Introduction to AI Systems
Good morning, AI enthusiasts, this week’s issue is all about building AI systems that can recover. Whether it’s a query that needs re-routing, a retrieval step that missed the mark, or a policy model that overreacts to change, this issue is packed with techniques that keep things stable and smart.
What’s AI Weekly
This week, in What’s AI, the second free lesson from our 10-hour LLM Video Primer course is shared. In 2 hours, it discusses how to overcome the limitations of LLMs and harness their power through techniques such as RAG, fine-tuning, structured outputs, and more, following the typical path a company should take to achieve this. The key topics covered include:
- How Corrective RAG rewrites bad queries and re-searches for better docs
- How PPO adapts real-time pricing models in volatile environments
- How Adaptive RAG routes are based on complexity, and built with feedback loops
- And how to build LLM-powered financial doc retrieval using Gemini + LlamaIndex
Learn AI Together Community Section
The Learn AI Together Discord community is flooded with collaboration opportunities. If you are excited to dive into applied AI, want a study partner, or even want to find a partner for your passion project, join the collaboration channel. Some of the featured community posts include:
- Phantom_80757 has built an AI document assistant, which will be particularly useful for law firms and includes features such as a centralized legal document repository, context-aware legal review, AI-powered document generation, smart template completion, and case-based reasoning.
- The community also hosts a poll on what comes next for MCP, with 81% saying MCP is here to stay.
Collaboration Opportunities
The community has several collaboration opportunities available, including:
- gabrielayo is looking for a collaborator with outlier experience.
- Quixy8330 is launching a small team to develop and sell AI automation systems to local businesses.
- Franciszz is building a community of n8n automation devs and is looking for automation engineers who have built useful workflows.
TAI Curated Section
This section features some of the must-read articles, including:
- Proximal Policy Optimization in Action: Real-Time Pricing with Trust-Region Learning, which examines the application of Proximal Policy Optimization (PPO) for dynamic, real-time pricing.
- Corrective RAG: How to Build Self-Correcting Retrieval-Augmented Generation, which presents a method for improving traditional Retrieval-Augmented Generation.
- Building a Financial Report Retrieval System with LlamaIndex and Gemini 2.0, which provides a step-by-step guide for building a Retrieval-Augmented Generation (RAG) system for financial documents.
- Adaptive RAG: The Smart, Self-Correcting Framework for Complex AI Queries, which outlines an Adaptive Retrieval-Augmented Generation (RAG) framework to enhance the way AI systems respond to questions.
Conclusion
In conclusion, this issue of AI weekly is all about building AI systems that can recover and adapt to changes. It features several techniques and methods for improving the stability and smartness of AI systems, including Corrective RAG, PPO, and Adaptive RAG. The community section also highlights several collaboration opportunities and features some of the must-read articles on AI.
FAQs
Q: What is Corrective RAG?
A: Corrective RAG is a method for improving traditional Retrieval-Augmented Generation by integrating a self-correction mechanism that evaluates the relevance of retrieved documents.
Q: What is PPO?
A: PPO stands for Proximal Policy Optimization, which is a method for dynamic, real-time pricing that uses a clipped surrogate objective to maintain learning stability.
Q: What is Adaptive RAG?
A: Adaptive RAG is a framework that utilizes an intelligent routing system to analyze query complexity and select the most suitable data source, enhancing reliability with a feedback loop that grades retrieved documents.
Q: How can I get involved in the Learn AI Together community?
A: You can join the Learn AI Together Discord community and participate in collaboration opportunities, such as joining the collaboration channel or responding to community posts.
