Author Archives: Allen Rodriguez
Microsoft AI-103: Azure AI Search Semantic Ranking Semantic ranking in Azure AI Search applies a second-stage relevance model to the candidate results returned by keyword, vector, or hybrid retrieval. It cannot recover relevant content that the first retrieval stage excluded, and it does not replace indexing, chunking, or vectorization. In Microsoft AI Agents, semantic ranking […]
Microsoft AI-103: Prompt Shields for AI Apps Prompt injection is dangerous because an AI application mixes instructions and data inside the same reasoning context. A user can try to override system rules directly, while an attacker can place hidden instructions inside a document, email, web page, or tool result that the model later reads as […]
GitHub Copilot Public Code Filtering: Risks and Controls Public code filtering in GitHub Copilot is a governance control for a specific question: what should happen when a Copilot suggestion matches or closely resembles code that is publicly available on GitHub? The answer affects developer workflow, attribution review, and enterprise policy, but it does not make […]
Amazon AWS AIP-C01: Vector Search with Aurora PostgreSQL Vector search inside Amazon Aurora PostgreSQL can be attractive to AWS AIP-C01 application designers when relational data and semantic retrieval need to share consistent permissions, filters, and an operational model without a separate vector database. Aurora PostgreSQL supports the pgvector extension, and AWS documents using an Aurora […]
Amazon AWS AIP-C01: Secrets Management for AI Apps AI applications collect credentials at nearly every boundary: model providers, vector stores, relational databases, observability platforms, SaaS APIs, webhook destinations, and custom tools. In AWS AIP-C01 security design, removing avoidable long-lived secrets matters before choosing where to store the remainder. On AWS, those credentials should not be […]
Amazon AWS AIP-C01: SageMaker AI Endpoint Autoscaling Amazon SageMaker AI endpoint autoscaling turns model serving capacity into a feedback system, making the choice of real demand signals an important AWS AIP-C01 deployment decision. A production variant can add or remove instances as demand changes instead of forcing operators to choose one fixed instance count for […]
Amazon AWS AIP-C01: Testing RAG Quality on AWS A retrieval-augmented generation system can fail even when every component is technically healthy, making separately measured retrieval quality and answer grounding essential to AWS AIP-C01 evaluations. The vector store can return results, the model can produce fluent text, and the application can stay within latency targets while […]
Amazon AWS AIP-C01: Step Functions for Agentic Workflows Agentic applications are often described as loops—ask a model, call a tool, inspect the result, and continue—but AWS AIP-C01 production designs also need explicit state, retries, and recovery that can survive the failure of one in-memory process. Long-running approvals, retries, timeouts, parallel branches, compensating actions, and external […]
Amazon AWS AIP-C01: SageMaker AI Model Registry For AWS AIP-C01 model governance, Amazon SageMaker AI Model Registry is a control point for deciding which trained model artifact is allowed to move toward production, and which version and approval evidence supports that decision. Training systems can generate many candidate models, but production needs a smaller set […]
Claude vision is most useful when an image is part of a larger workflow rather than a one-off screenshot question. Production systems need to decide how images enter the request, how resolution affects cost, what metadata is retained outside the model, how results are verified, and when a human should review the interpretation. A Claude […]
Claude API capacity planning must account for spend limits and separate request and token-rate constraints, not just a monthly budget. Anthropic applies both spend limits and rate limits, and the rate limits are expressed across requests and token flow rather than one simple request-per-minute number. A reliable Claude Engineering rollout measures demand by arrival rate, […]
Amazon AWS AIP-C01: Prompt Injection Defenses on AWS For AWS AIP-C01, prompt injection is a control-boundary problem disguised as a language problem: untrusted text must not gain authority over tool permissions or application instructions. A user, retrieved document, tool result, or external webpage can contain text that tries to override developer instructions, reveal hidden prompts, […]
Amazon AWS AIP-C01: Private GenAI Networking on AWS Private networking for generative AI on AWS is less about hiding one endpoint and more about controlling the complete data path. A production request may begin in an application subnet, call Amazon Bedrock, retrieve documents from Amazon OpenSearch Service or S3, read credentials from Secrets Manager, use […]
Amazon AWS AIP-C01: OpenSearch Neural Search OpenSearch neural search changes the retrieval problem from “which documents contain these words?” to “which documents are semantically closest to what the user meant?” That distinction is important for generative AI because a RAG system can produce fluent output from poor evidence. In Generative AI on AWS, Amazon OpenSearch […]
Microsoft AI-103: Model Deployment Quotas in Foundry Model deployment quota in Microsoft Foundry is capacity planning expressed as an application constraint. A model may be available in the catalog and a deployment may be technically valid, yet the application can still fail under load because its assigned tokens-per-minute or request rate is too low for […]
