hodios-ai-ml

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.

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.

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.

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.

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.

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.

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.

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.

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.