📰 Dev.to · klement Gunndu

Your RAG pipeline has a front door and a back door. Both are wide open. The front door lets users...

A 2025 peer-reviewed study (Vectara, NAACL 2025) found something most RAG teams get...

Most people think hybrid search in RAG is just "run BM25 and vector search, combine the...

The MCP Dev Summit happened 5 days ago. 95 sessions, 170 member organizations. Here are the 5 decisions that affect your Python code right now.

Claude Code ships with no permission rules by default. These 5 patterns lock down file access, Bash commands, MCP tools, and destructive git operations.

Your data sits in CSVs and databases. These 4 Python patterns turn an LLM into an analyst that writes and runs queries for you.

Most MCP servers ship with zero tests. These 4 Python patterns catch tool failures, schema drift, and edge cases before your users do.

AI agents call your API differently than humans do. They retry aggressively, parse errors literally, and chain calls without confirmation. These 5 patterns make

Most Python projects ship with low test coverage. These 4 Claude Code workflows generate unit, edge case, integration, and regression tests with working code.

Your agent calls the wrong tool because your tool definitions are ambiguous. These 5 Python patterns fix that with working code.

Your AI agent needs a model server, vector DB, and monitoring. Docker Compose defines the entire stack in one file. Here are 4 patterns with working YAML.

OWASP ranks prompt injection as the #1 LLM vulnerability. These 4 defense patterns protect your Python agent with working code.

Space systems use 4 autonomy levels to prevent unauthorized actions. Here's how to apply the same permission architecture to your AI agents in Python.

Prompt engineering optimizes how you ask. Context engineering optimizes what information surrounds the ask. Here are 4 production patterns with working Python c

Freelancers spend 10+ hours a week on admin. These 5 Python scripts use OpenAI structured outputs to automate proposals, reports, invoices, emails, and scope do

One agent hits a ceiling fast. These 3 multi-agent patterns — subagents-as-tools, supervisor, and handoffs — let you split work across specialists with working

Human eval does not scale. LLM-as-a-Judge matches human agreement rates at 1000x the throughput. Here are 3 patterns with working Python code.

AI engineering changed faster than any career guide predicted. Here are the 5 layers of the modern stack — with the exact tools, versions, and learning order th

Unit tests check if your code runs. These 4 patterns check if your AI agent actually works — schema contracts, deterministic mocks, eval datasets, and failure i

Every AI agent is a while loop. Here's the exact pattern LangChain, CrewAI, and OpenAI Agents SDK hide from you — with working code.

Calling one LLM provider is a single point of failure. LiteLLM lets you route between OpenAI, Claude, and Gemini with automatic fallback — here's how.

MCP servers give LLMs access to your data and tools through a standard protocol. Here are 3 patterns — tools, resources, and prompts — with working Python code.

Your CI/CD pipeline ships code. AI agents need model versioning, eval gates, and shadow deploys. These 5 patterns close the gap with working examples.

Mars Pathfinder kept rebooting itself on Mars because of a software bug. These 4 patterns from space life support systems prevent the same class of failures in