Guide to LLM Cost Estimation: How to Calculate RAG Chatbot Costs
Learn how AI Product Managers estimate LLM, embedding, RAG, and human-support costs—and build a business case for an AI customer-support chatbot.
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AI
AI concepts, tools, and how AI is changing product and marketing work.
Context Engineering for AI Product Managers: RAG, Prompt Engineering and Fine-Tuning
Learn context engineering for AI product management, including RAG, vector embeddings, prompt engineering, fine-tuning, and how to choose the right approach.
ReadHow LLM Inference Works: A Practical Guide for AI Product Managers
Understand how LLM inference works, from tokenization and embeddings to self-attention, logits, sampling, and detokenization.
ReadConfusion Matrix in Machine Learning: A Simple Guide
Learn what a confusion matrix is, how to interpret TP, TN, FP, and FN, and why it matters when evaluating machine learning classification models.
ReadEmbeddings and Tokenization for AI Product Managers: A Practical Guide
Learn how tokenization, vector embeddings, dimensions, semantic search, and AI model inputs work—and why AI Product Managers should understand them.
ReadLangChain for AI Product Managers: What It Is, How It Works, and When to Use It
Learn what LangChain is, how chains, RAG, memory, and agents work, and when AI Product Managers should use LangChain in production AI products.
ReadAI Guardrails: What They Are, Why They Matter, and How They Work
What are AI guardrails? Learn how input, output, and system-level guardrails improve AI safety, accuracy, security, and compliance.
ReadAI Development Life Cycle: 6 Stages of Building an AI Product
Learn the AI development life cycle, from problem definition and data preparation to model development, deployment, monitoring, and continuous improvement.
ReadDoes Your Product Really Need AI? A Practical Framework for Finding the Right AI Use Case
Does your product need AI? Use this practical framework to identify strong AI use cases, evaluate alternatives, manage risk, and estimate ROI.
ReadMCP vs API: What's the Difference for AI Agents?
MCP vs API for AI agents: how Model Context Protocol works, when to use each, and why MCP complements APIs instead of replacing them.
ReadWhat Is RAG (Retrieval-Augmented Generation)? A Simple Guide for AI Product Managers
Learn what Retrieval-Augmented Generation (RAG) is, how vector databases and embeddings work, and why RAG helps AI applications provide more current, contextual, and source-grounded answers.
ReadEvals 101: A Practical Guide to Evaluating AI Agents
Learn what AI evals and scorers are, how code-based and LLM-as-a-judge scorers work, and how offline and online evaluations improve AI agent quality.
ReadHow Does AI Work? A Simple Guide to Understanding Artificial Intelligence
How modern AI actually works — prediction, parameters, training, and alignment — explained in plain language without the complicated math.
ReadNormal Distribution: A Simple Guide to the Bell Curve
How normal distribution works, including mean, standard deviation, the 68-95-99.7 rule, and practical examples.
ReadRCTF Prompting Framework: A Practical Guide to Better AI Prompts
Learn the RCTF prompting framework — Role, Context, Task, and Format — to write clearer AI prompts and get more useful responses.
ReadAttention Is All You Need Explained for Dummies: The 2017 Paper That Changed AI
What if one research paper from 2017 helped create the AI world we live in today? A plain-language walkthrough of self-attention, Transformers, and how they led to ChatGPT.
ReadHow AI Can Transform Zerodha's Kite & Coin: 5 AI Product Features for Smarter Investing
Five AI product ideas for Zerodha's Kite and Coin — a portfolio risk analyzer, research copilot, overlap detector, earnings summarizer, and concept explainer — built out as a full AI Product Management case study.
ReadHow AI Product Manager role is different from Traditional PM?
How an AI Product Manager differs from a traditional PM — responsibilities, AI PM types, overlapping roles, key skills, and a day in the life of an AI PM.
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