 10,000. This flips the reinforcing loop. Instead of “more code → more work → more code,” you get “more complexity → more pruning → restored equilibrium.” Finally, introduce a Steward Layer — human or meta-agent — whose sole mandate is long-horizon coherence. Not velocity. Not tickets closed. But architectural narrative. This layer ensures that local optimizations by agents don’t fragment the system over time. Think of it as system memory and taste. In short: build agents that generate, evaluate, prune, and remember. When stock health becomes the objective function, reinforcement and balancing loops coexist intentionally — and abundance becomes leverage instead of entropy. #systemsthinking #ai #agenticai #systems

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