RAG vs Fine-Tuning: How to Choose for Your AI Application
Learn when to use RAG, fine-tuning or prompting for an AI app, with a comparison table, support-assistant example and evaluation checklist.
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Practical articles by Gaurav Sharma on full-stack development, Flutter, AI engineering with LLM APIs, and SEO for modern web apps.
Learn when to use RAG, fine-tuning or prompting for an AI app, with a comparison table, support-assistant example and evaluation checklist.
Compare Next.js and React with Vite for public websites, dashboards, SEO and AI search, with a practical decision table and launch checklist.
Build more reliable n8n webhook workflows with authenticated requests, payload validation, durable event storage, idempotency and failure recovery.
Compare Flutter and React Native by team skills, UI, native SDKs, performance testing and release work before choosing your mobile app stack.
A plain-language guide to vector embeddings: what the numbers mean, how embedding models learn to put similar text close together, how cosine similarity and dot products measure that closeness, a working Python semantic search in 20 lines, where embeddings are used (RAG, search, clustering, recommendations), how to pick a model, and the mistakes that quietly ruin results.
Every SQL join type explained on the same two small tables so you can see exactly which rows survive: INNER, LEFT, RIGHT, FULL OUTER, CROSS and SELF JOIN, plus the row-count rules, the ON vs WHERE trap, anti joins with LEFT JOIN ... IS NULL, and the join questions interviewers actually ask.
A clear comparison of session-based and JWT authentication: what a session cookie and a JSON Web Token really contain, how login, every request and logout work in each, the trade-offs on storage, scaling and revocation, where to store a JWT safely, refresh tokens, and minimal Express.js code for both.
RAG lets a language model answer from your own documents by searching them first and putting the best matches in the prompt. This guide covers the full pipeline, chunking, embeddings and vector databases, a short Python example, RAG vs fine-tuning, evaluation and the failures you will actually hit.
A practical campus placement roadmap for computer science students in India: the usual hiring rounds, the order to study aptitude, DSA, core CS subjects and development, a 12-week plan, and free handwritten notes for every subject.
Next.js 16 renamed middleware.ts to proxy.ts and the middleware export to proxy. This guide explains what changed, how to migrate with the official codemod, how the matcher works, and why proxy should only do optimistic auth checks.
Next.js 16 caching is explicit: nothing is cached until you add the 'use cache' directive. This guide covers Cache Components, cacheLife profiles, cacheTag, updateTag vs revalidateTag, Partial Prerendering and the mistakes that break builds.
A clear guide to the JavaScript event loop for interviews: the call stack, Web APIs, the microtask and macrotask queues, why Promise callbacks run before setTimeout, how async/await fits in, the Node.js differences, and six output-prediction questions with step-by-step answers.
Which programming language should you use for DSA and campus placements? A practical comparison of C++, Java and Python: speed, libraries, what interviewers ask about each, the same problem solved in all three, and a simple way to decide.
How the deal pipeline in Atlass CRM works: dnd-kit columns and sortable cards, moving cards between stages while dragging, a DragOverlay preview, and persisting stage and order to MongoDB through one Express endpoint.
The architecture I used for PingChat: authenticated Socket.IO connections, one room per conversation, save-then-emit messaging with acknowledgements, and cursor-paginated history in MongoDB.
A practical comparison of Provider and Bloc for Flutter state management inside an MVVM architecture, using an EdTech-style app with course modules, video playback and quizzes as the example.
A practical architecture for an AI content-generation agent that produces daily Instagram, LinkedIn and blog drafts: a schedule trigger, structured Gemini output validated with Zod, a human review step, and n8n or Node.js to wire it together.
LLMs do not always return valid JSON. Here is the pattern I used in HireMate to get structured, type-safe output from Google Gemini by validating every response with Zod.
A practical checklist for ranking a Next.js site in Google and getting cited by AI answer engines like ChatGPT, Perplexity, and Google AI Overviews.