Research

Structures verification of headline private-market AI funding rounds into an evidence checklist for strategy, finance, and partnerships teams. The workflow separates announced valuation, round size, lead investors, previously committed capital, and revenue run-rate figures from independently confirmable filings or issuer press releases. It cites CNBC reporting on May 28, 2026 that Anthropic announced a $65 billion Series H at a $965 billion valuation led by Altimeter Capital, Dragoneer, Greenoaks, and Sequoia Capital—including $15 billion of previously committed investments with $5 billion from Amazon—surpassing OpenAI's reported $852 billion valuation after its March funding round, while Anthropic cited a $47 billion revenue run rate and releases of Claude Opus 4.8 and Claude Mythos Preview—without treating media valuations as internal planning numbers.

Structures verification of public claims about AI-driven memory shortages, high-bandwidth memory (HBM) demand, and trillion-dollar memory-chip valuations into an evidence checklist for finance, procurement, and platform teams. The workflow separates analyst price-target moves, year-to-date equity rallies, and vendor statements about agentic-AI workloads from independently observable supply signals (long-term agreements, stated capacity constraints, peer pricing power). It cites CNBC reporting that Micron crossed a $1 trillion market cap on May 26, 2026 after UBS raised its price target from $535 to $1,625, and that SK Hynix joined the trillion-dollar club on May 27, 2026 with shares up roughly 250% year to date amid AI chip demand lifting South Korea's Kospi—without endorsing any single stock call.

Turns public semiconductor announcements into a verification checklist when vendors claim novel scaling laws, stacked logic architectures, or nanometer-class equivalence without independent benchmarks. Teams separate marketing nomenclature from manufacturing readiness by demanding yield, thermal, packaging, and third-party validation evidence—patterns highlighted when CNBC reported Huawei's LogicFolding and τ Scaling Law claims alongside analyst skepticism about true 1.4nm-class process breakthroughs without EUV access. The skill also maps export-control context (ASML EUV restrictions) and competitive implications for GPU vendors operating in constrained geographies.

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.