Category Archives: Amazon
Amazon AWS AIP-C01: IAM Design for Generative AI Workloads IAM design becomes more difficult in generative AI because one user request can cross many service boundaries. A chat request may invoke a foundation model, retrieve private documents, call a Lambda function, write to a database, publish an event, and record traces. The temptation is to […]
Amazon AWS AIP-C01: Private Data and Model Access Private data and model access are often presented as two configuration screens: where the documents live and which models are enabled. In a production generative AI system, they are one trust-boundary problem. A user request can cause data retrieval, prompt assembly, model inference, logging, tool calls, and […]
Amazon AWS AIP-C01: Latency Tuning for AI Applications Latency tuning for AI applications becomes useful only when the team can explain where the time goes. Generative AI request paths are long: authentication, prompt assembly, retrieval, routing, model queueing, time to first token, token generation, tool calls, post-processing, and network transfer can all contribute. Optimizing the […]
Amazon AWS AIP-C01: Generative AI Cost Controls and Trade-offs Cost control for generative AI fails when teams begin with a cheaper model before they understand what is consuming money. A workload can be expensive because prompts are oversized, outputs are verbose, requests are retried, retrieval returns too much context, traffic is poorly attributed, or the […]
Amazon AWS AIP-C01: GenAI Observability Generative AI observability fails when a team treats the model as the whole application. A user sees one answer, but the production path may include retrieval, prompt assembly, model routing, safety checks, tool calls, queues, databases, and post-processing. If the answer is slow, expensive, unsafe, or wrong, a single “model […]
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 […]
