A simple idea is rarely enough to build a real product. AutoCoder’s Prompt Optimization turns a basic concept into a complete product plan: → clearer structure → richer features → fewer wasted iterations From one rough idea to a more buildable product. #autocoder #PromptOptimization #AIProduct

Prompt Optimization Techniques that I learnt from my work linkedin.com/pulse/prompt-op… #PromptEngineering #LLM #GenAI #AIOptimization #PromptOptimization #AIinBusiness

Now companies building GEPA as a service! In short Prompt Optimization as service. Not a bad idea if there is a demand and there are use cases .. Come on @SuperagenticAI get into the game along with @driaforall #GEPA #PromptOptimization #AgentOptimization #SuperOptiX

Thank you for the shoutout Raja! This strategy of doing PromptOptimization (such as with GEPA) -> RL (Arbor) is exactly what we described in the mmGRPO paper! arxiv.org/abs/2508.04660 The idea of prompt optimization -> weight optimization is introduced as "BetterTogether" in a paper by @dilarafsoylu @ChrisGPotts @lateinteraction and others here: arxiv.org/abs/2407.10930

Have you heard of GEPA? 🔥 GEPA has quickly become one of the most popular prompt optimization techniques in AI—and through our collaboration with @DSPyOSS, #MLflow now makes it easier than ever to apply GEPA to your agents. MLflow brings GEPA to where your traces and eval datasets already live—no code change needed. We tested it on @OpenAI Agents with the HotpotQA dataset and saw a 14% accuracy boost in just 30 minutes! 🚀 📖 Read the full guide: mlflow.org/blog/mlflow-promp… #opensource #oss #ai #GEPA #DSPy #PromptOptimization

🎥Video: My talk from @PromptEngConf London🇬🇧 is now live! (Not really video but audio with slides) 🎤 “Beyond Handcrafted Prompts: The Era of Prompt Optimization.” which covered 🧠 DSPy → @DSPyOSS turns prompts into programmable modules 🧬 GEPA → evolves prompts through reflection and mutation 🌐 Context Engineering → because context is the new compute 🤖 Agent Optimization → You need optimize everything in Agentic system like RAG, MCP Tools, Memory not just prompts. Special mention to @hammer_mt at the end for his amazing talk on DSPy GEPA in London Agentic AI Meetup. Amazing discussion with @JacquesVerre from @Cometml on end to end Agent Optimization with industry standard optimizers! Amazing conference indeed. 📹 Link to the Talk 👇 #DSPy #GEPA #PromptOptimization #ContextEngineering.

Iterate your prompts like code. Version 1: Basic request. Version 2: Add context. Version 3: Specify format. Version 4: Add examples. Version 5: Perfect output. Nobody gets it right the first time. Prompt engineering IS iterative development. 🔄 #IterativePrompting #PromptOptimization #AIWorkflow #ContinuousImprovement

🌉 Come on, Bay Area: Visit Hyatt Regency, Burlingame, San Francisco on 30th October at ODSC AI Expo 🌉 🎤Join my talk on Agent Optimization @_odsc ODSC AI Conference Let me show how can you use DSPy for context engineering and powerful advance optimizers like GEPA in SuperOptiX: end to end optimization of Agentic AI pipelines to optimize you prompts, RAG, MCP tools and memory. The Optimization layer of Agentic AI that you never seen before that goes beyond Context Engineering Brought to you by @SuperagenticAI 👇 #AgenticAI #AgentOptimnzation #PromptOptimization #DSPy #GEPA

Join My Talk on Prompt Optimization at World's First ever Prompt Engineering Conference @PromptEngConf Canary Wharf, London along with amazing speakers from Google, Microsoft, AWS, IBM and many more.. I will be covering Prompt Optimization with @DSPyOSS GEPA and highlights of new kid "Agentic Context Engineering" in my talk. I also discovered Meenatchi Sundari also talking about DSPy & @JacquesVerre from @Cometml talking about other propmt optimizers too.. It's going to be fun day! Conference Link Below 👇 #DSPy #GEPA #PromptOptimization #AgentOptimization

Yesterday I delivered my talk on “Beyond Handcrafted Prompts: The Era of Prompt Optimization.” in the pre-conference meetup at Metro bank Office as a trailer to upcoming main talk in the conference @PromptEngConf in London 🇬🇧 Prompt Optimization with DSPy and GEPA was highlight of the talk. Why Prompt Optimization is important now than ever and journey towards Agent Optimization. 🧠 DSPy: turns prompts into programmable code 🧬 GEPA: evolves prompts through reflection and mutation 🌐 Context Engineering: Because context is the new compute Thanks to everyone who joined and shared bright ideas. Slides of the trailer talk below 👇 #PromptOptimization #GEPA #DSPy #AgentOptimization

🐛 Systematic Debugging & Optimization: Fix & Improve! Diagnose poor outputs: Check for ambiguity, test iterations, measure metrics like accuracy. Use audits: Refine via checklists, add reflections. Continuous improvement—log errors, iterate prompts. Save hours debugging! Share your debug tip below. #AIDebugging #PromptOptimization

Automated prompt optimization (GEPA) can push open-source models beyond frontier performance on enterprise tasks — at a fraction of the cost! 🔑 Key results from our research @DbrxMosaicAI: 1⃣ gpt-oss-120b GEPA beats Claude Opus 4.1 on Information Extraction ( 2.2 points) — while being 90× cheaper to serve. 2⃣ The same technique also lifts frontier models (Claude Sonnet 4, Opus 4.1), pushing them to new SOTA benchmarks. 3⃣Versus Supervised Fine-Tuning (SFT): GEPA delivers equal or better performance at 20% lower serving cost. Even better → GEPA SFT together gives the highest gains. 4⃣Lifetime cost analysis shows GEPA gpt-oss is orders of magnitude cheaper overall. At scale, the one-time optimization overhead fades away — making optimized agents highly practical for real-world deployment. #dspy #gepa #promptoptimization #airesearch

🚀 Unleash the Power of Evolutionary AI with EvoAgentX! We’re thrilled to announce a game-changing feature in EvoAgentX: the EvoPrompt Optimizer. This isn’t just another tweak — it’s a leap forward in how multi-agent workflows can improve themselves. 🔥 What makes it different? The EvoPrompt Optimizer introduces the power of evolutionary algorithms into the heart of AI workflows. Instead of static prompts, your agents can now evolve, adapt, and compete — leading to stronger performance with each iteration. •🧬 Two classic algorithms: Genetic Algorithm (GA) & Differential Evolution (DE) •🔁 Automatic prompt optimization across multi-agent workflows •⚡ Parallel evolution & combination optimization across multiple nodes •📊 Built-in detailed logging & clear training charts to track progress Minimal manual setup is needed — you choose parameters like population size and iterations, and the optimizer takes care of the heavy lifting. 📊 Real Results on BIG-Bench Hard We put EvoPrompt Optimizer to the test on one of the most challenging benchmarks, and the numbers speak for themselves: • ruin_names: Accuracy jumped from 0.5150 → 0.7400 (DE), a 43.7% boost • snarks: Both GA and DE achieved 16.5% improvements • multistep_arithmetic_two: Even with a strong baseline, EvoPrompt still delivered 3–4% gains • geometric_shapes: DE reached 7.6% improvement, showing robustness across task types Each run produces summary logs and visual charts — making optimization progress easy to understand and compare. ✨ Why this matters The EvoPrompt Optimizer is more than an optimizer. It’s a new mindset: workflows are no longer fixed scripts, but adaptive systems that learn to get better over time. This opens the door for more resilient AI agents that can adapt across tasks, industries, and rapidly changing environments. Whether you’re optimizing sarcasm classifiers with multi-prompt voting ensembles, or running challenging reasoning tasks, EvoPrompt gives you the edge. ⚙️ How to try it yourself 1. Getting started is straightforward: Define your workflow (for example, a three-prompt voting program, where each prompt evolves independently). 2. Register your prompt nodes with ParamRegistry. 3. Pick your optimizer: GA or DE, each with configurable parameters like population_size, iterations, combination_sample_size, and concurrency_limit. 4. Run & monitor: EvoPrompt handles the optimization and generates logs and charts for transparency. The full tutorial includes runnable code, environment setup, and even a complete working example (evoprompt_workflow.py) you can use today. 👉 Explore the full tutorial here: EvoPrompt Optimizer Tutorial: github.com/EvoAgentX/EvoAgen… 🌱 Let your prompts evolve to win. With EvoAgentX’s EvoPrompt Optimizer, your AI workflows won’t just run — they’ll adapt, improve, and thrive. Acknowledgment: Our implementation builds on EvoPrompt (Qingyan et al.), re-implemented in EvoAgentX with permission. We follow the Microsoft Open Source Code of Conduct.opensource.microsoft.com/cod… 📎 Original repo: github.com/beeevita/EvoPromp… & github.com/microsoft/EvoProm… #EvoAgentX #EvoPrompt #GeneticAlgorithm #DifferentialEvolution #PromptOptimization #MultiAgent #LLM #OpenSource #AIInnovation

🧠 Most bad AI responses aren’t the model’s fault. They’re the prompt’s. That’s why MemSync just rolled out Prompt Optimization — and it’s a gamechanger. We’ve all been there: You ask your AI assistant for help… and get some generic, shallow, robotic response. But here's the truth: your model didn’t fail. Your prompt wasn’t clear. 🚀 Prompt Optimization solves this by: Analyzing your input and restructuring it into high-signal instructions Leveraging your personal memory via MemSync to add precision and context Delivering radically better answers — instantly No prompt engineering skills. No advanced syntax. Just smarter conversations with every assistant you use. Whether you’re chatting with ChatGPT, Claude, Grok, or any LLM — MemSync now supercharges your inputs on the fly. Your memory. Your language. Your optimized prompt. Try it now 👉 memsync.ai New tweet → x.com/memsync_ai/status/1960… Chrome Extension → chromewebstore.google.com/de… @memsync_ai @OpenGradient @andrey_versus @gemspecialist01 @K_Gass @kurlyk27 @0x_Nate1 #PromptOptimization #MemSync #AIUX #LLM #SmartAgents #OpenGradient #DePIN #OnChainAI #web3explorers #crypto #OnchainCulture #NFTCommunity #Web3Art #CryptoCreatives #OROdrop #DeFi #Crypto #CryptoTikTok #CryptoYouTube #CryptoNews #Bitcoin #Ethereum #Solana #Blockchain #Altcoins #Metaverse #BullRun

Attacks and Defenses Against LLM Fingerprinting - arxiv.org/pdf/2508.09021 As large language models are increasingly deployed in sensitive environments, fingerprinting attacks pose significant privacy and security risks. We present a study of LLM fingerprinting from both offensive and defensive perspectives. Our attack methodology uses reinforcement learning to automatically optimize query selection, achieving better fingerprinting accuracy with only 3 queries compared to randomly selecting 3 queries from the same pool. Our defensive approach employs semantic-preserving output filtering through a secondary LLM to obfuscate model identity while maintaining semantic integrity. The defensive method reduces fingerprinting accuracy across tested models while preserving output quality. These contributions show the potential to improve fingerprinting tools capabilities while providing practical mitigation strategies against fingerprinting attacks. Authors: Kevin Kurian, @oeschsec, @ethanbholland #LLMFingerprinting #AIAttacks #ModelIdentification #PromptEngineering #AIPrivacy #AdversarialAI #Cybersecurity #ModelFingerprinting #ReinforcementLearning #AIThreats #AIVulnerabilities #PromptOptimization #AIModelSecurity #ModelAttribution #LLMSecurity #AIForensics #AIReverseEngineering #FingerprintingDefense #AIAttackVectors #ModelObfuscation

Boost Your Skills with AI Prompt Optimization Think your prompts are good enough? Most people miss the psychology behind AI prompt optimization. #Dcret #tradingpsychology #AItips #promptoptimization #tradertransformation

💡 Promptomatix beats manual existing frameworks Across QA, math, summarization, and classification: ✅ Better performance ✅ Shorter prompts ✅ Lower compute cost It even adapts to your constraints: quick search vs heavy search = instant vs thorough optimization. #PromptEngineering #PromptOptimization #LLM #AI #DSPy #FewShotLearning #NLP #AIBenchmarking

The secret to a perfect AI prompt? A simple two-step process: 1️⃣ Smart Analysis (to find what's weak) 2️⃣ Powerful Optimization (to make it strong) Our tool, PrompTessor, does both for you in seconds. Watch how it works! 👇 #AI #PromptEngineering #PromptOptimization

Can we build high-accuracy, low-latency text-to-SQL systems without runtime retrieval? We show the answer is yes! 🔍 We introduce Iterative Prompt Optimization (IPO) — a framework where LLMs refine instructions and exemplars offline to create a static, database-specific prompt for production use, without requiring retrieval at inference time. 💡 Key results: ✅Outperforms strong retrieval-based baselines (e.g., MIPROv2, RES) ✅ Achieves 59.2% execution accuracy on BIRD ✅ Reduces prompt size by ~70% (from 23K to 6.5K tokens) ✅ Supports multi-objective tuning via latency-augmented BIRD-MULTI By eliminating runtime retrieval, IPO delivers faster, simpler, and more reliable NL2SQL pipelines — better suited for real-world deployment. 📄 Read the paper: megagon.ai/publications/effe…, accepted at SIGMOD’s NOVAS Workshop taking place this Sunday, June 22nd. @eserkandogan @subZero_saj #NL2SQL #LLMs #PromptOptimization #SIGMOD2025 #birdmulti #DatabaseAI #TextToSQL #AIResearch #RAG #NLP #NLG2SQL #AI #MachineLearning

Just crossed 1,500 users on Promptimize AI! 🎉 We are doing for AI what Grammarly did for writing - one click transforms basic prompts into expert-level queries. Our mission is making sure EVERYONE can be on the winning side of this AI revolution. #AIForAll #PromptOptimization