AdalFlow

Software engineers can easily build LLM prototypes using ready-to-use libraries and SDKs. However, they often lack the AI-first approach needed to productionalize these demos. You should not implement anything more complicated without first having a dataset and a metric. AI is uncertain, experimental, and iterative in nature, spanning from data to implementation. We created two open-source projects to help software engineers make this transition: 1️⃣ adalflow.com: The "PyTorch" library for building and auto-optimizing LLM apps. 2️⃣ LLM Engineer Handbook: A curated list of resources to navigate the end-to-end LLM workflow. 👉 Star the repos, share, repost and follow 😀 Big release ahead on auto-optimizing complicated LLM applications. #adalflow #artificialintelligence #machinelearning #llms

You should not prioritize mastering advanced techniques like Query Expansion, HyDE, or complicated agent architectures. Here’s why: - Before moving to any advanced techniques, you have to measure your vanilla RAG performance, identify where the problems exist, and improve it as much as possible. There’s a lot of beauty in simple and effective solutions. - You only need a complicated solution when you know it will theoretically improve your baseline. There’s simply no value in learning advanced techniques—or it’s not effective—unless you need to solve a real problem and are actively searching for the best solutions. - Many tools or libraries won’t solve your problem just because they implement more complicated techniques. When it comes to LLM applications, you need to understand your use case and your data. Never use complicated solutions unless you have a solid reason. #adalflow #artificialintelligence #machinelearning #llms

Manual prompting should stay in 2024! 🤶I have been grinding hard in the last two weeks for AdalFlow's next release. Trainable agents, multi-hop RAG, RAG with cycle,  and any multi-node tasks you have built, along with *papers*! Also, I'm excited to be collaborating with Atlas Wang and many others such as Dr. Junyuan Hong, Fanqi Yan, Dejia Xu, Zixin Ding, Wuyang Chen, Neel P. Bhatt, Lasya Yakkala, Zhengheng Li, and Gabriel Jaco Perin to fully benchmark AdalFlow's optimization capabilities and to push the research to new heights. Which use case do you plan to optimize then? 👉 follow for the big release #adalflow #artificialintelligence #machinelearning #llms

I’ve rounded up some of the top LLM frameworks. I knew there were many, but this really puts things into perspective. It’s simultaneously never been easier to build, yet more difficult to know where to start / which tools will give you an edge. The reality is that it’s tough (almost impossible) to make this decision before you start, and it’s easier than you think to choose something that will actually slow you down in the long run. That being said, some really smart people have thought deeply about patterns and best practices when building with LLMs, and if you don’t end up going all in on one of these you should absolutely still reference their implementations to guide your own thinking. Some of my favourites: - Haystack (deepset) - Langraph (LangChain) - AdalFlow - CrewAI - Dynamiq Check it out: https://lnkd.in/ePfwcBH6

Get optimized high accuracy prompts in minutes with AdalFlow Struggling to get the most out of your LLM prompts? Stop tweaking them manually! With AdalFlow, you can automatically optimize prompts in just a few steps, saving time and improving accuracy for your tasks. In just three training steps and in a few minutes, I was able to turn a vague prompt into one that’s precise. It works for zero-shot, few-shot, or fully trained production-ready pipelines. Try optimizing your prompts with even more training steps using this notebook: https://lnkd.in/eTyfkpJg Star the AdalFlow repo: https://lnkd.in/enx2Kv3r Tell us what you optimized on Discord: https://lnkd.in/djxbbbBa #adalflow #artificialintelligence #machinelearning #llms #python

Two big risks to watch out for using LLM productivity tools. LLMs are amazing at memorizing stuff, but they’re pretty bad at understanding projects on a large scale or handling research tasks without clear specs. I’ve been using LLMs a lot to boost productivity, and here’s how I’d rate their usefulness: 1️⃣ Writing: ChatGPT is great for things like math formula in latex, and GitHub Copilot does well in sentence auto-completion. 2️⃣ AdalFlow library: Depends on the task. For well-defined stuff like graph traversal or boilerplate testing code, LLMs are helpful. But for research exploration and new features, I’m mostly on my own. 3️⃣ Frontend code: Since I’m less experienced with frontend, LLMs can be a real help here. That said, here are the two big risks to watch out for: 1️⃣ LLM hallucinations: They can introduce bugs and suboptimal code, so you always need to double-check and really understand what you’re doing. 2️⃣ Over-reliance: Using LLMs too much can make you less sharp over time. You’ve got to stay intentional, keep learning, and deepen your own understanding. What do you use LLM for? How does it impact your productivity? #artificialintelligence #machinelearning #llms

Manual prompting is not engineering, here is how to start with a more engineering based approach: Optimizing Task Pipelines with AdalFlow: A Deeper dive In this video, I go through the first parts of the code of the object counting optimization with AdalFlow. This should give you a bit of an understanding on how the task pipelines are build and all the components. You can go to the repo to read more about each component but by the end of the video you should get the general idea and also be ready to move on to the training part of the code. This is a simple task but it showcases the capabilities of the framework and what it can do. Here’s What We Cover: System Prompt: Learn how the task instruction is structured to guide the model through reasoning step by step. Few-Shot Demos: Understand the importance of providing a few examples to the model to teach it how to solve similar tasks efficiently. The Generator (llm_counter): Explore how the generator interacts with the model, processes the input and returns an answer. Evaluation vs. Training Mode: Evaluation Mode: We see how the model’s response is returned in the GeneratorOutput format, giving the final answer along with detailed reasoning. Training Mode: This mode outputs a Parameter, containing the raw_response for further optimization and improvement of the pipeline. Read more about the components, look at the full notebook, and message us on discord - link in the comments below #adalflow #artificialintelligence #machinelearning #llms #python

Intro to optimizing LLMs with textual feedbacks 💡 In this introductory video, I’ll be showing you an overview of LLM prompt optimizations with AdalFlow using Textual Gradients and a student teacher setup. This approach is similar to traditional backpropagation, but instead of numerical gradients, we use textual feedback to guide a cheaper student model using a stronger teacher model. I will dive deeper into each of these aspects in future posts, for now check out the following: AdalFlow repo: https://lnkd.in/enx2Kv3r Notebook to try the video example: https://lnkd.in/dZwWaiWp Text-Grad Paper: https://lnkd.in/dAZ8TwV5 Join the Discussion on Discord: https://lnkd.in/djxbbbBa #adalflow #artificialintelligence #machinelearning #llms #python

Stop thinking about prompts as static constructs. Prompt engineering is much closer to #machinelearning engineering than software engineering. Prompts should be treated more like a model artifact rather than a piece of code. For in-context learning use cases, LLM prompting is very sensitive: ➡️ The accuracy gap between the best and the worst-performing prompts can be as high as 40%.  ➡️ Accuracy can go down unexpectedly when the model changes. The future of LLM applications is in auto-prompt optimization - a process that is very similar to model training. I love AdalFlow library, which is inspired by PyTorch and makes it super simple to get started with building and optimizing #LLM applications. Check out this Google Collab notebook with a demo: https://lnkd.in/ejAxJNxR And support the project by giving a 🌟: https://lnkd.in/edTAKhWw

Stop thinking about prompts as static constructs. Prompt engineering is much closer to #machinelearning engineering than software engineering. Prompts should be treated more like a model artifact rather than a piece of code. For in-context learning use cases, LLM prompting is very sensitive: ➡️ The accuracy gap between the best and the worst-performing prompts can be as high as 40%.  ➡️ Accuracy can go down unexpectedly when the model changes. The future of LLM applications is in auto-prompt optimization - a process that is very similar to model training. I love AdalFlow library, which is inspired by PyTorch and makes it super simple to get started with building and optimizing #LLM applications. Check out this Google Collab notebook with a demo: https://lnkd.in/ejAxJNxR And support the project by giving a 🌟: https://lnkd.in/edTAKhWw