@lakshyaaagrawal
PhD @ucberkeleyofficial.bsky.social | Past: AI4Code Research Fellow @msftresearch.bsky.social | Summer @EPFL Scholar, CS and Applied Maths @IIITDelhi | Hobbyist Saxophonist https://lakshyaaagrawal.github.io Maintainer of https://aka.ms/multilspy
Show HN: Optimize_anything: A Universal API for Optimizing Any Text Parameter We built optimize_anything, an API that optimizes any artifact representable as text — code, prompts, agent architect...
Origin | Interest | Match
Show HN: Optimize_anything: A Universal API for Optimizing Any Text Parameter We built optimize_anything, an API that optimizes any artifact representable as text — code, prompts, agent architect...
Origin | Interest | Match
📰 GEPA Unveils 'optimize_anything': Universal AI API to Optimize Code, Prompts, and Agents
GEPA AI has launched 'optimize_anything,' a groundbreaking open-source API that uses AI-driven search to optimize any text-based artifact—from code and prompts to agent architectur...
#AINews #AI #Teknoloji
📰 optimize_anything: Tek API ile Kod, Promt ve Ajanları Otomatik Optimize Eden Devrim
GEPA AI tarafından duyurulan optimize_anything, artık kod, yapay zeka promtları ve sistem yapılandırmalarını tek bir API ile ölçüp optimize ediyor. Bu teknoloji, sad...
#YapayZekaAraçlarıveÜrünler #AI #Teknoloji
Stop what you are doing and try out GEPA now!
"GEPA: Reflective Prompt Evolution Can Outperform Reinforcement Learning" presents such elegant ideas by a collection of amazing researchers!
Here is a tldr of how it works:
GEPA (Genetic-Pareto) is a sample-efficient prompt optimization method for compound AI systems that works by reflectively evolving prompts using natural language feedback instead of traditional scalar rewards.
In each iteration, GEPA samples system rollouts (including reasoning traces, tool outputs, and any diagnostic text), reflects on them via an LLM to identify issues or propose improvements, and updates specific module prompts accordingly based on the feedback.
To ensure diversity and avoid local optima, GEPA maintains a pool of candidates and uses Pareto-based selection, which keeps all non-dominated strategies discovered so far and stochastically proposes new prompt variants, enabling robust generalization with far fewer rollouts than reinforcement
GEPA: prompt optimization can exceed RL performance
They used Qwen3-8B (which was not trained for math, coding, agency, etc.) and show that GEPA performed better than RL rollouts
paper: arxiv.org/abs/2507.19457
github: github.com/gepa-ai/gepa
DSPy docs: dspy.ai/api/optimize...
Automating Agentic Prompts: A new algorithm called GEPA, developed by researchers at UC Berkeley, Stanford, and other institutions, improves the performance of agentic systems by automatically refining their prompts.
AGI is just around the corner!
I'm learning to use DSPy with GEPA (Genetic-Pareto) prompt optimization. In GEPA a larger "teacher" LLM adjusts the prompt for a smaller "student" LM to perform a specific task as well as possible. The teacher will try many different prompts and evaluate the […]
Hey, would love to get any feedback on how you'd think about improving the interface
Just what I was looking for. Thank you for sharing, looking forward to the read.
propose and test prompt updates, and combine complementary lessons from the Pareto frontier of its own attempts.
arxiv.org/abs/2507.19457
DSPy folks love GEPA, so here's a GEPA paper for anyone who wants to learn more.
Given any AI system containing one or more LLM prompts, GEPA samples system-level trajectories (e.g., reasoning, tool calls, and tool outputs) and reflects on them in natural language to diagnose problems,
ArXiv page 7
..GEPA and prompt optimization explained: https://arxiv.org/abs/2507.19457v1
(7/7)
ArXiv page 6
..make adapting large models more practical—especially when compute or data is limited. It’s like giving AI a way to learn from its own “thinking out loud,” turning natural language into a powerful tool for self-improvement.
Links:
Paper on arXiv: https://arxiv.org/abs/2507.19457 ..
(6/7)
ArXiv page 5
..code on the fly.
What’s cool here is the shift from treating AI tuning as a blind search for a higher score to a reflective process that leverages the AI’s native strength: language. By evolving prompts through thoughtful reflections, GEPA unlocks smarter, faster learning that could..
(5/7)
ArXiv page 4
..fewer attempts than traditional reinforcement learning methods. On several tough tasks like multi-step question answering and instruction following, GEPA consistently outperforms both standard reinforcement learning and previous prompt optimizers. It even shows promise for optimizing..
(4/7)
ArXiv page 3
..strategies by mixing and matching what works best.
GEPA treats AI prompt tuning like a conversation with itself, iterating through generations of prompts that learn from detailed feedback written in words, not just numbers. This lets it learn much more efficiently—up to 35 times..
(3/7)
ArXiv page 2
..what went wrong and how to fix it? That’s the idea behind a new approach called GEPA. Instead of relying solely on those sparse reward signals, GEPA has AI inspect its own attempts using natural language reflections. It diagnoses errors, proposes prompt fixes, and evolves smarter..
(2/7)
ArXiv page 1
What if language itself could teach AI to get better, faster?
Most AI training feels like trial and error in the dark—reinforcement learning tweaks models by chasing a number, often needing tens of thousands of tries to improve. But what if the AI could actually *talk to itself* about..
(1/7)
gepa 0.0.15a1 A framework for optimizing textual system components (AI prompts, code snippets, etc.) using LLM-based reflection and Pareto-efficient evolutionary search.
Origin | Interest | Match
New research released today from Databricks shows how its GEPA (Generative Evolutionary Prompt Adaptation) technique improves prompt optimization by an order of magnitude.
venturebeat.com/ai/the-usd10...
🚀 #GEPA: Automatic #Prompt Optimization by @databricksinc.bsky.social: gpt-oss-120b beats Claude Sonnet 4 (+3%) at ~20x lower cost. Completes with DSPy SIMBA/MIPROv2
📜 MIT lic
🔗 Link in first 💬⤵️
Repost 🔁 #AI #LLM #RAG #PromptEngineering #ContextEngineering
