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Basic RAG follows a fixed path. It retrieves passages, hands them to a language model, and trusts the result. That path works for many questions, and it fails in predictable ways: it retrieves when it does not need to, it accepts weak evidence, and it rarely checks whether the final answer follows from what was found. Three designs try to fix these weaknesses from different directions. Self-RAG builds retrieval decisions and self-critique into the model. Corrective RAG adds a

Standard RAG has a quiet weakness. It retrieves passages, passes them to the language model, and trusts that they are relevant. When retrieval returns weak or wrong passages, the model often builds a confident answer on top of them. Corrective RAG, often shortened to CRAG, adds a checkpoint. After retrieval, a lightweight evaluator grades the results. Good results are cleaned and used. Poor results are discarded, and the system looks elsewhere. Uncertain results are combined

Every homoeopath knows how much goes into a single case. Long consultations, careful symptom selection, and repertory work all come before a remedy is chosen. Homeo AI is a clinical copilot that brings AI into this process while leaving every decision with the doctor. This blog explains what Homeo AI is, how its main capabilities work, and how doctors can try it. Everything here is based on the information Homeo AI shares about its platform. What Is Homeo AI? Homeo AI is an A

Connecting an agent to more tools sounds like pure progress. Each new MCP server adds capability, and each one also adds text that the model must read before it can do anything useful. Tool names, descriptions, and input schemas all take up space in the context window. Large tool results take up more. Left unmanaged, this context bloat raises cost, slows responses, and can make the agent worse at choosing the right tool. This blog explains where the tokens go, how to measure

Teams building AI agents keep running into two ideas that sound similar and do different jobs: skills and MCP. Both extend what an agent can do. Mixing them up leads to bloated prompts, missing connections, and systems that are harder to maintain than they need to be. In short, MCP gives an agent access to tools and data. Skills give an agent know-how: instructions, procedures, and resources for doing a kind of task well. One answers the question of what the agent can reach.

AI agents are learning to use websites the way people do. They read the page, find a button, click it, and hope the layout has not changed since the last visit. It works, and it is fragile and slow. WebMCP proposes a different approach. Instead of guessing how a page works, an agent asks the page what it can do. The website publishes a list of tools with clear names, descriptions, and inputs, and the agent calls them directly. This blog explains what WebMCP is, how it works,

Two protocol names keep appearing in discussions of AI agents: MCP and A2A. They are often mentioned together, and they are sometimes treated as rivals. In practice, they solve different problems. MCP, the Model Context Protocol, connects an agent to tools and data. A2A, the Agent2Agent protocol, connects one agent to another. A single agent system can use both, and many serious multi-agent designs will. This blog explains what each protocol does, how they differ, how they fi

When a RAG assistant gives a wrong answer, the first reaction is usually to blame the language model. Teams try a bigger model, rewrite the prompt, or add more instructions. Often none of it helps, because the model never saw the right information. A model cannot use a passage it was never given, and it will confidently answer from whatever it was given instead. This blog explains why retrieval should be evaluated first, how to separate retrieval failures from generation fail

The usual way an AI agent uses tools is simple. The model picks a tool, the system runs it, the result goes back to the model, and the cycle repeats. It works well for a handful of tools and short tasks. It strains as the number of tools grows. Every tool definition sits in the model's context, and every intermediate result passes through the model on its way to the next step. Code Mode MCP takes a different route. Instead of calling tools one at a time, the agent writes a sm

Most AI systems today are built around one idea: a large language model reads a prompt and writes text, one token at a time. That approach is flexible, and it has shaped how teams think about every AI problem. Many decisions inside business software do not need flexibility. They need a fast, reliable answer to a narrow question, such as which queue a request belongs in or whether a case should be escalated. In September 2026, TypeSafe introduced Jev, a model it describes as t

Reviewing AI outputs by hand is slow and expensive. So many teams ask a language model to do the grading. A judge model reads an answer, scores it against a rubric, and moves on to the next thousand. The idea works well in some settings and fails quietly in others. A judge that agrees with your reviewers on easy cases can still miss the mistakes that matter most, and it can reward the wrong things without anyone noticing. This blog explains when an LLM judge tends to agree wi

A RAG system lives or dies on what it retrieves. If the right passage never reaches the language model, the answer will be wrong, however good the model is. Re-ranking is a popular fix. The system retrieves a larger set of candidate passages, then a second model reads each one alongside the question and puts the best at the top. It often improves relevance. It also adds time to every request, and that time is paid by every user. This blog explains what re-ranking does, what i

Most AI projects do not fail because the model was wrong. They fail because the goal was fuzzy, the data was not ready, nobody owned the result, or the team and the client stopped understanding each other somewhere between the second and third week. Those problems look the same in every industry and every country. They get harder when the people involved sit in different time zones, speak different languages, and work under different rules. This blog shares the lessons that m

A large language model does not need to know a customer's name, address, or account number to answer most questions. Yet personal data finds its way into prompts, documents, logs, and caches, often without anyone deciding that it should. Once it is there, it is copied. A support message becomes a stored trace, a retrieved document becomes an embedding, and a prompt becomes a record at a vendor. Each copy is a place where the data can leak, linger, or be hard to remove. This b

Sooner or later, an AI system says something false to a real customer. It sounds confident, it looks official, and it was never in any of your documents. The first reaction is usually to blame the model. That is rarely the whole story. Hallucinations that reach customers almost always pass through several weak points: a missing document, a search that returned the wrong passage, a prompt that rewards guessing, and no check before the answer went out. This blog explains how to

A voice bot can look impressive in a demo and still lose callers in the first thirty seconds. Customers do not complain about the language model. They notice the pause before it answers, the way it talks over them, and the moment it says it did not understand for the third time. Then they hang up, and the call center sees the same issue arrive later as an angry call. This blog explains why customers hang up on voice bots and the three fixes that address most of the causes: fa

A model that is right 95 percent of the time sounds excellent, and it is one of the most misleading numbers in machine learning. If only five percent of transactions are fraud, a model that labels every transaction as legitimate is 95 percent accurate and catches nothing. The score looks great while the model is worthless. This blog explains why accuracy fails on many business problems, which metrics to use instead, and how to choose one that reflects the real cost of mistake

Multi-agent systems are an attractive idea. Give each task its own specialist, let a manager agent coordinate them, and the design looks like a small company of experts. In practice, many teams find that the company-of-agents design adds more cost, delay, and confusion than it removes. A growing number of them end up replacing it with something much simpler: one agent that handles the judgment, and ordinary code that handles everything else. This blog explains why that change

A RAG bot that gets only about three answers in ten right is not rare. It is the normal result when a team connects a language model to a pile of documents, sees a few good demos, and ships. The assistant sounds fluent, so the wrong answers are easy to miss until a customer, an auditor, or an employee acts on one. The good news is that low accuracy in retrieval-augmented generation is usually a pipeline problem, not a model problem, and pipeline problems can be measured and f

Most AI assistants stop working the moment the conversation ends. You ask, they answer, and the next step waits until you come back. In September 2026, OpenAI introduced dots, always-on agents inside ChatGPT that keep working toward a goal after the chat is over, using their own cloud computer, a browser, and the apps you connect. This blog explains what OpenAI dots are, how they work, who can use them today, which safety controls come with them, the risks enterprises still n