AI

Combine lexical and dense retrieval with rank fusion, metadata enforcement, and measured reranking instead of trusting one search signal.

Design RAG chunks around document structure and retrieval tasks, preserve authorization metadata, and evaluate boundary changes against a labeled query set.

Compare ChatGPT, Google Gemini, and Anthropic Claude by workflow, integrations, project context, coding, research, governance, and evaluation rather than benchmark headlines.

Control production LLM cost and latency with per-feature budgets, measured model routing, bounded outputs, cache-aware prompts, and quality regression checks.

Upgrade production AI models with versioned evaluation sets, shadow traffic, bounded canaries, compatibility checks, and an operational rollback path.

Evaluate retrieval-augmented generation by separating search recall, ranking, context quality, grounded answers, and production failure analysis.

Contain prompt injection in tool-using AI agents with trust labeling, least-privilege capabilities, deterministic authorization, approval gates, and adversarial tests.

Build a deterministic boundary around model-generated JSON with bounded parsing, schema checks, evidence validation, and application-owned decisions.

Use AI as an additional code-review tool for bounded diffs, test gaps, security, maintainability, and architecture while keeping human engineers accountable.

Build repository-level evaluations for AI coding agents using realistic tasks, executable checks, review rubrics, and failure analysis instead of demo impressions.