Category Archives: AI & Machine Learning
Prompt optimization sounds like a shortcut for writing better instructions, but the useful version is more disciplined than “ask another model to rewrite the prompt.” Google’s Prompt Optimizer can search for improved instructions and examples against an evaluation objective, giving teams a repeatable way to compare prompt candidates instead of relying only on intuition. Google […]
Retrieval-augmented generation becomes operationally difficult after the first successful demo. The model call is usually the easy part. The harder questions are how documents are parsed, how chunks are created, which embedding model represents them, where vectors and metadata are stored, how retrieval is filtered, and how the evidence is passed into generation without losing […]
Generative AI data governance is not a single setting in Amazon Bedrock. It is the set of decisions that determine what data can enter a model workflow, which identities can access it, where intermediate artifacts are stored, what gets logged, how long evidence is retained, and which outputs are allowed to leave the system. A […]
Hallucination is not one measurable defect. A response can invent a fact, misstate a fact present in the source, answer a different question, combine two true facts into a false conclusion, or present an unsupported claim with high confidence. Treating all of those failures as a single “hallucination rate” produces a number that is easy […]
AWS Lambda is a strong backend component for generative AI when the workload is event-driven, bursty, and decomposable into short units of work. It can validate requests, retrieve configuration, call Amazon Bedrock, stream a response, transform model output, update state, or dispatch follow-up work without requiring a permanently running application server. The important design question […]
Amazon Bedrock model evaluation is useful when a team needs evidence for choosing, changing, or releasing a model. The service can run automatic evaluations, evaluations with an LLM as a judge, and human-based evaluations, while related Bedrock evaluation capabilities can assess retrieval-augmented generation systems. The important step is choosing the evaluation method that matches the […]
Generative orchestration in Copilot Studio is the planning layer that lets an agent decide how to use topics, tools, knowledge sources, and other agents in response to a request or event. Instead of forcing authors to predict every wording and hard-code every path, the orchestrator interprets intent, considers the descriptions and inputs of available capabilities, […]
Evaluation datasets give Microsoft Foundry teams a stable set of cases that can be rerun when a model, prompt, agent, tool, or orchestration policy changes. The important word is stable. Without a reusable test set, teams can demonstrate that a system works on today’s hand-picked examples but cannot tell whether tomorrow’s version quietly breaks a […]
Microsoft Foundry model benchmarks are useful because they reduce the first stage of model selection from guesswork to evidence. The Foundry model catalog exposes leaderboards and model-level benchmark results for selected models, allowing teams to compare quality, safety, throughput, and estimated cost before investing in workload-specific tests. The danger is assuming that a leaderboard is […]
The Microsoft Foundry model catalog is not just a list of model names. It is the discovery layer where architecture requirements begin to narrow the model field: provider, region, deployment option, lifecycle stage, inference task, supported features, and model-specific details can all affect whether a candidate belongs in the solution. The useful skill is therefore […]
Microsoft Foundry tool connections are where an agent’s reasoning meets external authority. A model can decide that it needs customer data, a search result, a calculation, or an operational action, but the connection determines how that tool is reached and which identity is allowed to use it. Foundry Agent Service supports built-in capabilities and custom […]
DynamoDB is a strong fit for some kinds of agent state because agent workflows often need a durable, low-latency record of facts that must survive process restarts: current workflow step, tool-call result, idempotency key, task status, user-to-session mapping, or a checkpoint that lets an execution resume safely. The important design decision is not “store agent […]
Amazon ECS and Amazon EKS can both run containerized AI services, but the important choice is not “which service supports AI?” Both do. The decision is which orchestration model best fits the platform team’s existing skills, portability requirements, operational controls, workload shape, and need for the Kubernetes ecosystem. AI adds specialized compute and scaling concerns, […]
Amazon Titan Text Embeddings V2 turns text into vectors that can be compared for semantic similarity in retrieval, search, clustering, classification, and related workflows. The model is simple to call, but embedding quality depends on more than the model endpoint. Chunk boundaries, dimensions, normalization, source language, vector-store configuration, metadata filters, and evaluation data all affect […]
Generative AI systems become operationally interesting when inference is only one step in a larger process. A document arrives, a customer record changes, a security finding is created, a batch completes, or a human approves a request; that event can trigger retrieval, classification, generation, tool calls, validation, and downstream updates. The useful architecture is therefore […]
