freeCodeCamp·1yHow to Write Effective Prompts for AI Agents using Langbase
Learn how to write effective prompts for AI agents using Langbase. The post covers essential techniques such as defining clear goals, experimenting with prompts, using specific instructions, and applying advanced methods like few-shot training, memory-augmented prompting, and role-based prompting. Practical tips and a step-by-step guide for using Langbase to build serverless AI agents are also included.
Daily Dose of Data Science | Avi Chawla | Substack·1y12 Powerful Tools For AI Agents
A comprehensive guide listing 12 powerful tools included in the CrewAI framework for building AI agents. The tools range from file reading and writing, code interpreting, and web scraping to advanced functionalities like RAG-powered searches and natural language to SQL conversion. Additionally, the post highlights a full crash course on AI agents, covering everything from fundamentals to production optimization.
Daily Dose of Data Science | Avi Chawla | Substack·1y5 Levels of Agentic AI Systems
Agentic AI systems are capable of making decisions, calling functions, and running autonomous workflows. The levels of AI agency include basic responders, router patterns, tool calling, multi-agent patterns, and fully autonomous patterns. Each level indicates a different degree of independence and capability of the AI system.
Medium·1yAI Agents are not Ready Yet
AI agents need more robust development tools as current options are not mature enough for effective debugging and collaboration in real-time. Companies should focus on building flexible systems integrating multiple AI capabilities rather than betting on single trends. Advanced areas like language understanding and task automation are maturing, but challenges such as pinpointing errors, maintaining security, and ensuring inspectability persist.
Daily Dose of Data Science | Avi Chawla | Substack·1yAI Agent Crash Course—Part 7
The AI Agent Crash Course Part 7 focuses on providing knowledge for building Agentic systems, covering various aspects such as memory, agentic flows, and design patterns. It aims to fully equip readers with the ability to build autonomous AI systems that can reason, plan, take actions, and correct themselves when necessary. The course is practical and beginner-friendly, covering the fundamentals and beyond.
Daily Dose of Data Science | Avi Chawla | Substack·1yAI Agent Crash Course—Part 4
The AI Agent Crash Course—Part 4 deep dives into building robust AI agents, covering fundamentals, memory, agentic flows, guardrails, design patterns, and optimization. It explores how AI agents can access, retrieve, filter data, and make real-time decisions, going beyond the capabilities of traditional RAG systems to unlock full autonomy in AI systems.