Category Archives: Technology
Safety filters are one of the most misunderstood parts of a generative-AI architecture because teams often treat them as a universal “safe mode.” In practice, the Gemini API exposes configurable content-safety controls for defined harm categories, while Google also applies non-configurable protections for certain prohibited content. The application chooses thresholds for supported configurable categories and […]
Databricks materialized views sit between ordinary SQL views and hand-built ETL tables. Instead of executing the defining query from scratch every time a consumer reads it, a materialized view stores precomputed results and refreshes them as upstream data changes. That makes it useful for repeated aggregations, dashboard-serving datasets, compliance transformations, and other workloads where predictable […]
“Vertex AI Search grounding” now spans two generations of Google Cloud naming. In April 2026 Google renamed Vertex AI Search to Agent Search on Gemini Enterprise Agent Platform. The console and some APIs still expose older names, and the retrieval object used in Gemini requests can still be labeled VertexAISearch. That makes current architecture work […]
Table maintenance is easy to postpone because the symptoms arrive gradually: more small files, slower scans, stale statistics, higher storage use, and increasing time spent deciding when to run maintenance commands. Databricks Predictive Optimization addresses that operational burden for Unity Catalog managed tables by deciding when maintenance is useful and automatically running the appropriate work. […]
Interactive notebooks create a familiar infrastructure problem: analysts and engineers want compute immediately, but long-lived clusters create idle cost, configuration drift, and a queue of platform tasks that have little to do with the code being written. Databricks serverless compute for notebooks moves that infrastructure lifecycle behind the service boundary so a notebook can attach […]
Amazon AWS AIP-C01: GenAI Data Governance on AWS 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 […]
Streaming tables are designed to process new data incrementally, but incremental execution does not eliminate maintenance. A production streaming table accumulates checkpoints, data files, refresh history, schema assumptions, and dependencies that must remain coherent over time. When a definition changes or source retention no longer matches recovery expectations, the choice between an ordinary refresh, checkpoint […]
Amazon AWS AIP-C01: Hallucination Evaluation on AWS 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 […]
Cloud cost becomes difficult to manage when teams can see a monthly total but cannot connect it to the jobs, users, notebooks, products, or data products that created the spend. Databricks system billing tables make that attribution much more concrete by exposing account usage as queryable operational data. The central table, system.billing.usage, records billable usage […]
Amazon AWS AIP-C01: Lambda for GenAI Backends 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 […]
Planning Copilot Studio Generative Orchestration Generative orchestration in Microsoft Copilot Studio changes the agent from a collection of manually routed topics into a planner that can select multiple tools, topics, knowledge sources, and other agents to satisfy a request. Current Microsoft guidance describes this as the standard harness: an LLM-driven layer interprets intent, fills inputs […]
Microsoft AI-103: Foundry Evaluation Datasets Microsoft Foundry evaluation datasets provide a reusable test asset for measuring model or agent quality across prompt, model, and agent versions. Current Foundry guidance describes JSONL as the common reusable format and supports turn-level and conversation-level evaluation, uploaded/versioned datasets, inline data, synthetic generation, trace-derived datasets, and evaluation directly from stored […]
Microsoft AI-103: Foundry Model Benchmarks Microsoft Foundry model leaderboards help teams compare models across quality, safety, throughput, estimated cost, and scenario-specific benchmarks, but current Microsoft documentation labels the leaderboard feature as preview and warns against treating preview capabilities as production guarantees. For Microsoft AI agents, the leaderboard is best used to narrow candidates and understand […]
Microsoft AI-103: Foundry Model Catalog The Microsoft Foundry model catalog is a discovery surface for Microsoft, partner, and open-source models with model cards, task and modality information, benchmark data, licensing details, and deployment options. Current Foundry guidance also distinguishes serverless API and managed-compute deployment paths, with some models supporting instant access. For Microsoft AI agents, […]
Microsoft AI-103: Foundry Tool Connections 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 […]
