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⚡ AI Lessons
Dev.to · Roman Belov
3d ago
Human-in-the-Loop for AI Products: When the Model Decides and When a Person Does
Fully autonomous AI systems in production make critical mistakes. Adding human-in-the-loop reduces...
Dev.to · Roman Belov
1w ago
Prompt A/B Testing: a scientific approach to improving AI response quality
Most production prompts never go through formal comparative testing. Teams change wording on...
Dev.to · Roman Belov
💻 AI-Assisted Coding
⚡ AI Lesson
1w ago
ADR Template: How AI Generates Architecture Decision Records Your Future Self Will Thank You For
Teams make dozens of architectural decisions every month but document almost none of them. The rest...
Dev.to · Roman Belov
🤖 AI Agents & Automation
⚡ AI Lesson
2mo ago
Multi-Agent Architecture: When One AI Isn't Enough
A single LLM call handles a single task. But real product workflows rarely fit in one call. Analyzing...
Dev.to · Roman Belov
💻 AI-Assisted Coding
⚡ AI Lesson
2mo ago
AI Code Review Checklist: Correctness, Security, Performance, Readability
Most defects missed in code review are logical errors and edge cases — not formatting issues, not...
Dev.to · Roman Belov
🧠 Large Language Models
⚡ AI Lesson
2mo ago
LLM-as-Judge: Automated Quality Gate for LLM Outputs in Production
LLM-as-Judge is a pattern where one language model evaluates another model's outputs against defined...
Dev.to · Roman Belov
3mo ago
Prompt Engineering System: Managing 50+ Prompts in Production
The average LLM project in production uses 20–50 prompts. Classification, summarization, data...
Dev.to · Roman Belov
🧠 Large Language Models
⚡ AI Lesson
3mo ago
Context Engineering: How to Manage Context for AI Models and Agents
Claude's context window holds 200,000 tokens. Gemini's stretches to two million. But response quality...
DeepCamp AI