Building a Production RAG Pipeline: From Documents to Answers
A technical guide to chunking, retrieval, re-ranking, and observability for RAG systems at scale
AI Explainers & Guides
Cornerstone guides on the topics that matter for production AI. We publish a new explainer every week.
AI coding agents in 2026: What actually works in production
A technical breakdown of Copilot, Cursor, and competing tools for real codebases, with honest limits.
Embedding Models Compared: OpenAI, Cohere, Open Source 2026
Choose the right text embeddings for semantic search and RAG with benchmark data, cost analysis, and latency trade-offs
How to Reduce LLM Hallucinations: RAG, Grounding, and Evaluation
A production-focused guide to fixing made-up facts in customer-facing language models
Prompt Engineering in 2026: Patterns That Work in Production
The system prompts, few-shot techniques, and evaluation loops that ship in real LLM applications
Best AI image generators in 2026: Midjourney, DALL-E, Stable Diffusion, Flux
A technical comparison of four leading models: quality, pricing, licensing, and which to pick for your workflow.
LLM API pricing compared in 2026: which provider costs least
Per-token pricing, context windows, and batch discounts across OpenAI, Anthropic, Google, and newer challengers.
AI Transcription Pricing 2026: Whisper vs Deepgram vs AssemblyAI
Compare per-minute rates, free tiers, and accuracy across the top speech-to-text APIs.
Vector Databases for RAG: Pinecone, pgvector, Chroma, and How to Choose
A technical guide to selecting the right embeddings index for retrieval-augmented generation systems.
What Is the Model Context Protocol? A Developer's Guide
How MCP standardizes connections between LLMs and external tools, data sources, and APIs.
FastAPI and Starlette Security for AI Agents: Production Hardening
Vulnerabilities, patches, and a hardening checklist for Python teams deploying AI agent backends.
How Large Language Models Work: A Technical Explainer
Understand tokens, attention, and next-token prediction without the PhD-level math.
GPT-5 vs Claude 4.5 vs Gemini Ultra: Choose the Right LLM
A technical comparison for production teams selecting a frontier model in 2026
What Are AI Agents? A Practical Guide for Builders in 2026
How autonomous agents differ from chatbots, what frameworks actually work, and where they reliably fail.
Fine-tuning vs RAG vs Prompt Engineering: A Decision Framework
When to customize an LLM: cost, latency, and accuracy tradeoffs explained for engineering leaders.
What is Retrieval-Augmented Generation (RAG)? A 2026 Guide
How RAG fixes LLM hallucinations by letting models fetch real data before generating answers.
Coming soon
- LLM context windows explained: tokens, limits, and long-context trade-offs
- LLM tool use and function calling: a builder's guide
- Fine-tuning LLMs with LoRA: when it's worth it and how to do it safely
- Local LLMs vs cloud APIs: cost, privacy, latency, and quality trade-offs
- Best open-source LLMs in 2026: Llama, Mistral, Qwen, and deployment notes
- Semantic search with embeddings: architecture, pitfalls, and evaluation