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What Is a Tool, and Why Does a Language Model Need One
A tool is an external action the model requests from the system: a search, a calculation, a write. The model names the tool and fills in the fields; the program does the rest.
OpenAI Ships GPT-6 Astra: 63% Lower Cost Per Task
OpenAI unveiled GPT-6 Astra: a 12-point lead on the domain-specific benchmark and a claimed 63% lower price per task. Agent budgets are being recalculated.
Tool calling: what it means in plain terms
Tool calling is a model's ability to reach external programs mid-conversation: to search a database, check stock levels, or open a support ticket.
What Agentic AI Is and Where It Pays Off Fastest
Agentic AI is a system that carries a multi-step task through to the end on its own: it decides what to do next, calls the services it needs, and delivers the result.
Source citation: how to verify a model answer in a minute
Source citation means a link to a specific fragment of a document next to every claim a model makes: the answer can be verified in a minute and is fit to act on.
What a company perimeter is, and when data never leaves it
A company perimeter is the boundary inside which data and computation stay under the company's control: its own servers, its own networks, its own accounts.
What is a knowledge base and how a model answers from it
A knowledge base is a company's documents gathered in one place with dates, owners and access rights: the model answers from them and cites its source.
What Semantic Search Is and What It Does for a Business
Semantic search finds documents by the meaning of a query: the system returns the right document even when it contains none of the words from the question.
Model API
API модели — это способ обратиться к ИИ по сети: система компании отправляет запрос, получает ответ и платит только за обработанный объём.
What Is a Chatbot, and When Does It Pay Off
A chatbot is a program that carries on a conversation in place of an employee: it answers questions, collects data, and hands the hard cases onward.
Knowledge Cutoff — What It Means in Plain Terms
A knowledge cutoff is the date after which a model knows nothing about the world: anything more recent has to be supplied along with the request.
Google Halves the Price of Gemini 3.7 Flash: Task Volume Gets Recalculated
Google has released Gemini 3.7 Flash — the cheap model class has improved at code and agents, and input costs $0.75 per 1M tokens through the end of 2026.
Generative AI — What It Is in Plain Words
Generative AI is a class of models that produce new text, images, or code on request, drawing on patterns learned from training data.
What is latency and how to keep it within seconds
Latency is the time between a request and the model's answer: it is broken into its parts, each part is measured, and the user's wait is brought down to seconds.
What Is a Large Foundation Model
A large foundation model is an AI trained once on an enormous body of data and then usable across dozens of company tasks without separate training for each.
Non-determinism: what it means in plain terms
Non-determinism is a language model's tendency to answer the same request differently each time; the spread is narrowed by a fixed response format, a quality threshold, and sample-based acceptance testing.
Prompt engineering: how to get the answer you need from a model
Prompt engineering is the practice of drafting and testing how a task is worded for a model: the same model makes noticeably fewer mistakes when the instruction has been refined.
Z.ai Opens GLM-5.3 for Coding and Agent Work
Z.ai has released GLM-5.3 with open weights. Per-token pricing for the new model has not been published yet, so budgets have to be built on the previous version's rate.
DeepSeek V4 Pro Raises Prices: How to Redo the Budget in One Evening
DeepSeek has released V4 Pro 0813 and raised API prices by 1.5–2.5 times. Cache went up the most, and a double multiplier applies during peak hours.
Context bloat: what it means in plain terms
Context bloat is the growth in the volume of text sent to the model with every request, without any growth in the value that text delivers.
Contextual chunk enrichment — what it means in plain terms
Contextual chunk enrichment means adding a short explanatory note to every piece of a document so that knowledge base search finds the right one more often.
Sber launches GigaAgent: an agent pilot inside the Russian perimeter
Sber has opened access to an autonomous general-purpose AI agent. The launch runs through Cloud.ru Agents Space, with a RUB 4,000 starter grant for new users.
What context rot is and how to hold quality steady
Context rot is the drop in answer accuracy as the volume of supplied text grows: the window is not yet full, but the detail that matters is already getting lost.
What is context compaction and why long sessions need it
Context compaction replaces a bloated conversation history with a short summary: the work continues, but the input volume of each turn stops growing.
Long context: when it pays to feed the whole document
Long context is a model's ability to hold dozens of pages in a single request: a contract, a policy manual and an email thread are read whole, without being cut into pieces.
Context engineering — explained simply
Context engineering is the assembly of a working environment for the model: which data, rules and tools enter the request, in what order and in what volume.
Qwen3.8-Max: a million tokens of context at $2 per input
Alibaba has released a flagship model with 2.4 trillion parameters and a context window of up to 1 million tokens. What that changes in the budget for long documents.
Three cents per task: where DeepSeek V4-Flash cuts costs
DeepSeek V4-Flash: roughly 3 cents per routine task against $3.15 for Claude Fable 5. The pricing converted into rubles at monthly volume, and the profiles where it changes the math.
Embedding weaknesses: four conditions for accurate semantic search
Embedding weaknesses are a known set of places where semantic search misses: exact numbers, outdated revisions, shared access. Each one is fixed by configuration and measurement.
What Parameter-Efficient Fine-Tuning Is and When It Pays Off
Parameter-efficient fine-tuning adapts a ready-made model to a specific task by training a fraction of a percent of its weights: the result weighs megabytes and trains in hours.
Fine-tuning or RAG — which one fits your task
Fine-tuning and RAG are two ways to adapt a model to a company's work: the first changes how it answers, the second gives it access to current documents.
Task-Specific Fine-Tuning — Explained Simply
Task-specific fine-tuning means adapting an off-the-shelf model on a company's own examples so that it answers in the required format and follows the rules of one particular process.
OpenAI Cuts Luna Pricing by 80%: Volume Workloads Recalculated
Input on GPT-5.6 Luna dropped from $1 to $0.20 per million tokens; on Terra, from $2.50 to $2. Quarterly budgets are being redone.
Open weights — what they are and when they beat closed models
Open weights are the published parameters of a trained model: it can be downloaded, run on a company's own hardware and tuned to its own documents.
FSTEC order on AI: what to prepare before September 1
FSTEC has published a draft order to replace No. 21. For the first time, information security in AI use is set out as a separate area, with the rules taking effect on September 1, 2026.
What Is a System Prompt, and Why It Decides Everything Before the First Word
A system prompt is the hidden instruction that gives a model its role, tone and limits before the conversation begins. It determines whether the model is rude or genuinely useful.
What Is a Foundation Model, and Why Companies Save Money on It
A foundation model is a large general-purpose model trained on enormous volumes of data: for a specific job it gets configured, not trained from scratch.
Anthropic Ships Claude Opus 5: Nearly Fable at Half the Price
The model comes close to flagship Fable 5 at half the price and becomes the default workhorse for business use.
What a neural network is, in plain terms
A neural network is a program that derives rules from examples: trained on labelled data, it learns to process requests, texts and images without an algorithm written out by hand.
What is artificial intelligence: no myths, just facts
Artificial intelligence is a set of technologies that derive rules from examples and take over part of the decisions and routine work inside a company's process.
What Machine Learning Is and When It Pays Off
Machine learning is a way to derive a rule from accumulated data instead of writing it out by hand: the system learns from examples and produces a prediction for each new case.
AI law signed: regulatory threshold set at 1 billion parameters
Russia signed its first standalone federal AI law on 26 July. It introduces a threshold of 1 billion parameters and requires companies to appoint someone accountable for compliance.
What is model fine-tuning and when it pays off
Fine-tuning adjusts an off-the-shelf model on a company's own examples so that it answers in the required format and tone; it adds no knowledge of the company's documents.
Embeddings explained simply: how machines search by meaning
Embeddings, explained simply, are a way of writing text down as numbers so that phrases with similar meanings end up with similar numbers.
AI hallucinations: why models confabulate and how to catch it
A hallucination is a confident answer that looks plausible but does not match reality: an invented link, a made-up number, or a clause that appears in no regulation.
What Is an AI Agent, and How Does It Differ from a Chatbot
An AI agent is a program built on a language model that receives a goal stated in plain words, chooses its own steps, calls the tools it needs, and carries the task through to a result.
AI agent in six weeks: where to start and when it pays off
Breaking a process down step by step delivers a first working version in six weeks. Four conditions for an agent to pay off, and the checks that confirm them in two days.
What Inference Is and What Makes Up the Bill for It
Inference is the moment a trained model answers a request — and it is what gets billed every month.
What is a context window, and why does the model "forget"?
A context window is the limit on how much text a model can see in a single request: instructions, documents, the question, and the conversation history all count together, and anything past the limit is dropped silently.
What Is a Large Language Model, in Plain Language
A large language model is a program that continues any text with the most likely next word. It learned from books, articles and correspondence, and it answers in human language.
AI on your own premises: when data cannot leave the building
On-premise deployment means running a language model on company-owned hardware, for cases where data must not leave the perimeter — either by law or by internal policy.
What is a prompt and why the result depends on it
A prompt is a written instruction for a language model, and its precision decides whether the right result arrives on the first try or has to be redone.
What is RAG: how to make a model answer from your documents
RAG is a way of working with AI where the system first finds the right passage in a company's own documents and then builds an answer from it.
The Real Cost of AI Adoption: Nine Budget Lines and Three Hidden Ones
The range from RUB 500,000 to several million comes down to nine cost lines. Here the budget is taken apart piece by piece, including the three lines that rarely appear in a vendor proposal.
What Is a Token and Why You Pay for It
A token is a chunk of text roughly three quarters of a word long: the model reads a request in tokens, and the provider bills for how many there are.
What Is a Vector Database and Why Business Needs It
A vector database stores text and documents as numerical coordinates, so search runs on meaning rather than on matching words.