MLOps for Product Managers: What You Need to Know
You do not need to implement MLOps. You do need to understand it well enough to know when your engineering team is cutting corners and what it costs you. Here is the practitioner's guide for AI product managers.
AI Product PRD Template: The Practitioner's Version
A PRD template built specifically for AI products — not a generic PM template with 'AI' bolted on. Includes sections for data requirements, model evaluation, failure mode mapping, and regulatory considerations.
Vector Databases Compared: Pinecone vs Weaviate vs Chroma vs Qdrant
Pinecone, Weaviate, Chroma, and Qdrant all store and retrieve vectors — but they make very different tradeoffs on performance, cost, operational complexity, and features. Here's how to choose.
AI Product Metrics That Actually Matter: Beyond Accuracy
The most common product review question I get in AI initiatives is some version of: "What's the model accuracy?" It'
Pricing AI Products: Models That Work
AI product pricing is harder than SaaS pricing because the value delivered is variable, the cost to serve is variable, and buyers are skeptical of promises they can't verify. Here's the pricing model analysis I use when designing AI product economics.
Why Most AI Products Fail Before They Launch
Most AI product failures aren't model failures. They're alignment failures, scoping failures, and organizational failures. By the time the model underperforms, the product was already dead on the whiteboard.
Embedding Strategies That Actually Scale
Bad embedding choices do not surface in a prototype with 100 documents. They surface in production with 100,000 documents, elevated retrieval latency, degraded search quality, and a surprise bill from your embedding API. Here is how to get it right from the start.
The Shadow AI Problem: When Your Team Uses AI Tools You Don't Know About
Your clinicians are using ChatGPT for patient notes. Your analysts are using Claude for financial models. Your engineers are using Copilot on proprietary code. You almost certainly don't know the full extent. This is the shadow AI problem.
Prompt Engineering Playbook for Product Teams
Prompt engineering is not a hack you use before the real engineering starts. Done properly, it is a disciplined practice that determines whether your AI feature works. Here is the playbook I have built across multiple production deployments.
RAG vs Fine-Tuning vs Context Windows: Which Architecture Actually Works for Clinical AI?
Healthcare AI teams face a critical architecture decision: RAG, fine-tuning, or massive context windows? After shipping systems across clinical trials, drug discovery, and patient recruitment, here is what actually works in production.
The Complete Guide to AI Model Evaluation
Most AI teams measure accuracy on a test set and ship. This is how you end up with a model that scores 94% on benchmarks and fails in production. Real evaluation covers technical metrics, business metrics, human judgment, and continuous monitoring.
How I Automated My Entire Content Pipeline with AI Agents
I have a full-time job at HCLTech, a side brand, a D2C startup, and I'm trying to publish consistently. Automation isn't optional — it's the only path. Here's the entire content pipeline I built with AI agents.
Managing Technical Debt in AI Products
AI products accumulate debt in three dimensions simultaneously: model debt (outdated architectures, obsolete training approaches), data debt (stale labels, incomplete coverage), and infrastructure debt (brittle pipelines, manual processes that should be automated). Managing all three re...
Model Distillation: Making AI Affordable at Scale
GPT-4o is extraordinary. At $15 per million output tokens, calling it a billion times per month is also extraordinary in a bad way. Model distillation is how you get 90% of the quality at 10% of the cost. Here is how it works and when it makes sense.
The Moat Myth: What Actually Defends AI Products
The most common AI startup pitch includes the line about a proprietary model that competitors can't match. Almost none of these claims hold up. Model performance is the least defensible part of an AI product.