SINAPSA Infocomplex·22h agoGet your LLM search for-and-activate by itself the Tools that it needs[NOTE: This post is starting similarly with our post here — about building a simple LLM agent that gets busy filling its day]
Yelkin Arslan·Oct 20LLM Uygulamalarında RAG ve Tool-Calling: Doğru Mimariyi Nasıl Seçeriz?Herkese yeniden merhaba!
Adithya Garigipati·Oct 13Building a Chatbot: 3 Lessons I Learned About Prompts and Tool CallingWhile building my Doctor Assistant chatbot using Python, FastAPI, OpenAI’s Responses API, and PostgreSQL, I learned something important…
Archie Arya·Sep 27I Built an Agent That Decides Where to Look for an AnswerI’ve been spending a lot of time recently building agentic workflows, mainly because I wanted to understand what actually changes when you…
Saad·Aug 24Fine-Tuning Meta’s Muse Glimmer 30B for Function Calling — Entirely on One GPUA local LoRA run that teaches a 30B agentic model to emit exactly the tool call your parser expects, and nothing else.
Laraib Ishtiaq·Aug 3Building Guardrails for Enterprise AI: Keeping AI Systems Safe and ReliableLarge Language Models are remarkably capable.
Rauf Ahmad·Jul 27The Hidden Complexity of AI Agents: Context, Memory, Tool Calls, and Failure HandlingIf you’ve watched enough AI demos recently, it’s easy to believe that building an AI agent is mostly about connecting an LLM to a few tools…
Bimal Sharma·Jul 18RAG Explained Simply: Why LLMs Need External KnowledgeA practical guide to Retrieval-Augmented Generation (RAG), fine-tuning trade-offs, and building grounded LLM apps with LangChain.
Divyaant Kumar Jain·Jul 18Your AI Agent Doesn’t Need More Retries — It Needs a DiagnosisPALADIN: The Research-Backed Framework That Teaches AI Agents to Diagnose Tool Failures, Not Just Survive Them
