How Adaptive RAG Works That Doesn’t Need an LLM At All
One of the most popular ways to reduce the hallucination rate of language models is the RAG method, a scheme where the model accesses external data as needed instead…
What is RAG? A Deep Dive into Risk Assessment and Governance
The Importance of RAG in Project Management and Decision Making
You are already using an agent. You just haven't noticed.
Experts say that in the next few years, everyone will have a personal AI agent. It will write code, help choose a coffee machine, count calories for dinner. It sounds…
30 seconds instead of 30 minutes: how we automated the generation of streaming processing configurations using RAG and A2A
Hello, tekkix! My name is Dmitry Titov, I am a DevOps engineer in the Platform V Synapse integration services team at SberTech. Our team is working on the product Platform…
AI Agents and Code Generation
When an AI agent writes code in an empty project, it spends the first thirty to forty percent of tokens on orientation, reading README, tutorials, and API references,…
Local AI in Obsidian without Subscriptions: A Working Combination with Ollama, Gemma 4, and Infio Copilot
I wanted to create a local AI assistant for Obsidian that can work with my notes offline and without subscriptions. As a result, I tested several approaches and settled…
How to Evaluate RAG Systems: Metrics, Methods, and What to Measure First
When a RAG system fails, it is almost impossible to tell from the final answer exactly where the error occurred. Generation with retrieval is a basic pattern in LLM applications:…
AAF: Architecture of an Autonomous AI Agent with GraphRAG, EventBus, and Docker Sandbox
In our community, the agent @vega_exactly_not_ai has been around for quite some time.
Grep-AST or How We Replaced Vector Search with Just One Library
Hello everyone! This is Sofia from the large language model application team at ecom.tech. Today, I want to share a little-known library that, by fate, we found on GitHub,…
Semantic Updatable Cache on AlloyDB Omni
Suppose you have built an RAG service on SQL, and it works great. Quite fast, very accurate, and very expensive, as each request to the service requires calling the LLM…
Personal AI assistant on your data. Part 1: Vector Database ChromaDB + DeepSeek | GPT
Today we’ll talk about a topic that sparks lively interest among many developers and AI enthusiasts — integrating large language models like DeepSeek or ChatGPT with…
Replacement of Langchain: How OpenAI Agents SDK Handles Deep Search?
Agents are super buggy. In our company projects, we noticed that Langchain started to perform worse. In multi-agent systems, agents often loop because they don’t understand…
Advisor: Employment Assistant
Hello, tekkix! My name is Gurtziev Richard, I am a first-year master's student at AI Talent Hub. During the first semester, I immersed myself in a cool project, the goal…
Improving RAG with Knowledge Graphs
Generation with augmented sampling is a method that connects external data sources to improve the output of large language models. This method is ideal for LLM to access…
Basics and advanced techniques of RAG
In this post, we will talk in detail about RAG at each of its stages, its modifications, and its promising directions of development at the time of writing.
Hallucinations in LLM and methods of combating them
One of the main problems with using large language models in business is that LLMs are prone to hallucinations. How can you trust your customers to a chatbot that can…
Generation of augmented extraction: from theory to implementation of LangChain
From the theory of the original academic paper to its implementation in Python with OpenAI, Weaviate, and LangChain
Hippo-RAG: Long-term memory for LLM inspired by neurobiology
Scientists are often inspired by our or animal biological structures: CNN, MLP, Backprop, and for many other studies, similarities can be found in the real world. Recently,…