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How to Build a Domain-Specific AI Assistant: HomeoAI Case

Notes from my conversation with Tejesh Vaish, IIT Kanpur CSE graduate and lead engineer at an AI startup in Dubai, on his viral headlines, his JEE journey and building an AI career A week after Tejesh Vaish joined us on the Codersarts Podcast to talk about AI coding agents, his story was everywhere. More than 250 news articles ran some version of the same headline: a Lucknow sweet seller's son who scored 48% in Class 5, made it to IIT Kanpur and now earns ₹2.5 crore a year at

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

Notes from my conversation with Ishwar, Engineering Lead at JPMorgan Chase, on reliability, cost, security and the future of AI-assisted engineering. Getting an AI feature to work in a demo takes an afternoon. Getting customers, engineers and stakeholders to trust it in production is a different engineering problem. Most teams find this out the hard way. The prototype impresses everyone, then the real problems show up: token bills that grow every month, answers nobody can ver

Context windows keep growing. Models now accept inputs that once seemed impossible, and it is tempting to conclude that retrieval is obsolete. Why build an index when you can paste everything into the prompt? The answer is that capacity is not the same as capability. A model can accept a very long input and still use it unevenly. As the input grows, answers can become less reliable, details in the middle can be missed, and irrelevant text can pull the model off course. This p

Standard RAG retrieves for every question, whether or not it needs to. It passes the same number of passages to the model, whether they help or not, and it rarely checks whether the final answer actually follows from them. Self-RAG proposes a different design. A single language model learns to decide when retrieval is needed, to judge whether each retrieved passage is relevant, and to critique whether its own output is supported by the evidence. It does this by producing spec

A user asks a short, vague question. Your RAG system turns it into a vector and searches for similar passages. The best passage is in the index, and the search misses it, because a question and its answer rarely look alike. HyDE, short for Hypothetical Document Embeddings, attacks this gap. Instead of embedding the question, a language model first writes a short hypothetical answer. That draft may contain mistakes, and it does not matter. The system embeds the draft and uses

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

Standard RAG finds text chunks that look similar to a question and hands them to a language model. It works well for questions answered by a single passage. It struggles when the answer depends on connections spread across many documents. LightRAG is a retrieval approach that adds a lightweight knowledge graph to the retrieval step. Instead of searching only chunks, it searches entities, relationships, and themes extracted from your documents, and it can add new content witho

Retrieval-augmented generation became the default way to give language models private knowledge. A retriever finds relevant passages at question time, and the model answers from them. It works, and it adds moving parts: an index, a retriever, ranking, and the chance of fetching the wrong passages. Cache-augmented generation, or CAG, takes a different route for the right cases. Instead of retrieving at question time, you load the whole knowledge set into a long-context model a

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

Notes from my unfiltered conversation with Tejesh Vaish, Lead AI Engineer in Dubai and IIT Kanpur Computer Science graduate, on AI coding agents, Claude Code skills and getting real work done with AI. Watch the full episode Most people use AI the same way they did in 2023: ask a chatbot a question, copy the answer, paste it somewhere, fix the error, paste the error back. It helps, but it doesn't scale. A small group of engineers and founders work very differently. They run se

Not every feature needs an LLM. Use rules for clear, fixed logic that must be exactly right, machine learning for predictions from structured historical data, and LLMs for language and messy, varied content. This guide shows how to choose the right approach for each feature, how to combine all three, and which mistakes to avoid when building AI into your product.

Modern AI voices can sound almost human in a short clip, yet many voice agents still feel mechanical on real calls. The biggest causes usually aren’t the voice. They’re long pauses, cutting callers off, ignoring interruptions and replies written for reading rather than speaking. This guide explains each cause of robotic-sounding voice AI and how to fix it, without pretending to be human.

A RAG system can’t be more accurate than the content behind it. Outdated, duplicated or badly formatted documents lead to confident, well-written wrong answers. This data readiness checklist helps you find those problems before you build. You check sources, quality, formats, metadata, permissions, privacy and freshness, then score your readiness and follow a clear plan to fix the gaps.

AI agent demos look magical until the agent meets real data, real users and real edge cases. This walkthrough of an illustrative AI lead qualification agent shows the decisions behind a working agent MVP. It covers narrow scoping, mixing fixed workflow steps with AI judgement, a practical tech stack, and the common failures that break agents, from made-up facts to prompt injection.

Early users forgive missing features, but rarely a broken sign-up, a double charge or a data leak. This MVP launch checklist shows exactly what to test before real users arrive. It’s organised by risk, so you know what must pass and what can wait. It covers core flows, security, payments, mobile, performance and AI features, and ends with a final go/no-go check for launch day.

Choosing who builds your MVP can decide whether you launch in weeks with clean, ownable code or burn your seed money on a product nobody can maintain. This guide compares freelancers, development agencies, in-house teams and fixed-price product studios on cost, speed, risk and control. It helps you pick the right option for your stage and avoid the common pitfalls.

Every founder is being asked “Where is the AI?”, but AI added for its own sake makes an MVP slower, costlier and harder to test. This guide explains when each of the three main options is the right choice. Use plain LLM integration for drafting and summarising, RAG when answers must come from your own data, and AI agents only for genuine multi-step work. It also covers how to tell when AI isn’t needed at all.

Every feature feels essential when it’s your idea, which is why so many MVPs turn into bloated, delayed builds. This guide shows founders how to decide what goes into an MVP. Start from your riskiest assumption, map one core user journey, and rank features using MoSCoW, RICE, Kano or an impact vs effort matrix. Then keep a “not now” list to protect your scope and budget.