Ravena O

Ravena ONov 3, 2026

Why use an expensive LLM for every task when a smaller model could do the job just as well? Classifying a support ticket and debugging a production outage require different levels of intelligence. Yet many AI applications send both requests to the same model. The solution? LLM routing: choosing the right model for each task based on complexity, cost, latency, and accuracy. 🟢 How LLM Routing Works Before the model call: Check the cache, apply privacy and budget policies, and classify the request. During execution: Route the request to the most suitable model. 🟢 5 Types of Models to Consider → Reasoning models: Complex problem-solving and multi-step planning. → Small language models: Classification and information extraction at scale. → Multimodal models: Images, documents, charts, and audio. → Coding models: Code generation, debugging, and refactoring. → Fast inference models: Real-time chat and latency-sensitive applications. After the response: Evaluate quality and use a stronger model when necessary. 🟢 Resources to Explore 📌 LiteLLM: Model routing, fallbacks, and load balancing https://lnkd.in/gKzJTRBg 📌 OpenRouter: Access and compare models through a unified API https://openrouter.ai/ 📌 LangChain: Build LLM-powered applications https://lnkd.in/gA4XFC87 📌 LangGraph: Build stateful AI workflows https://lnkd.in/gzntC3VT 📌 Hugging Face: Explore open-source models https://lnkd.in/gzWnxgmi 📌 MLflow: Track experiments and evaluate model performance https://lnkd.in/g6Ae9hA2 🟢 What Should You Measure? → Cost per successful request → Response latency → Accuracy and quality → Cache hit rates → Fallback frequency The goal isn't to use the cheapest model for everything. It's to use the least expensive model that meets your quality requirements. Question for AI engineers: Does your team use an LLM router, or do you send every request to the same model? Save this post for your next AI architecture project. CC: Prem Natrajan #AIEngineering #LLM #GenerativeAI #AIInfrastructure #MachineLearning

Ravena ONov 6, 2026

Is your AI agent equipped to make decisions, or does it simply know how to retrieve information? Knowing the facts, understanding the current situation, and deciding what to do next are three different challenges. This is where three types of graphs come together to make AI agents more context-aware, reliable, and actionable. Let's understand with a simple example: "Rebook this passenger." 🟡 Knowledge Graph — What is true? Connects structured enterprise knowledge such as passengers, flights, fares, routes, and policies. It provides the trusted information an AI agent needs to make decisions. 🟡 Context Graph — What matters right now? Identifies the information relevant to the current situation. For example: → Gate C8 → Four seats available → Nine minutes before boarding closes → Outcomes from similar past situations Instead of processing everything, the agent focuses on what matters for this specific decision. 🟡 Procedural Graph — What should happen next? Defines how the agent should move through a workflow. Check Availability → Compare Alternatives → Verify Visa Requirements → Resolve or Escalate to a Human. Rather than relying entirely on static prompts, procedural graphs represent workflows and relationships between actions. 🟡 Why does this combination matter? The agent can reason using relevant context, follow defined procedures, and adapt when circumstances change. Execution traces can also help improve the system: → Past decisions improve future context. → Failed executions reveal workflow gaps. → Repeated patterns can become governed enterprise knowledge. My takeaway: Enterprise AI agents need more than access to information. They need the right knowledge, the right context, and a structured way to act. And every decision should be traceable. 📚 Want to learn more? Here are some FREE courses: 🟡 Neo4j Fundamentals — Understand graph databases 🟡 Building Knowledge Graphs with LLMs — Build graphs from unstructured data 🟡 Neo4j & Generative AI Fundamentals — Learn Knowledge Graphs, RAG and GraphRAG 🟡 LangChain Academy — Learn to build AI agents and workflows 🟡 Microsoft Learn — Introduction to Building AI Agents Which of these three graphs is missing from your AI agent architecture today? More AI agent insights → https://lnkd.in/gatGAt9X Govern your AI agents → https://lnkd.in/gqbqM5V3 Save, share, and follow for more practical AI agent insights. CC: Rakesh Gohel #AIAgents #AgenticAI #GenerativeAI #EnterpriseAI #AIGovernance #KnowledgeGraphs #ContextEngineering

Ravena ONov 2, 2026

What if your AI system could answer questions without searching a vector database every single time? That’s where CAG comes in. While RAG retrieves relevant information at query time, CAG preloads knowledge into the model’s context. This difference can impact latency, data freshness, scalability, and cost. I’ve animated both pipelines side by side so you can see how they work. 👇 🟡 RAG (Retrieval-Augmented Generation) Every question triggers a retrieval process: → The query is converted into an embedding. → A vector database retrieves relevant information. → Relevant chunks are filtered and added to the prompt. → The LLM generates an answer grounded in the retrieved context. → Additional validation can check the response for quality and accuracy. Best for: Large knowledge bases, frequently updated data, and enterprise applications requiring targeted retrieval. 🟡 CAG (Context-Augmented Generation) Instead of retrieving knowledge for every query, the system preloads relevant information into the context. → Knowledge is prepared and loaded before queries arrive. → The model uses the available context to answer questions. → Responses can be validated for quality and consistency. → When the underlying knowledge changes, the context or cache may need refreshing. Best for: Stable knowledge, repeated queries, and use cases where the required information fits within the model’s context window. 🟡 The real trade-off: → Latency: CAG can reduce retrieval overhead, while RAG adds a retrieval step. → Freshness: RAG can access updated data when its index is current; CAG may require context refreshes. → Scalability: RAG supports large knowledge bases, while CAG is constrained by context capacity. → Complexity: RAG requires retrieval infrastructure; CAG shifts complexity toward context preparation and cache management. 🟡 What works best in production? Sometimes, the answer is both. A hybrid architecture can preload frequently used instructions and stable knowledge while using RAG to retrieve changing information or content that exceeds the context window. The right choice depends on your data, latency requirements, context limits, and evaluation results. Remember: Faster responses mean little if the answers are outdated or incomplete. Question for AI engineers: If your entire knowledge base fits within the context window, would you still use RAG, switch to CAG, or combine both? Save this for your next discussion on RAG, context engineering, or production AI architecture. CC: Brij Kishore Pandey #ArtificialIntelligence #RAG #LLM #GenerativeAI #AIEngineering #DataEngineering