Engineering Notes
Thoughts and Ideas on AI by Muthukrishnan
Tag: Llm
Improving Decisions Through Multi-Agent Debate and Deliberation
How AI agents can reach better decisions by arguing with each other—exploring debate protocols, deliberation architectures, and the surprising power of constructive disagreement.Beam Search and Parallel Decoding for Multiple Solution Paths in AI Agents
Master beam search—a powerful technique for exploring multiple solution paths simultaneously in AI agents, from classical NLP to modern LLM reasoning systems.How AI Agents Learn from Themselves Through Self-Play and Iterative Refinement
Explore how AI agents improve through self-play and iterative refinement—from AlphaGo's game mastery to modern LLM self-correction techniques.How Transformers Power Modern AI Agents with Attention and Reasoning
Understand how attention mechanisms enable AI agents to reason, maintain context, and make intelligent decisions in modern LLM-based systems.Advanced Problem-Solving with Tree of Thoughts
## 1. Concept Introduction When you solve a simple math problem, you might follow a single, linear sequence of steps. ...Beyond the Training Data with Retrieval-Augmented Generation for AI Agents
## 1. Concept Introduction Imagine taking an exam. In a "closed-book" test, you rely solely on what you've memorized. ...How AI Agents Use Tools with Function Calling
## 1. Concept Introduction Think about a smart assistant like Siri or Google Assistant. When you ask, "What's the weat...Fusing Reasoning and Action in LLM Agents with ReAct
Welcome to our series on mastering AI agent programming. Today, we're exploring one of the most foundational patterns f...