Tag: #ai-development
Writing on GPUs, LLMs, MLOps, Kubernetes — and mindset · 22 posts
22 posts are tagged #ai-development; the most recent was published on 2026-05-14.
Often tagged together: #rag 8 #llm 7 #ai-agent 6 #career-strategy 2 #claude 2
Most read in the last 90 days:
The Complete Guide to AI Development Automation — GitHub Integration, Ticket-Based Agentic Workflows, Copilot, Claude Code, Devin, Jules (2025) ♪ Listenable
2025 was the year AI moved from 'suggesting code' to 'owning the ticket.' Assign a GitHub Issue to an AI agent and it rolls forward on its own — branch creation, implementation, PR, passing CI. A 4-level maturity model,
2026-05-14 · 29 min read #ai-development#automation#github#copilot#claude-codeVector Database Comparison 2025: Pinecone vs Weaviate vs Chroma vs pgvector
A practical 2025 comparison of Pinecone, Weaviate, Chroma, and pgvector across performance, cost, and operational complexity. Includes real code examples and a decision matrix to help you pick the right vector database f
2026-03-18 · 8 min read #vector-database#pinecone#weaviate#chroma#pgvectorRAG vs Fine-tuning vs Prompt Engineering: The Complete Decision Framework
A practical comparison of RAG, fine-tuning, and prompt engineering from a production engineering perspective, with a concrete decision framework, real cost calculations, and honest gotchas from building these systems in
2026-03-18 · 7 min read #rag#fine-tuning#prompt-engineering#llm#ai-developmentModel Context Protocol (MCP) Complete Guide: The New Standard for AI-World Integration
Anthropic's MCP is the USB-C of AI tool integration — write your server once, use it with any AI client. This guide covers MCP architecture, building a Python server from scratch, and honest comparisons with OpenAI funct
2026-03-18 · 7 min read #mcp#model-context-protocol#anthropic#claude#ai-agentPractical LLM API Cost Optimization: How to Cut Costs by 90%
Five battle-tested strategies to slash LLM API costs — prompt caching (90% reduction), model routing, semantic caching, Batch API, and output optimization — all with production-ready code and real cost calculations.
2026-03-18 · 12 min read #llm-cost-optimization#api-cost#prompt-caching#model-routing#ai-developmentEmbedding Model Selection Guide 2025: From OpenAI to Open-Source Options
A practical 2025 comparison of the major embedding models — OpenAI, Cohere, BGE-M3, E5-mistral, nomic-embed — with MTEB benchmark analysis, language-specific recommendations, and a decision framework for choosing the rig
2026-03-18 · 9 min read #embedding#embedding-model#rag#ai-development#openaiAI Startup vs Big Tech AI Team: Where Should You Work to Truly Grow?
AI startup vs big tech AI team — which leads to faster growth? An honest comparison covering compensation, experience, growth pace, and the unique dynamics of the Korean AI ecosystem.
2026-03-18 · 7 min read #ai-career#ai-startup#big-tech#career-strategy#ai-developmentTool Calling in Practice: How AI Interacts with the World and Common Pitfalls
Tool calling is what transforms an LLM from a text transformer into an agent that can actually do things. Full implementation of OpenAI function calling and Claude tool use, parallel execution patterns, and the five mist
2026-03-18 · 9 min read #tool-calling#function-calling#ai-agent#llm#ai-developmentRAG Chunking Strategies: From Naive Splitting to RAPTOR
Chunking determines 70% of your RAG system quality. This guide covers 5 chunking strategies with real code — from simple fixed-size splitting to Semantic Chunking, Parent-Child, and RAPTOR — with honest assessments of wh
2026-03-18 · 8 min read #rag#chunking#text-splitting#langchain#raptorMulti-Agent Systems Compared: AutoGen vs CrewAI vs LangGraph — Which Should You Choose?
AutoGen, CrewAI, LangGraph — a practical comparison of three multi-agent frameworks with real code. Learn which one fits your use case, what each gets wrong, and the production pitfalls that apply regardless of which fra
2026-03-18 · 9 min read #multi-agent#autogen#crewai#langgraph#ai-agentLLM Agent Design Patterns: From ReAct to Multi-Agent Orchestration
ReAct, Chain of Thought, Plan-and-Execute, Reflection, Tree of Thoughts — a practical breakdown of which LLM agent design pattern to use and when, from an engineer who has built them in production.
2026-03-18 · 9 min read #ai-agent#react#llm#design-patterns#ai-developmentHybrid Search Guide: Combining BM25 and Vector Search for Better RAG
Pure vector search has a blind spot: exact keyword matching. This guide explains how to combine BM25 and vector search using RRF (Reciprocal Rank Fusion) to get the best of both worlds — with real code, benchmark numbers
2026-03-18 · 7 min read #hybrid-search#bm25#vector-search#rag#rrfAgent Memory System Design: Everything About How AI Agents Remember
LLMs have no memory by default. How do we solve this fundamental limitation? From four memory types to production architecture and the Mem0 framework — a complete guide to agent memory systems.
2026-03-18 · 8 min read #agent-memory#ai-agent#long-term-memory#langchain#mem0Vibe Coding: The New Paradigm of AI-Assisted Development — Potential and Pitfalls
A deep dive into Andrej Karpathy's vibe coding concept from 2025. Is it a genuine revolution, or a technical debt machine? An honest look based on real experience.
2026-03-18 · 7 min read #vibe-coding#ai-coding#cursor#github-copilot#ai-developmentRAGAS Complete Guide: How to Quantitatively Evaluate Your RAG System
Learn to use RAGAS — the gold standard for RAG evaluation — to measure Faithfulness, Answer Relevancy, Context Precision, and Context Recall. Includes complete Python code, automated CI/CD evaluation pipelines, synthetic
2026-03-18 · 9 min read #ragas#rag-evaluation#llm-evaluation#ai-development#qualityPrompt Engineering 2025: Getting Maximum Performance from Modern LLMs
Even the best model gives poor results with a poor prompt. Six prompt engineering techniques proven in production, with working code for GPT-4o, Claude, and open-source models.
2026-03-18 · 7 min read #prompt-engineering#llm#ai-development#gpt-4o#claudeLLM Hallucination: Why AI Makes Things Up and 5 Strategies to Prevent It
A technical deep-dive into why LLMs hallucinate, covering all four hallucination types and five practical prevention strategies including RAG, self-critique pipelines, Chain of Verification, and temperature tuning — all
2026-03-18 · 9 min read #hallucination#llm#ai-reliability#ai-development#rag1 Million Token Context Windows: Is RAG Becoming Obsolete?
With Gemini 1.5 Pro's 1M token context and Claude's 200K, many developers ask: why bother with RAG? We compare real costs, latency, and quality to give you an honest answer.
2026-03-18 · 6 min read #context-window#rag#llm#long-context#ai-developmentGraphRAG Complete Guide: How Knowledge Graphs Overcome RAG Limitations
Standard RAG fails on global queries like "summarize the key risk themes across all these documents." We walk through how Microsoft Research's GraphRAG solves this, with real code and honest cost analysis.
2026-03-18 · 8 min read #graphrag#knowledge-graph#rag#microsoft#ai-developmentFine-tuning in Practice: Building Your Own Model with LoRA and QLoRA
You don't need 7 H100s. With LoRA and QLoRA, you can fine-tune a 70B model on a single consumer GPU. A practical end-to-end guide with working code.
2026-03-18 · 7 min read #fine-tuning#lora#qlora#llm#ai-development