Scored the Index Against Known Answers: 10 of 12, and What the Cap Really Costs
Entry 17 ended on a problem I'd made for myself: I'd been judging rankings with nothing to check them against. This entry fixes that, at least a lit…
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Retrieval Architecture for an Online Course Tutor — A Release Checklist
Short answer: use a staged retrieval architecture with explicit collections, bounded queries, and source context that can be traced back to a course…
How to Build a Lightning-Fast, 100% Private Local RAG Chatbot with Ollama, LangChain, and Streamlit
Every time we query a cloud-based LLM with proprietary documents, there’s a quiet compromise on data privacy. What if you could run a fully private,…
LlamaIndex Web Search Tool: Add Free Web Search to an Agent (No API Key)
A LlamaIndex agent without web access can only answer from its model's training data and whatever you indexed yourself. The quickest fix is a LlamaI…
Your RAG Knows Today: Building News-Aware RAG with the Beans API
Your users ask what happened today. Your RAG hands them last month's answer with immaculate citations. Congratulations: you built a very articulate…
Website to Vector Database: A Small, Traceable Pipeline
A vector database can store vectors. It cannot tell you which page produced a passage or whether that passage is stale. This example uses one docume…
Copy or federate? A per-source table for agent data access
Your agent pilot will not wait for the warehouse program to finish. If the agent can reach only a subset of your systems, choose that subset per sou…
Cutting Vector Index Costs in a Developer Changelog Tracker (and When Web Search Wins)
Short answer: use a staged retrieval design with explicit collections, bounded queries, and traceable source context. Keep the vector index for know…
How to Build Retrieval Architecture for a Multi-Tenant SaaS Help Center (with Pagination)
Short answer: for retrieval architecture in a multi-tenant SaaS help center, use staged retrieval with explicit collections, bounded queries, and so…
Finding the Bugs I Built an AI Agent to Prevent: Building Deterministic Guardrails with LangGraph & Python
`` A real debugging log: fabrication bugs, deployment gaps, and false test passes found and closed across 6 rounds of adversarial testing. System pr…
Counting OpenAI Tokens per Request in FastAPI — Without Touching My Use Cases
I'm rebuilding evorag , a Python retrieval service: it chunks documents, embeds them with OpenAI's text-embedding-3-small , stores vectors in Postgr…
Your MCP Server Returns the Source. Does the Answer Still Cite It?
A benchmark for what happens to citations after a retrieval server hands its results to an AI agent it doesn't control. Part 1 of 6 in the series Ci…
Building a RAG assistant that says “I don’t know”: evaluation, security and a $0 deployment
Most RAG tutorials end when the chatbot answers a question about a PDF. I wanted to find out what comes after that: how do you know it works, who is…
BOOTH Was Featured in Python Weekly: A Small Milestone for My Open-Source Project
A small milestone for a project I've been building: BOOTH was featured in Python Weekly! It appears in Python Weekly Issue #767 (October 15, 2026) u…
Why Agents Need a Knowledge Base: The Gaps an LLM Can't Fill by Itself
When a team starts building an agent, the first version is very often only a model, a system prompt and a few tools, and for a demo that is usually…
I benchmarked 8 PDF extractors on 27 documents from 1 to 487 pages. Here's what broke, and what won.
I knew something was off when two numbers landed in the same afternoon. Every tool that reads the PDF text layer had scored exactly 0.00 on every ch…
Travel Planner Retrieval Design with Bounded Access Rules and Source Citations
Short answer: a travel itinerary planner needs a staged retrieval architecture with bounded access rules and source context attached to every answer…
Claims Evidence Retrieval: Immutable Revisions for Insurance Intake Pipelines
Short answer: keep every uploaded claim document immutable, assign a monotonically increasing revision, and let retrieval filter by an explicit as-o…
Metadata filtering in vector search: post-filter loses results, measured
Multi-tenant RAG needs a filter: “only search documents this user may see.” Vector indexes are built for nearest neighbours, not for “nearest neighb…
Recruiting Candidate Search Source Deduplication — A 3-Stage Node.js Retrieval Design
Short answer: use a staged retrieval design with explicit collections, bounded queries, and traceable source context for recruiting candidate search…
Evaluating AI-Powered RAG Systems: Beyond Just Correct Answers
Artificial Intelligence is changing how we search for information and interact with documents. Retrieval-Augmented Generation (RAG) is an important…
Postgres Retrieval Architecture: Multi-Tenant SaaS Help Center Schema Design
Short answer: use a staged retrieval design with explicit collections, bounded queries, and traceable source context, then make index cost a schema…
RAG Evaluation & Monitoring: Metrics, Frameworks & Production Observability
RAG Evaluation & Monitoring: Metrics, Frameworks & Production Observability Comprehensive guide to evaluating and monitoring Retrieval-Augme…
Building RAG from Scratch: No Frameworks Needed
Building RAG from Scratch: No Frameworks Needed Understand Retrieval-Augmented Generation by building it yourself — pure Python, zero dependencies.…
Ragas vs Truvec for RAG Retrieval Evaluation
I have built and debugged many RAG systems, and the most common failure I saw was not because of bad prompts or weak LLM models. Most failures were…
Insurance Claims Retrieval Architecture with Node.js: Five Access-Rule Checks
Short answer: use a staged retrieval design with explicit collections, bounded queries, and traceable source context; make access rules part of retr…
Candidate Search Architecture: Tenant-Bounded Retrieval and Source Traceability
Recruiting search has an awkward constraint: a highly relevant profile is still a security incident if it came from the wrong customer. Short answer…
Building Better RAG
Most RAG systems that disappoint in production fail at retrieval, not generation: the model answers well from the wrong context, or from none at all…
The Atomic Answer Rule: Engineering Content for RAG Chunking and AI Search
The Atomic Answer Rule states that every H2 section of a document must answer its query as an independent, self-contained micro-document, without re…
One Interface, Seven Formats: How Solon AI Turns Files, Web Pages, and Even Database Schemas into RAG Documents
Every RAG pipeline starts the same way: you have stuff, and the model needs Document s. The interesting question is how far that idea stretches. Sol…