Genkit Agents API, ORA, Python AI Explainer: New Tools for Workflow Automation
Genkit Agents API, ORA, Python AI Explainer: New Tools for Workflow Automation Today's Highlights This week, Google's Genkit ships a powerful Agents A…
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From Documents to Intelligent Answers: Building a RAG Agent from Scratch & Lessons Learned
Artificial Intelligence applications are rapidly moving beyond simple question-answering systems. Modern enterprise AI assistants need to understand i…
Your RAG Eval Isn't Flaky. Your Retrieval Is Non-Deterministic.
Same query. Same documents. Same model. And the RAG eval can still hand back a different Recall@8. Not because the model is flaky. Because of an ORDER…
Building a Robust RAG Pipeline Architecture for Production
Answer up front: A RAG pipeline architecture is a set of connected services that ingest raw documents, turn them into embeddings, store them in a vect…
How to parse lots of PDFs and more into markdown, with Laravel
You want to ask questions about your own documents from a Laravel app. The AI SDK that shipped with Laravel 13 handles most of it: embeddings, vector …
A Folder of Docs Is Not a Knowledge Base
Disclosure up front: I build agentproto and its corpus tooling, which is what the walkthrough uses. The commands are real and checkable; the problem i…
DoorDash RAG Architecture, AI Agent Mesh, & Open-Source Supply-Chain Scanner
DoorDash RAG Architecture, AI Agent Mesh, & Open-Source Supply-Chain Scanner Today's Highlights This week, we explore advanced AI agent orchestrat…
The benchmark that built the tools
How we measured a markdown knowledge graph as agent memory — lost to grep, rebuilt our search and editing primitives, retracted our own best number, a…
RAG - Meta Filtering and Reranking
Generally, when a user asks a query, the system searches for the relevant chunks stored in the vector database using cosine similarity. The better we …
Documents Aren't Bags of Chunks
One thing kept bothering me about retrieval systems: they often destroy the document before trying to understand it. Imagine taking a technical specif…
Anatomy of a Full RAG Application: Every Concept, One Self-Hosted Stack
"Chat with your documents" sounds simple. Then you build it, and you discover a good RAG system is really eight systems wearing a trench coat. I recen…
RAGFlow + MCP: Turning Your Best RAG Config Into a Production Assistant
You've found your best RAG settings. Now how do you turn them into a real assistant your team uses every day? In my previous post I covered how tools …
AI Agents & Workflows: Local Deployment, Label Orchestration, Cloud Enablement
AI Agents & Workflows: Local Deployment, Label Orchestration, Cloud Enablement Today's Highlights This week highlights innovative approaches to AI…
Quantified Self 2.0: Stop Guessing Your Health History—Build a Personal Medical Vector Database
Let's be real: our personal medical history is a mess. It’s a chaotic mix of PDF lab results, grainy scans of prescriptions, and cryptic Electronic Me…
Azure AI Search in 2026, how to build a RAG pipeline
Most RAG tutorials online show the same pattern: take a document, split it into chunks, generate embeddings, store them in a vector database, and when…
AI Agents: Memory Layers, Test Automation, and Workflow Orchestration
AI Agents: Memory Layers, Test Automation, and Workflow Orchestration Today's Highlights This week's highlights dive deep into critical aspects of AI …
Debugging a Python "Memory Leak" That Was Actually a Measurement Bug (ru_maxrss vs VmRSS)
I was running a pipeline for building a RAG knowledge base: crawl web articles, split them into chunks, create embeddings, and push them into Qdrant. …
RAG from Scratch with ChromaDB (No LangChain Required)
Frameworks like LangChain are great for moving fast, but they also hide a lot of what's actually happening under the hood. If you want to understand R…
Bigger Context Windows Didn't Make Our RAG Smarter
We stopped measuring retrieval quality by how many tokens we could fit into the prompt. When long-context models became available, many of us made the…
Stop Digging Through PDFs: Build a FHIR-Standard EHR Knowledge Base with RAG
We’ve all been there: staring at a stack of printed lab results or a folder full of cryptic report_final_v2_NEW.pdf files, trying to remember if our c…
What I learned building a compliance answer engine that refuses to make things up
Swiss business compliance is a genuinely hard information problem. There is no national minimum wage, VAT rules live at the federal level, social-insu…
Stop Making Your AI Agent Smarter. Make Its Context Smaller.
One of the most common questions I hear is: "Which LLM should we use?" It's an understandable question. Model capability matters. But after spending t…
Building a Four-Tier Parallel RAG Pipeline with Gemini
The Problem When building BotForge, our AI no-code chatbot platform, we needed a retrieval system that could handle messy, real-world user queries — t…
Your RAG System Is Lying To You About That Table
Retrieval Augmented Generation, or RAG, has become the default way to ask questions about long documents. You do not train a model on your data. You j…
AI Agents Address Hallucinations; New Tools for Code Gen & Enterprise Auth
AI Agents Address Hallucinations; New Tools for Code Gen & Enterprise Auth Today's Highlights This week highlights practical solutions for AI agen…
Zettelkasten as a note-taking method for coding agents
I wanted to give AmblerTS , my Deno/TypeScript state-machine framework, the ability to record non-obvious learnings that would otherwise require signi…
Open Source Project of the Day (#116): SAG — Multi-Hop RAG Retrieval via SQL JOINs Instead of PageRank
Introduction "Graph RAG typically pre-builds a global graph offline, then uses PageRank for query-time scoring and expansion — but PageRank has decay:…
Before adopting DSPy, prove the LM program has a contract
Before adopting DSPy, prove the LM program has a contract DSPy is easy to undersell. If you describe it as "a nicer way to write prompts", you will pr…
Your AI Forgets Everything. Here's How Cognee Fixes It.
Have you ever noticed this? You explain your project to an AI chatbot, have a great conversation, then come back later... and it asks you to explain e…
I Designed a RAG Variant for Multi-Agent Simulations. Here's the Design and the Honest Tradeoffs.
Standard RAG is great for static knowledge bases. Embed documents, embed a query, return top-k by cosine similarity. That works. But put RAG inside a …