LLMs can generate feedback on their work, use it to improve the output, and repeat this process iteratively.
Appears on
Quick read
Latest capture 2026-08-20 03:05
0 paths
Agent instructions and tool configuration found in this repository.
No config files detected.
6 observed captures since 2026-05-27. Observed captures are shown by default.
Stars from first capture +15
Observed captures only
All tracked data
Observed snapshots
Observed snapshots
Nearest indexed repositories by embedding similarity.
Benchmarking large language models' complex reasoning ability with chain-of-thought prompting
LLM Finetuning with peft
TextGrad: Automatic ''Differentiation'' via Text -- using large language models to backpropagate textual gradients. Published in Nature.
Prompt Engineering | Prompt Versioning | Use GPT or other prompt based models to get structured output. Join our discord for Prompt-Engineering, LLMs and other latest research
A framework for prompt tuning using Intent-based Prompt Calibration
Rigourous evaluation of LLM-synthesized code - NeurIPS 2023 & COLM 2024
