Skip to content

Harness Engineering

Definition
AI-generated

Harness engineering is the design of the software and control flow around a language model—tool APIs, validation, retries, logging, guardrails, evaluation harnesses, and deployment boundaries—so that model outputs are checked, constrained, and recoverable in real workflows rather than treated as one-off completions.

Topics
LLM and Agents
Related
Harness Human in the Loop (HITL) AI agent

Why it matters in GWAS

Automated QC, annotation assist, or agent-style pipelines need deterministic steps (file paths, column maps, significance thresholds) wrapped with tests and human checkpoints; the harness, not the prompt alone, determines whether mistakes are caught before they affect meta-analysis or reporting.

Example usage

"We spent more time on harness engineering than on prompt tweaks: the LLM proposes column mappings, but a Pydantic schema and diff against the header whitelist must pass before anything is written."

Harness Human in the Loop (HITL) AI agent Genome-Wide Association Study (GWAS) Large Language Model (LLM) Model Context Protocol (MCP) Prompt Engineering Context Engineering Tool Calling
Related-term network

Click any node to open that term page.

Node order. The current term is first. Next come every page linked from Related terms, in the same top-to-bottom order as that list. Then new terms appear in two-hop layers: for each of those direct neighbors (in that same order), their related terms are appended if not already listed. A third pass does the same for two-hop nodes. The walk stops when a fixed maximum number of nodes is reached.

References

  • Mialon G, et al. (2023). Augmented language models: a survey. arXiv:2302.07842.
← Harness Native Hawaiian or Pacific Islander →

Last updated (UTC · Git history)