AI and ML engineering
Building with models: LLM apps, RAG, evals, agents, training and MLOps.
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- Build an MCP server
Implements a Model Context Protocol server exposing the given tools and resources, with input validation, least privilege and error messages a model can act on. Use to connect a system to AI clients.
- Build an LLM structured extraction step
Builds an LLM step that turns documents into schema-valid JSON, with the schema, prompt, validation and repair loop, null handling and an eval set. Use when automating invoices, forms or emails.
- Choose between rules, ML and an LLM
Recommends rules, classical ML, a hosted LLM or a fine-tuned model for a problem, comparing accuracy, cost, latency and maintenance with the reasoning shown. Use before committing to an approach.
- Design an LLM agent architecture
Designs an LLM agent system, deciding first whether an agent is needed, then single or multi-agent, tools, memory, guardrails, human checkpoints, evals and cost limits.
- Design a RAG pipeline
Designs a retrieval-augmented generation pipeline from a corpus and its real questions, covering chunking, hybrid retrieval, reranking, citations and evals. Use before building or rebuilding RAG.
- Design tool definitions for an LLM agent
Designs tool or function definitions for an LLM agent, with names, descriptions, JSON Schema parameters and error returns that models call reliably. Use when exposing an API or capability to an agent.
- Machine-learning engineer
Acts as a machine-learning engineer who starts from the data and a baseline, insists on evals and reproducibility, and distrusts any gain a simpler model explains.
- Plan a fine-tuning project
Decides whether fine-tuning beats prompting or retrieval for a task and, if it does, plans the data, splits, training settings, evaluation against a prompt baseline, and cost.
- Plan a machine-learning experiment
Plans a machine-learning experiment before any training code exists: framing, baselines, leak-proof splits, metrics, ablations and a stop rule. Use when starting a new model or modelling spike.
- Reduce LLM costs and latency
Cuts an LLM feature's cost and latency through prompt trimming, caching, model routing, batching and output limits, each paired with the quality check that proves nothing regressed.
- Review a training dataset sample
Audits a sample of a labelled dataset for label noise, leakage, duplicates, class imbalance and representation gaps, and gives a concrete fix for each problem. Use before training or fine-tuning.
- Write an eval suite for an LLM feature
Writes an eval set for an LLM feature with golden, edge and adversarial cases, graders matched to each criterion, and pass thresholds. Use before shipping or changing a model, prompt or pipeline.
- Write a model card
Writes a model card with intended use, training data, metrics by slice, limitations and ethical considerations from training notes and eval results, flagging gaps. Use before releasing a model.
- Implement LLM tool calling
Implements tool calling in an LLM feature with tool schemas, a dispatch loop, argument validation, timeouts, limits and safe error handling. Use when wiring a model to functions or APIs.
Not: prompts for using an assistant (prompting domain); coding-agent setup (meta).