GenAI Mon 27 April 2026

Lessons Learned While Learning AgentScope

Closing out this series' look at AgentScope, it's worth stepping back from the individual architectural pieces — messages, memory, tools, multi-agent coordination — and pulling together the broader lessons that tend to matter most when actually learning and applying the...

GenAI Sun 01 March 2026

Common Python Errors in AI Projects

AI code breaks in predictable ways. Here are the traps that waste hours, with the fixes that save them. 1. Mutable Default Arguments # WRONG: All calls share the same list def predict(inputs, cache=[]): cache.append(inputs) return model(inputs) # RIGHT: Fresh list every call...

GenAI Sat 28 February 2026

Python Project Structure for GenAI

A messy GenAI project becomes unmaintainable fast. Prompts scattered in notebooks, API keys hardcoded, and no separation between inference logic and business code. Here's a structure that scales. The Layout genai-app/ ├── src/ │ ├── __init__.py │ ├── config.py # Centralized...

GenAI Mon 09 February 2026

Building Reliable LLM Prompts

This series has covered dozens of individual techniques and concepts — structure, examples, chain-of-thought, sampling parameters, testing, versioning, context engineering. "Reliability" is really the thread tying all of it together: not just getting a good response once, but...

GenAI Sat 07 February 2026

Reusable Prompt Templates

Somewhere between "type a fresh prompt every time" and "build a full internal prompt library," most people and teams land on the same practical habit: taking a prompt that worked well once and turning it into something they can use again and again, with just the details...

GenAI Mon 02 February 2026

How to Write Better AI Prompts

This series has covered a lot of ground — tokens, transformers, sampling parameters, chain-of-thought, structured output. But if you just want the practical takeaway — how do you actually write a better prompt, right now, without needing to think about attention mechanisms —...

GenAI Sun 01 February 2026

Common Prompt Engineering Mistakes

Most disappointing AI outputs aren't the model's fault so much as the prompt's. After covering a whole toolkit of techniques throughout this series — roles, examples, structure, chain-of-thought, sampling controls — it's worth flipping the lens: what are the recurring...