Topic Hub
LLM Engineering is the discipline of shipping production systems built on large language models — covering RAG architecture, fine-tuning strategies, model evaluation, and the practical tradeoffs that determine what gets deployed versus what stays in a notebook. These articles cover the technical decisions that matter when you move from prototype to production.
Build a RAG chatbot in Next.js with the AI SDK: embed the query, search pgvector, stream a grounded answer with citations, and stop hallucinations.
Vercel AI SDK in production: streaming, tool-calling, aborting generations, error retry UX, rate limiting, and cost control — the layer every tutorial skips.
Only 23% of autonomous AI agents reach production in 2026. The demo-to-production gap, why agents fail, and the playbook the winners actually use.
Build a RAG pipeline from scratch: chunking, embeddings, retrieval, reranking, grounded generation, and the production patterns that decide whether it works.
How to build an MCP server, step by step: JSON-RPC 2.0, the Streamable HTTP transport, typed tools, and agent discovery — from a real one I shipped.
RAG vs fine-tuning for LLMs in 2026: a practical decision framework covering architecture tradeoffs, cost, latency, and when to use each in production.
How to build enterprise-grade AI agents for free in 2026: a hands-on MaxKB + local LLM guide to RAG precision, security, and $0 API cost.
Kimi K3 vs Claude Fable 5, benchmark by benchmark: where the 2.8T open model beats Anthropic's flagship, where it loses, what it costs, and how to actually use it.
GPT-5.6 Sol vs Terra vs Luna compared on price, coding, latency, and cost per task — plus a routing strategy that cuts your bill without wrecking quality.
GPT-5.6 Sol, Terra & Luna: pricing ($1–$30/1M), benchmarks (Sol hits 91.9% on Terminal-Bench 2.1), 1.05M context, and which tier to actually use.
GPT-5.4 benchmarks, pricing, and API limits, compared to GPT-5.4 Pro and GPT-5.3-Codex — the complete developer guide with real numbers.
GPT-5.3 Instant brings 26.8% fewer hallucinations, fewer needless refusals, and better web-sourced answers — what changed and why it matters for devs.
DeepSeek V4 is expected in early March 2026. Here is what is confirmed, what remains unverified, and how it challenges U.S. AI rivals.
Anthropic exposes industrial-scale AI model theft by DeepSeek, Moonshot, and MiniMax: 16 million exchanges, 24,000 fake accounts. The forensic breakdown.
LLM Engineering is the practice of building production systems with large language models. It covers model selection, prompt design, RAG architecture, fine-tuning strategies, evaluation pipelines, and inference optimization — the full technical stack between a raw model and a working AI product.
RAG (Retrieval-Augmented Generation) fetches relevant context at inference time from an external knowledge base, making it ideal for dynamic or frequently-updated information. Fine-tuning adjusts model weights for specific tasks or communication styles and is better for consistent behavior and lower-latency responses. Most production systems combine both: fine-tuning for style and RAG for knowledge.
GPT-5.3 introduced significantly fewer refusals and better instruction following, reducing prompt engineering overhead. GPT-5.4 added expanded context windows and improved agentic tool use, making it easier to build reliable multi-step pipelines without complex fallback logic.
Local LLMs make sense for private codebases, high-volume batch tasks where API costs add up, offline workflows, and experimentation without usage limits. Models like Qwen3-Coder are viable for coding assistance on modern hardware. The tradeoff is quality: frontier models still outperform local alternatives on complex reasoning tasks.