Abstract
Recent breakthroughs in artificial intelligence (AI) are increasingly driven by systems orchestrating multiple large language models (LLMs) and other specialized tools, such as search engines and simulators. So far, these systems are primarily handcrafted by domain experts and tweaked through heuristics rather than being automatically optimized, presenting a substantial challenge to accelerating progress. The development of artificial neural networks faced a similar challenge until backpropagation and automatic differentiation transformed the field by making optimization turnkey. Analogously, here we introduce TextGrad, a versatile framework that performs optimization by backpropagating LLM-generated feedback to improve AI systems. By leveraging natural language feedback to critique and suggest improvements to any part of a system—from prompts to outputs such as molecules or treatment plans—TextGrad enables the automatic optimization of generative AI systems across diverse tasks. We demonstrate TextGrad’s generality and effectiveness through studies in solving PhD-level science problems, optimizing plans for radiotherapy treatments, designing molecules with specific properties, coding, and optimizing agentic systems. TextGrad empowers scientists and engineers to easily develop impactful generative AI systems.
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Fig. 1: Overview of TextGrad.
Fig. 2: Illustrative implementation of TGD and TextGrad’s backpropagation.
Fig. 3: Radiotherapy treatment plan optimization.
Fig. 4: Optimizing compound AI systems.
Data availability
We used publicly available data to evaluate TextGrad. Details on how to access the data is available at https://github.com/zou-group/textgrad.
Code availability
The TextGrad code and experiments are available at https://github.com/zou-group/textgrad and https://doi.org/10.5281/zenodo.14497017 (ref. [46](https://www.nature.com/articles/s41586-025-08661-4#ref-CR46 "Bianchi, F. et al. zou-group/textgrad: v0.1.6. Zenodo
https://doi.org/10.5281/zenodo.14497017
(2024).")).
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Acknowledgements
We thank D. Yilmaz, F. Dinc, B. Ergun, Y. Sun, I. Covert, K. Swanson, O. Faruk Akgun, Y. Efe, O. Khattab, K. Y. Wu, E. Wu, K. Vodrahalli, O. Pastor Serrano, P. J. Chia, J. Tagliabue, N. Thakkar, E. Simon, S. Eyuboglu, I. Gao, L. Chen and members of the Zou group and the Guestrin group for their support and comments on this work. This work was supported by funding from the Chan-Zuckerberg Biohub. C.G. was supported by funding from the Chan-Zuckerberg Biohub, Stanford HAI, AFOSR Grant FA9550-21-1-0397, gifts from Google and IBM.
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Author notes
These authors contributed equally: Mert Yuksekgonul, Federico Bianchi, Joseph Boen, Sheng Liu, Pan Lu, Zhi Huang
Authors and Affiliations
Department of Computer Science, Stanford University, Stanford, CA, USA
Mert Yuksekgonul, Federico Bianchi, Carlos Guestrin & James Zou
Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
Joseph Boen, Sheng Liu, Pan Lu, Zhi Huang & James Zou
Chan Zuckerberg Biohub, San Francisco, CA, USA
Carlos Guestrin & James Zou
Authors
Mert Yuksekgonul
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2. Federico Bianchi
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3. Joseph Boen
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4. Sheng Liu
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5. Pan Lu
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6. Zhi Huang
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Contributions
M.Y. and J.Z. conceptualized the research and led the overall project. M.Y. developed the primary codebase and led prompt optimization and solution optimization. M.Y., F.B. and J.B. designed the abstractions. F.B. led code optimization. J.B. led molecule optimization. S.L. led treatment planning optimization and compound system optimization. P.L. led solution optimization in multimodal settings and compound system optimization. Z.H. and C.G. advised the project. J.Z. supervised the project. All authors contributed to the preparation of the paper and approved the final version.
Corresponding authors
Correspondence to Mert Yuksekgonul or James Zou.
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Nature thanks Kai-Wei Chang and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.
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Extended data figures and tables
Extended Data Fig. 1 Molecule optimization via text.
TextGrad optimizes a starting benzene fragment to improve its druglikeness (higher QED) and binding affinity (lower vina score) to the protein receptor PPARA. The textual gradients for the first three iterations are shown in (a), and the performance of all ten iterations compared to clinically approved molecules targeting PPARA in (c). The molecule at the final iteration has low structural similarity with its most similar clinically approved counterpart, and better QED and Vina scores (d) with a highly plausible pose geometry shown in (e). Across 29 targets and three initial fragments, TextGrad successfully designs molecules with similar vina scores and greater QED scores than clinically approved molecules (b).
Extended Data Fig. 2 Comparing TextGrad to to other molecule optimization methods.
(a) Scaled mean top-5 AUCs for each method across all 58 protein targets. (b) Sample trajectories. For each algorithm, we selected the best performing trajectory to visualize, as measured by scaled Top-5 AUC, for the protein target listed, with the shaded error bars representing the standard error across the three repetitions (seeds/initial molecules). The blue star indicates the iteration at which TextGrad’s early stopping condition was triggered.
Extended Data Fig. 3 Code optimization details.
(a) We show the test-time objective we use for code optimization. (b) We report the results for LeetCode Hard using gpt-4o. We report the standard deviation over five random seeds in brackets.
Extended Data Fig. 4 Solution optimization details.
(a) Solution optimization for zero-shot question answering with gpt-4o. TextGrad outperforms the baselines consistently across different tasks. (b) We show the test-time objective objective we use for solution optimization. (c) We show an example where the mistake in the solution is identified in the test-time objective, and later the updated solution fixes the mistake.
Extended Data Fig. 5 Prompt optimization details.
(a) With TextGrad, we optimize a system prompt for gpt-3.5-turbo using gpt-4o as the gradient engine that provides the feedback during backpropagation. Supplementary Table 1 includes ablations with instruction-only and demonstration-only optimization with TextGrad. (b) We show an example of an optimized instruction for GSM8k. (c) We show an example of optimized in-context demonstrations for GSM8k.
Extended Data Fig. 6 Treatment planning details.
(a) We display the several dose metrics of the PTV target for all the clinical and TextGrad optimized plans, including the mean and minimum doses, as well as the D95. For all the metrics, we include the average deviations from the clinical goal across five plans and the standard deviation in brackets. Values in bold represent the best for each PTV target. (b) We show mean dose to capture OAR sparing. Lower values demonstrate better OAR sparing which is desirable, as this number indicates organs at risk, which should not get more than dosage than what is listed in the clinical guidelines. For all the metrics, we include the average mean dose across five plans and the standard deviation in brackets.
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This file contains Supplementary Notes 1–10, Figs. 1–4, Tables 1–3 and References.
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Yuksekgonul, M., Bianchi, F., Boen, J. et al. Optimizing generative AI by backpropagating language model feedback. Nature 639, 609–616 (2025). https://doi.org/10.1038/s41586-025-08661-4
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Received:12 June 2024
Accepted:16 January 2025
Published:19 March 2025
Issue Date:20 March 2025
DOI:https://doi.org/10.1038/s41586-025-08661-4
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