AI Engineering

Provides a systematic approach for measuring LLM feature quality, including building evaluation sets, selecting metrics, using LLM-as-judge, regression testing, and production evaluation. Use before iterating on prompts or models to avoid guesswork.

Use when integrating an LLM API into an application. Covers streaming, retries and rate limits, timeouts, caching, fallback across providers, and the production concerns that a tutorial integration ignores.

Use when building a Model Context Protocol server. Covers tool, resource, and prompt design, transport choice, authentication, error handling, and testing against a real client.

Helps select the right LLM for a task by evaluating capability, cost, latency, and routing strategies. Use when choosing a model for a new feature, reducing costs, or evaluating model migrations.

Write and improve prompts for language models. Covers instruction design, few-shot examples, reasoning elicitation, output formatting, and systematic failure diagnosis.

Use when building retrieval-augmented generation. Covers chunking, embedding and hybrid search, reranking, grounding and citation, and diagnosing whether a bad answer is a retrieval failure or a generation failure.

Use when an LLM must return machine-readable data. It covers schema design, native structured-output modes, validation and repair, and extraction from messy input.

Multi-subagent manuscript system for food and nutrition science covering literature review, data analysis, statistics, figure building, drafting, and journal formatting.

Orchestrates the complete food & nutrition research-to-publication pipeline, routing tasks to specialist AI skills with quality checks. Use when you need to manage a paper from research through submission and revision.

Performs deep, source-validated literature reviews on agricultural questions using a 12-subagent AI system with rigorous source screening and academic integrity checks.

Conducts comprehensive literature and evidence synthesis for agricultural science, acting as a senior agricultural scientist. Use for in-depth research, literature reviews, evidence briefs, or systematic reviews.

Multi-reviewer peer-review system for agricultural manuscripts. Simulates an editorial panel of domain reviewers and a formatting check against the target journal.