https://varunvemulapalli.com/blog/ AI explained one idea a day, in plain English, with real examples from banking, insurance, healthcare and retail. No PhD required. en https://varunvemulapalli.com/blog/day-100.html https://varunvemulapalli.com/blog/day-100.html Sat, 09 Jan 2027 12:00:00 +0000 You started with "what is AI?" and finished able to design, build, test and ship an agent responsibly. https://varunvemulapalli.com/blog/day-99.html https://varunvemulapalli.com/blog/day-99.html Fri, 08 Jan 2027 12:00:00 +0000 Pick one real problem and give it 90 days: use it safely, build it, then ship it and show it. https://varunvemulapalli.com/blog/day-98.html https://varunvemulapalli.com/blog/day-98.html Thu, 07 Jan 2027 12:00:00 +0000 Test outcomes by group, send only the data you need, and know which rules apply before you launch. https://varunvemulapalli.com/blog/day-97.html https://varunvemulapalli.com/blog/day-97.html Wed, 06 Jan 2027 12:00:00 +0000 Agents at work do the legwork around a decision (gather, check, draft, log) while a person keeps the decision. https://varunvemulapalli.com/blog/day-96.html https://varunvemulapalli.com/blog/day-96.html Tue, 05 Jan 2027 12:00:00 +0000 A one-line README, a 90-second demo and a pinned repo turn your agent into proof anyone can check in 2 minutes. https://varunvemulapalli.com/blog/day-95.html https://varunvemulapalli.com/blog/day-95.html Mon, 04 Jan 2027 12:00:00 +0000 Log every approval, edit and thumbs-down, fix the biggest bucket one change at a time, and turn each failure into a new eval case. https://varunvemulapalli.com/blog/day-94.html https://varunvemulapalli.com/blog/day-94.html Sun, 03 Jan 2027 12:00:00 +0000 Share your agent one rung at a time, from your laptop to a teammate to a small private web app, and hand the organization-wide version to IT. https://varunvemulapalli.com/blog/day-93.html https://varunvemulapalli.com/blog/day-93.html Sat, 02 Jan 2027 12:00:00 +0000 Treat every transcript as untrusted data, give tools the least power they need, mask PII, and keep a human approval before anything is created or sent. https://varunvemulapalli.com/blog/day-92.html https://varunvemulapalli.com/blog/day-92.html Fri, 01 Jan 2027 12:00:00 +0000 Log every run to know cost, seconds and errors per run; resent input tokens are usually the biggest lever. https://varunvemulapalli.com/blog/day-91.html https://varunvemulapalli.com/blog/day-91.html Thu, 31 Dec 2026 12:00:00 +0000 An eval is a fixed exam of inputs and golden answers that turns "it seems to work" into a score you rerun after every change. https://varunvemulapalli.com/blog/day-90.html https://varunvemulapalli.com/blog/day-90.html Wed, 30 Dec 2026 12:00:00 +0000 Give your agent the team's glossary and owner directory, in the prompt while small, through retrieval when large, and keep both up to date. https://varunvemulapalli.com/blog/day-89.html https://varunvemulapalli.com/blog/day-89.html Tue, 29 Dec 2026 12:00:00 +0000 Give your agent reading tools freely, but put every action behind an approval gate your code enforces. https://varunvemulapalli.com/blog/day-88.html https://varunvemulapalli.com/blog/day-88.html Mon, 28 Dec 2026 12:00:00 +0000 The agent loop is plain code: call the model, run the tool it asks for, send back the result, and stop when it's done or hits your limit. https://varunvemulapalli.com/blog/day-87.html https://varunvemulapalli.com/blog/day-87.html Sun, 27 Dec 2026 12:00:00 +0000 Write the spec for humans first, then translate it into a system prompt with a role, clear rules, tags and an example. https://varunvemulapalli.com/blog/day-86.html https://varunvemulapalli.com/blog/day-86.html Sat, 26 Dec 2026 12:00:00 +0000 Design an agent like a product: name the job, the users, the boundaries and the test for "done" before you write code. https://varunvemulapalli.com/blog/day-85.html https://varunvemulapalli.com/blog/day-85.html Fri, 25 Dec 2026 12:00:00 +0000 Pick the tool by who builds it, where users work, what it produces and what it must connect to, not by which demo looked best. https://varunvemulapalli.com/blog/day-84.html https://varunvemulapalli.com/blog/day-84.html Thu, 24 Dec 2026 12:00:00 +0000 A good policy agent answers from the documents, cites them, and hands personal cases to a human. https://varunvemulapalli.com/blog/day-83.html https://varunvemulapalli.com/blog/day-83.html Wed, 23 Dec 2026 12:00:00 +0000 Environments, data policies, sign-in and audit logs let a company say yes to agent builders without losing control of its data. https://varunvemulapalli.com/blog/day-82.html https://varunvemulapalli.com/blog/day-82.html Tue, 22 Dec 2026 12:00:00 +0000 Publish, pick channels where people already work, set authentication on purpose, and roll out from pilot to org with admin approval. https://varunvemulapalli.com/blog/day-81.html https://varunvemulapalli.com/blog/day-81.html Mon, 21 Dec 2026 12:00:00 +0000 Event triggers let an agent start itself, so keep instructions precise, credentials in mind and a person on final decisions. https://varunvemulapalli.com/blog/day-80.html https://varunvemulapalli.com/blog/day-80.html Sun, 20 Dec 2026 12:00:00 +0000 A tool, like an agent flow with clear inputs and outputs, turns your agent from an answerer into a doer. https://varunvemulapalli.com/blog/day-79.html https://varunvemulapalli.com/blog/day-79.html Sat, 19 Dec 2026 12:00:00 +0000 Topics give high-stakes conversations fixed, tested steps, while generative orchestration decides when to use them. https://varunvemulapalli.com/blog/day-78.html https://varunvemulapalli.com/blog/day-78.html Fri, 18 Dec 2026 12:00:00 +0000 Clear instructions plus well-described knowledge turn a long manual into an agent that answers with citations. https://varunvemulapalli.com/blog/day-77.html https://varunvemulapalli.com/blog/day-77.html Thu, 17 Dec 2026 12:00:00 +0000 Copilot Studio lets you assemble an agent from instructions, knowledge, tools, topics and triggers, and publish it where colleagues already work. https://varunvemulapalli.com/blog/day-76.html https://varunvemulapalli.com/blog/day-76.html Wed, 16 Dec 2026 12:00:00 +0000 Copilot, Codex and Claude Code share the same core skills; pick by where your work happens and keep a human reviewing every change. https://varunvemulapalli.com/blog/day-75.html https://varunvemulapalli.com/blog/day-75.html Tue, 15 Dec 2026 12:00:00 +0000 Describe the agent, review the plan, and let Claude Code build, test and run it, while you keep the approvals and the final decisions. https://varunvemulapalli.com/blog/day-74.html https://varunvemulapalli.com/blog/day-74.html Mon, 14 Dec 2026 12:00:00 +0000 Subagents split the work, hooks make steps automatic, and permission rules set hard limits that the model can't talk its way past. https://varunvemulapalli.com/blog/day-73.html https://varunvemulapalli.com/blog/day-73.html Sun, 13 Dec 2026 12:00:00 +0000 A research agent is the Agent SDK loop with search, read and write tools plus clear rules, and its brief is only as good as the citations you check. https://varunvemulapalli.com/blog/day-72.html https://varunvemulapalli.com/blog/day-72.html Sat, 12 Dec 2026 12:00:00 +0000 Use the API for single calls, Claude Code for hands-on work, and the Agent SDK when your own program needs an agent that reads, acts and loops without you. https://varunvemulapalli.com/blog/day-71.html https://varunvemulapalli.com/blog/day-71.html Fri, 11 Dec 2026 12:00:00 +0000 MCP servers give Claude a menu of tools for your systems, added with one command in Claude Code or as a connector in claude.ai. https://varunvemulapalli.com/blog/day-70.html https://varunvemulapalli.com/blog/day-70.html Thu, 10 Dec 2026 12:00:00 +0000 CLAUDE.md holds what Claude should always know, slash commands steer a session, and skills package a repeatable job your whole team can run by name. https://varunvemulapalli.com/blog/day-69.html https://varunvemulapalli.com/blog/day-69.html Wed, 09 Dec 2026 12:00:00 +0000 Claude Code is an agent in your terminal or editor that reads, runs and edits your project; give it clear expected-vs-actual and review every change. https://varunvemulapalli.com/blog/day-68.html https://varunvemulapalli.com/blog/day-68.html Tue, 08 Dec 2026 12:00:00 +0000 With tool use, Claude asks for a function call in JSON, your code runs it and returns a matching tool_result, and the answer is grounded in real data. https://varunvemulapalli.com/blog/day-67.html https://varunvemulapalli.com/blog/day-67.html Mon, 07 Dec 2026 12:00:00 +0000 A Claude API call is a model, a length limit and your messages, sent with a secret key, turning raw text into next steps from your own code. https://varunvemulapalli.com/blog/day-66.html https://varunvemulapalli.com/blog/day-66.html Sun, 06 Dec 2026 12:00:00 +0000 Pick a model for the job, put the context in a Project once, and let artifacts turn chats into things you can click, share and reuse. https://varunvemulapalli.com/blog/day-65.html https://varunvemulapalli.com/blog/day-65.html Sat, 05 Dec 2026 12:00:00 +0000 You write the spec and the tricky example; Codex plans, builds, tests and reviews; you approve the commit. https://varunvemulapalli.com/blog/day-64.html https://varunvemulapalli.com/blog/day-64.html Fri, 04 Dec 2026 12:00:00 +0000 Have Codex write tests first, refactor second and review third, and keep a human as the final approver. https://varunvemulapalli.com/blog/day-63.html https://varunvemulapalli.com/blog/day-63.html Thu, 03 Dec 2026 12:00:00 +0000 AGENTS.md is the onboarding note for your coding agent: short, specific rules in the repo, loaded before every task. https://varunvemulapalli.com/blog/day-62.html https://varunvemulapalli.com/blog/day-62.html Wed, 02 Dec 2026 12:00:00 +0000 Install Codex, sign in with ChatGPT, commit a checkpoint, and start with one failing test you can verify yourself. https://varunvemulapalli.com/blog/day-61.html https://varunvemulapalli.com/blog/day-61.html Tue, 01 Dec 2026 12:00:00 +0000 Codex is one coding agent you can use from the terminal, your editor, the ChatGPT app or the cloud, and a safe first job is asking it to explain code… https://varunvemulapalli.com/blog/day-60.html https://varunvemulapalli.com/blog/day-60.html Mon, 30 Nov 2026 12:00:00 +0000 Clear rules, worked numbers and tests turn Copilot into a reliable builder, and you into the checker. https://varunvemulapalli.com/blog/day-59.html https://varunvemulapalli.com/blog/day-59.html Sun, 29 Nov 2026 12:00:00 +0000 An instructions file tells Copilot your project's rules, and MCP servers give it new tools. https://varunvemulapalli.com/blog/day-58.html https://varunvemulapalli.com/blog/day-58.html Sat, 28 Nov 2026 12:00:00 +0000 Assign a clear issue to Copilot cloud agent and it returns a pull request, but a human still reviews and merges. https://varunvemulapalli.com/blog/day-57.html https://varunvemulapalli.com/blog/day-57.html Fri, 27 Nov 2026 12:00:00 +0000 In Agent, Copilot plans and edits across files, and your job becomes clear criteria and careful review. https://varunvemulapalli.com/blog/day-56.html https://varunvemulapalli.com/blog/day-56.html Thu, 26 Nov 2026 12:00:00 +0000 Completions finish the line you're writing; Chat explains, questions and tests it, and you check both. https://varunvemulapalli.com/blog/day-55.html https://varunvemulapalli.com/blog/day-55.html Wed, 25 Nov 2026 12:00:00 +0000 GitHub Copilot is an AI assistant in your editor, and the free plan plus VS Code is all you need to start. https://varunvemulapalli.com/blog/day-54.html https://varunvemulapalli.com/blog/day-54.html Tue, 24 Nov 2026 12:00:00 +0000 Terminal, Git, Python, APIs, secrets, docs and one LLM call make up the builder's loop that coding agents will now run with you. https://varunvemulapalli.com/blog/day-53.html https://varunvemulapalli.com/blog/day-53.html Mon, 23 Nov 2026 12:00:00 +0000 An LLM API call is a POST with a model and a prompt; ask for JSON and the answer can feed your code. https://varunvemulapalli.com/blog/day-52.html https://varunvemulapalli.com/blog/day-52.html Sun, 22 Nov 2026 12:00:00 +0000 Read docs on a fixed path (quickstart, auth, one endpoint, errors, rate limits) and skip the rest until you need it. https://varunvemulapalli.com/blog/day-51.html https://varunvemulapalli.com/blog/day-51.html Sat, 21 Nov 2026 12:00:00 +0000 Keep keys in environment variables, keep .env out of Git, and if a key leaks, revoke it first and clean up second. https://varunvemulapalli.com/blog/day-50.html https://varunvemulapalli.com/blog/day-50.html Fri, 20 Nov 2026 12:00:00 +0000 An API is a waiter between programs: you send a request to a URL with a method, and get back a status code and a JSON body you can read key by key. https://varunvemulapalli.com/blog/day-49.html https://varunvemulapalli.com/blog/day-49.html Thu, 19 Nov 2026 12:00:00 +0000 Variables, lists, dicts, loops, functions and packages are enough to read data and build the small tools agents rely on. https://varunvemulapalli.com/blog/day-48.html https://varunvemulapalli.com/blog/day-48.html Wed, 18 Nov 2026 12:00:00 +0000 Commits are save points, branches are safe copies, and pull requests are reviewed merges, so a team (or an AI agent) can change shared files without chaos. https://varunvemulapalli.com/blog/day-47.html https://varunvemulapalli.com/blog/day-47.html Tue, 17 Nov 2026 12:00:00 +0000 The terminal is a precise text conversation with your computer, and five safe commands are enough to find your way around and set up your workspace. https://varunvemulapalli.com/blog/day-46.html https://varunvemulapalli.com/blog/day-46.html Mon, 16 Nov 2026 12:00:00 +0000 Agents think, act and observe with tools, under guardrails and human checkpoints. From tomorrow, we build. https://varunvemulapalli.com/blog/day-45.html https://varunvemulapalli.com/blog/day-45.html Sun, 15 Nov 2026 12:00:00 +0000 Anything an AI reads can try to give it orders, so treat content as data, check inputs and outputs, limit what the AI can do, and keep a human on… https://varunvemulapalli.com/blog/day-44.html https://varunvemulapalli.com/blog/day-44.html Sat, 14 Nov 2026 12:00:00 +0000 Human-in-the-loop lets agents handle routine cases on their own and pause for a person on high-stakes ones, with every decision logged. https://varunvemulapalli.com/blog/day-43.html https://varunvemulapalli.com/blog/day-43.html Fri, 13 Nov 2026 12:00:00 +0000 Multi-agent systems give each part of a big job its own clean context, which helps on wide research but adds cost, so use them only when the work truly… https://varunvemulapalli.com/blog/day-42.html https://varunvemulapalli.com/blog/day-42.html Thu, 12 Nov 2026 12:00:00 +0000 Chaining, routing, parallel and orchestrator–workers are building blocks; pick the simplest one that fits the work. https://varunvemulapalli.com/blog/day-41.html https://varunvemulapalli.com/blog/day-41.html Wed, 11 Nov 2026 12:00:00 +0000 MCP is a shared standard that lets any compatible AI app plug into tools and data through servers, so you build each connection once, not once per app. https://varunvemulapalli.com/blog/day-40.html https://varunvemulapalli.com/blog/day-40.html Tue, 10 Nov 2026 12:00:00 +0000 Good agents break a goal into ordered steps, respect what depends on what, and re-plan out loud when something goes missing. https://varunvemulapalli.com/blog/day-39.html https://varunvemulapalli.com/blog/day-39.html Mon, 09 Nov 2026 12:00:00 +0000 Models don't remember; apps give agents memory by saving the right facts and task state and feeding them back in, and deciding what not to store… https://varunvemulapalli.com/blog/day-38.html https://varunvemulapalli.com/blog/day-38.html Sun, 08 Nov 2026 12:00:00 +0000 Give an agent small, clearly named tools with typed inputs, helpful errors and only the access each job needs, and it will guess less and break less. https://varunvemulapalli.com/blog/day-37.html https://varunvemulapalli.com/blog/day-37.html Sat, 07 Nov 2026 12:00:00 +0000 With tool use, the model decides which function to call and with what inputs, and your app runs it and returns the result, so answers come from real… https://varunvemulapalli.com/blog/day-36.html https://varunvemulapalli.com/blog/day-36.html Fri, 06 Nov 2026 12:00:00 +0000 An agent is a loop of think, act, observe, repeat, and the stopping conditions you give it matter as much as the goal. https://varunvemulapalli.com/blog/day-35.html https://varunvemulapalli.com/blog/day-35.html Thu, 05 Nov 2026 12:00:00 +0000 Use a chatbot to answer, a workflow when the steps are known, and an agent only when the steps depend on what it discovers along the way. https://varunvemulapalli.com/blog/day-34.html https://varunvemulapalli.com/blog/day-34.html Wed, 04 Nov 2026 12:00:00 +0000 Phase 4 done. You now know how to give an AI model knowledge it was never trained on, and how to pick the model in the first place. https://varunvemulapalli.com/blog/day-33.html https://varunvemulapalli.com/blog/day-33.html Tue, 03 Nov 2026 12:00:00 +0000 Open-weight models give you control but you run them; closed models give you ease but you trust a provider, so test both on your own task, cost and… https://varunvemulapalli.com/blog/day-32.html https://varunvemulapalli.com/blog/day-32.html Mon, 02 Nov 2026 12:00:00 +0000 Good RAG starts with good chunks: split by structure, keep tables whole, add overlap and metadata, and test retrieval with real questions. https://varunvemulapalli.com/blog/day-31.html https://varunvemulapalli.com/blog/day-31.html Sun, 01 Nov 2026 12:00:00 +0000 RAG retrieves the right passages from your own documents and gives them to the model, so answers are grounded, current and traceable to a source. https://varunvemulapalli.com/blog/day-30.html https://varunvemulapalli.com/blog/day-30.html Sat, 31 Oct 2026 12:00:00 +0000 A vector database stores embeddings with their metadata and quickly finds the closest matches by meaning, which is what makes semantic search work at… https://varunvemulapalli.com/blog/day-29.html https://varunvemulapalli.com/blog/day-29.html Fri, 30 Oct 2026 12:00:00 +0000 Semantic search compares meanings as embeddings, so it finds the right answer even when the user's words don't match the document's. https://varunvemulapalli.com/blog/day-28.html https://varunvemulapalli.com/blog/day-28.html Thu, 29 Oct 2026 12:00:00 +0000 An LLM predicts the next token from patterns; clear prompts, real sources and structured output make it reliable at work. https://varunvemulapalli.com/blog/day-27.html https://varunvemulapalli.com/blog/day-27.html Wed, 28 Oct 2026 12:00:00 +0000 Multimodal AI reads images, audio and text together, which makes intake faster and richer, but a human still checks what the model thinks it sees. https://varunvemulapalli.com/blog/day-26.html https://varunvemulapalli.com/blog/day-26.html Tue, 27 Oct 2026 12:00:00 +0000 Define a schema, ask the model for JSON that matches it, and validate every result, so AI output can flow into real systems instead of being copied by… https://varunvemulapalli.com/blog/day-25.html https://varunvemulapalli.com/blog/day-25.html Mon, 26 Oct 2026 12:00:00 +0000 The system prompt sets who the assistant is and the rules it follows, the user message is the request, and the assistant reply should follow both, with… https://varunvemulapalli.com/blog/day-24.html https://varunvemulapalli.com/blog/day-24.html Sun, 25 Oct 2026 12:00:00 +0000 Show the model a few labeled examples to get consistent answers in your format, and ask it to reason step by step when the task has math or multiple steps. https://varunvemulapalli.com/blog/day-23.html https://varunvemulapalli.com/blog/day-23.html Sat, 24 Oct 2026 12:00:00 +0000 Give the model a role, a clear task, the context, the format you want and the rules to follow, and a vague answer becomes a usable one. https://varunvemulapalli.com/blog/day-22.html https://varunvemulapalli.com/blog/day-22.html Fri, 23 Oct 2026 12:00:00 +0000 LLMs predict plausible text rather than look things up, so give them the source, ask them to cite it, let them say "I don't know", and verify anything… https://varunvemulapalli.com/blog/day-21.html https://varunvemulapalli.com/blog/day-21.html Thu, 22 Oct 2026 12:00:00 +0000 Temperature decides how adventurous the model is when picking each next word: turn it up for ideas, down for answers that must stay consistent. https://varunvemulapalli.com/blog/day-20.html https://varunvemulapalli.com/blog/day-20.html Wed, 21 Oct 2026 12:00:00 +0000 The context window is the model's working memory in tokens; when you overfill it, things get cut or ignored, so send only what matters. https://varunvemulapalli.com/blog/day-19.html https://varunvemulapalli.com/blog/day-19.html Tue, 20 Oct 2026 12:00:00 +0000 Fine-tuning teaches a model your format from approved examples, and RLHF teaches it what people prefer by learning from their choices between answers. https://varunvemulapalli.com/blog/day-18.html https://varunvemulapalli.com/blog/day-18.html Mon, 19 Oct 2026 12:00:00 +0000 Pretraining teaches a model language, facts and patterns through billions of next-word guesses, but it stops at a cutoff date and isn't an assistant yet. https://varunvemulapalli.com/blog/day-17.html https://varunvemulapalli.com/blog/day-17.html Sun, 18 Oct 2026 12:00:00 +0000 Transformers use attention so every word can look at every other word at once, which is how AI understands words in context. https://varunvemulapalli.com/blog/day-16.html https://varunvemulapalli.com/blog/day-16.html Sat, 17 Oct 2026 12:00:00 +0000 LLMs read text as tokens (about ¾ of a word each in English), and tokens drive both your bill and your limits. https://varunvemulapalli.com/blog/day-15.html https://varunvemulapalli.com/blog/day-15.html Fri, 16 Oct 2026 12:00:00 +0000 Classic AI picks from options it was given, while generative AI creates new content by predicting the next word, over and over. https://varunvemulapalli.com/blog/day-14.html https://varunvemulapalli.com/blog/day-14.html Thu, 15 Oct 2026 12:00:00 +0000 Two weeks in. If you've followed along, you now understand more about how AI works than many people who use it every day. https://varunvemulapalli.com/blog/day-13.html https://varunvemulapalli.com/blog/day-13.html Wed, 14 Oct 2026 12:00:00 +0000 Transfer learning reuses what a big model already learned so you need far less data, and GPUs make the heavy number-crunching fast by doing it in parallel. https://varunvemulapalli.com/blog/day-12.html https://varunvemulapalli.com/blog/day-12.html Tue, 13 Oct 2026 12:00:00 +0000 AI sees an image as a grid of numbers and builds up from edges to shapes to objects, layer by layer, using filters it learned from labeled examples. https://varunvemulapalli.com/blog/day-11.html https://varunvemulapalli.com/blog/day-11.html Mon, 12 Oct 2026 12:00:00 +0000 An embedding turns meaning into a list of numbers, so similar things land close together and AI can measure similarity with simple math. https://varunvemulapalli.com/blog/day-10.html https://varunvemulapalli.com/blog/day-10.html Sun, 11 Oct 2026 12:00:00 +0000 A network learns by measuring how wrong it is (loss), working out each weights share of the blame (backpropagation) and nudging every weight downhill… https://varunvemulapalli.com/blog/day-09.html https://varunvemulapalli.com/blog/day-09.html Sat, 10 Oct 2026 12:00:00 +0000 A neuron multiplies inputs by learned weights, adds them up and decides; stack enough of them in layers and you get a neural network. https://varunvemulapalli.com/blog/day-08.html https://varunvemulapalli.com/blog/day-08.html Fri, 09 Oct 2026 12:00:00 +0000 Overfitting is when a model memorizes its training data instead of learning the pattern, so it looks great in practice and fails on anything new. https://varunvemulapalli.com/blog/day-07.html https://varunvemulapalli.com/blog/day-07.html Thu, 08 Oct 2026 12:00:00 +0000 Accuracy says how often a model is right overall; precision says how much to trust its alarms; recall says how many real problems it catches. https://varunvemulapalli.com/blog/day-06.html https://varunvemulapalli.com/blog/day-06.html Wed, 07 Oct 2026 12:00:00 +0000 Supervised learns from answers, unsupervised finds patterns without answers, and reinforcement learns from rewards for its own actions. https://varunvemulapalli.com/blog/day-05.html https://varunvemulapalli.com/blog/day-05.html Tue, 06 Oct 2026 12:00:00 +0000 Training is the slow, expensive learning done ahead of time; inference is the fast answer the frozen model gives every time you use it. https://varunvemulapalli.com/blog/day-04.html https://varunvemulapalli.com/blog/day-04.html Mon, 05 Oct 2026 12:00:00 +0000 Data is the past examples, features are the clues, labels are the answers, and a model is only as good as all three. https://varunvemulapalli.com/blog/day-03.html https://varunvemulapalli.com/blog/day-03.html Sun, 04 Oct 2026 12:00:00 +0000 A machine "learns" by guessing, measuring how wrong it was on examples with known answers, and adjusting, over and over, until its guesses get good. https://varunvemulapalli.com/blog/day-02.html https://varunvemulapalli.com/blog/day-02.html Sat, 03 Oct 2026 12:00:00 +0000 AI is the umbrella, Machine Learning learns from examples, Deep Learning does it with many-layered neural networks, and Generative AI uses that to… https://varunvemulapalli.com/blog/day-01.html https://varunvemulapalli.com/blog/day-01.html Fri, 02 Oct 2026 12:00:00 +0000 AI is software that learns patterns from examples so it can recognize, predict or decide on situations it hasn't seen before.