Skip to content

Chain-of-Thought (CoT)

Definition
AI-generated

Chain-of-thought (CoT) prompting asks a language model to emit intermediate reasoning steps before a final answer—often improving multi-step problems in math, logic, or structured extraction—at the cost of longer generations and sensitivity to prompt phrasing.

Topics
LLM and Agents
Synonyms
CoT prompting Chain-of-thought prompting
Related
Genome-Wide Association Study (GWAS) In-Context Learning Large Language Model (LLM)

Why it matters in GWAS

Structured pipelines (field mapping, ontology alignment, or allele checks) sometimes use CoT-style scratchpads; outputs should still be validated against schemas and primary data because verbose reasoning can look plausible while wrong.

Example usage

"We added a CoT block that lists column harmonization steps before emitting the JSON, which reduced stray delimiter errors in the trait table."

Genome-Wide Association Study (GWAS) In-Context Learning Large Language Model (LLM) Prompt Engineering
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

  • Wei J, et al. (2022). Chain-of-thought prompting elicits reasoning in large language models. NeurIPS.
← Chain File Chalcolithic Age →

Last updated (UTC · Git history)