Author Archives: Allen Rodriguez
Amazon AWS AIP-C01: LLM Evaluation and Regression Testing Large language model testing becomes difficult the moment a team asks a simple question: did this release get better? A new model, prompt, retrieval setting, tool schema, or guardrail can improve one class of requests while quietly degrading another. Because output is probabilistic, a few impressive examples […]
Amazon AWS AIP-C01: AI Guardrails and Content Safety Content safety is often described as a filter placed after a model. That picture is too simple for a production generative AI system. Unsafe behavior can enter through the user prompt, retrieved documents, tool results, model output, or the application’s own downstream actions. A single classifier at […]
Amazon AWS AIP-C01: Bedrock Agents Classic Beyond Architecture Diagrams Agent diagrams usually show a model in the middle, tools on one side, and a knowledge base on the other. The diagram is useful, but it hides the hard parts: how state is preserved, how permissions are scoped, how tool failures are handled, how intermediate steps […]
Amazon AWS AIP-C01: Amazon Bedrock Knowledge Bases A retrieval system is easy to draw as three boxes: documents, a vector store, and a model. Production retrieval is harder because the quality of the final answer depends on every transformation between those boxes. Documents must be ingested, parsed, chunked, embedded or indexed, filtered, retrieved, ranked, inserted […]
Amazon AWS AIP-C01: Agentic AI Orchestration An agent that can only generate text is mostly an inference problem. An agent that can choose tools, call APIs, retrieve private data, wait for results, retry, and decide what to do next is an orchestration problem. The model may provide the reasoning signal, but production behavior emerges from […]
Amazon AWS AIP-C01: Prompt Management at Application Scale A prompt can begin life as a few lines in a notebook and end up controlling behavior across thousands of production requests. The technical risk appears in the transition. Once a prompt affects real users, it is no longer disposable text. It becomes a versioned application artifact […]
Amazon AWS AIP-C01: Foundation Model Selection and Routing Choosing a foundation model is rarely a one-time benchmark exercise. A production application has to balance quality, latency, cost, context capacity, modality, tool use, safety behavior, regional availability, throughput, and the consequences of a model change. The model with the strongest score on a public benchmark can […]
Amazon AWS AIP-C01: Vector Database Design “Use a vector database” is not a design decision. It is a category label that hides the choices that determine retrieval quality, operational burden, cost, and failure behavior. In a production generative AI system, the important questions are how many vectors must be stored, how quickly they change, how […]
Amazon AWS AIP-C01: Embeddings and Semantic Similarity An embedding is a numerical representation designed so that items with related meaning can be compared mathematically. In RAG systems and contextual generative AI assistants, that sentence is the beginning of the explanation, not the end. The operational value comes from understanding which model produced the vector, what […]
Amazon AWS AIP-C01: Enterprise RAG Chunking Chunking looks trivial in a Retrieval-Augmented Generation diagram: take a document, split it into pieces, create embeddings, and store them. In enterprise content, that small box determines whether the retrieval system preserves the meaning users actually need. The wrong chunk boundary can separate a requirement from its exception, a […]
Amazon AWS AIP-C01: From Prompt to Production Generative AI on AWS now spans much more than choosing a foundation model and sending it a prompt. Production systems need model access, tool execution, identity, memory, retrieval, observability, evaluation, guardrails, vector search, deployment automation, and cost controls that can survive real users. Amazon Bedrock provides model and […]
Cisco 200-301: Controller-Based Networking Software-defined networking is often summarized as “separating the control plane from the data plane.” That phrase is useful and incomplete. Traditional networks already contain distinct control and forwarding functions. What controller-based networking changes is where decisions are coordinated, how much network state is centralized or abstracted, and how operators express intent […]
Cisco 200-301: REST APIs and Network Automation Network automation is often taught from the tool backward: write a script, call an API, parse JSON, and push a change. Production systems work better when the reasoning runs in the opposite direction. Start with a desired network state, identify the authoritative data and the devices that must […]
Cisco 200-301: QoS Queuing Basics Quality of Service is often introduced with a misleading shortcut: important traffic gets priority. That statement is directionally true and operationally incomplete. A network cannot prioritize traffic until it can identify which traffic belongs to which class, decide where that classification should be trusted, and determine what behavior should occur […]
Cisco 200-301: First-Hop Redundancy: Operational Trade-offs A default gateway is easy to draw as one box. In production, that box can become one of the most consequential single points of failure in an access network. Hosts may have redundant switches, multiple uplinks, resilient servers, and diverse WAN paths, yet a single failed gateway address can […]
