Research

Context-Aware QA is a prompting technique where an AI model is instructed to retrieve and cite authoritative sources before answering factual questions. By combining retrieval-augmented generation (RAG) with explicit verification instructions, it dramatically reduces hallucinations in production AI systems.

Builds production-ready retrieval-augmented generation pipelines with deliberate chunking strategies, embedding model selection, vector store configuration, hybrid search blending, and reranking so agents answer from your documents with reduced hallucination and cited sources. This skill focuses on the engineering decisions that separate a working prototype from a production-quality RAG system.

Curates, deduplicates, and formats training datasets for fine-tuning so that the resulting model actually improves on target behaviors rather than learning noise. Fine-tuning preparation covers dataset quality filtering, output format consistency, train/test splits, and avoiding common pitfalls like data leakage that invalidate fine-tuning results.

Crafts prompts with explicit task framing, role definition, output constraints, citation requirements, and few-shot examples so model responses are consistent, grounded in evidence, and actionable for downstream tasks. Prompt engineering reduces the variability and hallucination risk that comes from under-specified prompts.

Keeps AI assistant answers anchored to the actual library documentation, changelog, and typed signatures that are shipped rather than to memory or stale blog summaries. This is essential during major version bumps, unfamiliar SDK integration, or on-call hotfixes where confident but incorrect guesses about API behavior cause more damage than the original bug.

Systematically identifies threats to a system by mapping data flows, defining trust boundaries, and enumerating adversaries and misuse cases before shipping. This produces a security-focused diagram and prioritized mitigation list that makes subsequent security reviews faster and more substantive than starting from a generic checklist.

Checks whether a claim is backed by a primary source, a current official page, or a reputable secondary source before that claim becomes published copy. This skill is essential for AI tool directories, MCP server listings, and news summaries where accuracy and trustworthiness directly affect reader decisions and SEO credibility.

Explores goals, constraints, risks, and design options before committing to a specific implementation path. This technique is most valuable when facing product or UX decisions where the wrong choice is expensive to reverse—new features with uncertain user value, architectural pivots, or cross-functional dependencies where each team has a different mental model of the problem.

Prioritizes official OpenAI documentation, model cards, and API references when researching integration details, model capabilities, or API behavior changes. This avoids the noise and staleness of third-party blog posts that may summarize older model versions or incomplete information.