Content by gaurav bhardwaj (4)

Gaurav Bhardwaj proposes a Microsoft Foundry + Azure AI Search architecture that preserves meaning when turning job descriptions into recruiter-reviewable Boolean searches, with structured extraction, terminology provenance, an explicit approval step, and evaluation/observability to catch errors like “preferred” skills becoming mandatory.

Gaurav Bhardwaj walks through a practical “refund agent” example to show why AI agent observability needs more than green HTTP checks, and how to use Microsoft Foundry plus Azure Monitor (Application Insights and Log Analytics) to trace runs, evaluate tool-call correctness, set alerts, and bake in security and privacy controls.

Dustin Ellis outlines practical ways to keep GitHub Copilot usage predictable under usage-based billing, focusing on token drivers like model choice, context size, and tool/agent usage. The post includes concrete prompting patterns, team guardrails, and lightweight policy ideas to reduce waste without losing the benefits of AI-assisted development.

Gaurav Bhardwaj describes a hybrid Azure-based document extraction architecture for construction drawings and project documentation, combining deterministic field extraction with bounded LLM verification. The post breaks down the event-driven pipeline, confidence gating, cost trade-offs, and the security controls needed to run this kind of document intelligence workflow in production.