#ai

2026-05-25 · 8 min read

Building AI Applications That Are Secure and Privacy-Compliant

Security in AI apps isn't just the usual web attack surface — though you've got that too. On top of SQL injection, broken auth, and CSRF, there's a new class of problems specific to how LLMs work: prompt injection, data leakage through model outputs, PII flowing into API calls, context window contamination, and third-party data processor obligations you might not have noticed you signed up for.

2026-05-24 · 8 min read

AI Cost Optimization: How to Reduce API Bills Without Losing Quality

A weekend project I built last spring cost me $140 in API fees before it saw a single real user. The code worked. The model responses were good. But I'd written every prompt like money was no object — long system prompts, GPT-4 for every request, no caching, no batching. The bill fixed that habit fast.

2026-05-24 · 5 min read

Choosing the Right AI Model: GPT-4, Claude, Gemini, or Llama?

The question comes up constantly. Someone wants to build a product or add AI to an existing one, and the first thing they hit is: which model? The advice online is mostly useless — half of it is sponsored, the other half is from someone who tested the models for two hours on their laptop.

2026-05-23 · 8 min read

Token Limits Explained: How to Chunk and Process Large Documents

Your 500-page contract review just threw a context length error. The model has a 200k token context window, and you've still managed to overflow it. Welcome to the practical side of token limits. Most introductions to this topic start with "tokens are pieces of text." That's true, but it's the wrong thing to know first. What matters is this: every LLM call has a ceiling, you'll hit it more often than you expect, and the strategy you use when you do determines whether your application returns useful output or quietly fails.

2026-05-22 · 7 min read

Understanding AI Hallucinations and How to Mitigate Them in Production

Your AI model will fabricate things. Not sometimes — regularly. And it will do it with confidence. The question isn't whether to trust it. The question is how to build a system that works even when it lies.

2026-05-21 · 7 min read

Building a Vector Database from Scratch vs Using Pinecone/Weaviate

The question isn't whether you need a vector database. If you're working with embeddings — for RAG, semantic search, recommendations, or anything that converts text to vectors — you need somewhere to store and search them. The question is whether you should build that layer yourself or use something that already exists. Most developers approach this wrong. They either reach for a managed service before understanding what it does, or they spend a week building their own before discovering it breaks at 50k vectors. This article covers both paths honestly, with working code for all three approaches.

2026-05-20 · 7 min read

RAG (Retrieval-Augmented Generation) Explained with Real-World Examples

LLMs have a memory problem. Ask Claude or GPT-4 about your internal documentation, this quarter's pricing changes, or a contract signed last week — and you'll get one of two outcomes: "I don't know," or something confidently wrong. RAG fixes this. Not by retraining the model. Not by fine-tuning. By handing the model the documents it needs, right before it answers.

2026-05-19 · 7 min read

Prompt Engineering Best Practices for Production Applications

Most prompts that work in a playground break in production. Not because the model changed — because your inputs did.

2026-05-18 · 7 min read

Understanding LLM APIs: A Practical Guide for Web Developers

LLM APIs look like REST APIs but don't behave like them. If you've built integrations with Stripe or GitHub's API, you know the pattern: send a request, get structured data back, handle errors. LLM APIs follow that same HTTP shape, but they add a handful of concepts that don't exist in typical API work. Skip past them and you'll hit confusing bugs and unexpected bills.