Как превратить Cursor в долговременную память проекта: LLM Wiki, QMD и Cursor CLI
Большой контекст в Cursor спасает ровно до тех пор, пока проект живёт дольше одного чата: требования, SQL-гипотезы и причины отброшенных решений быс…
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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…
Вторая память и второй мозг — не одно и то же. Как я собрал 200 тысяч файлов своей жизни и подключил к ним агентов
Модно называть вторым мозгом папку с заметками в Obsidian. Но 200 тысяч файлов сами по себе ничего не делают - это вторая память. Мозгом она станови…
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…
Первый RAG комом: как мы учились анализировать ошибки автотестов с помощью LLM
Привет! Меня зовут Алексей, я автотестер в hh.ru . У нас около 3000 запускаемых классов с e2e (UI) тестами на Java. Они прогоняются по несколько раз…
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…
One Interface, Three Search Backends: Web Search Is Just Another Repository
If you build anything with LLMs, you will need web search sooner or later. The model's training data is frozen; your users' questions are not. In So…
Python Retrieval Architecture for Course Tutors: Chunk Boundaries (and Why)
Short answer: use a staged retrieval design with explicit collections, bounded queries, and source context that survives all the way to the tutor's…
Production RAG Is an Evidence Pipeline, Not a Vector-Database Demo
A production RAG system is not “put documents in a vector database and ask an LLM.” It is an evidence pipeline with two versioned paths: an offline…
How to implement context decay so an AI agent forgets stale or incorrect instructions over time?
A year into a project, one agent's memory holds three instructions that should no longer win. It says to install packages with npm, and since June i…