#python

2026-06-02 · 8 min read

Building an AI-Powered Document Summarizer with Python and Claude API

I have about twelve PDF tabs open right now that I haven't read. A vendor comparison, two research papers, a spec doc from last week. At some point I'll get to them. Probably. Rather than actually reading them, I built a summarizer. This tutorial walks through the same thing: a Python script that takes any `.txt` or `.pdf` file from the command line and returns a clean summary using the Claude API. You'll need Python 3.10+, an Anthropic API key, and basic Python familiarity. No prior LLM experience required.

2026-05-26 · 8 min read

Versioning and Managing Prompts Like Code in Your AI Projects

Somewhere between sprint three and sprint seven, a prompt in your codebase changed. Nobody announced it. The output got worse — responses started being too short, the tone shifted, or edge cases started failing. You grep through the code trying to find what happened, and eventually realize someone edited the prompt string directly in the source file, probably to fix something else, and didn't think much of it.

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.

2026-05-22 · 7 min read

Fine-Tuning vs Prompt Engineering: When to Use Which Approach

Both approaches solve the same problem: getting an LLM to do exactly what you want. They solve it in completely different ways, at completely different costs, and one of them is almost always the wrong choice for what you're actually trying to do.

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.

2026-05-19 · 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.