LLM Batch Processing in 2026 — Anthropic / OpenAI Batch API for 50% Off
Practical LLM batch processing: when 24-hour latency is fine, queueing patterns, retry logic, error handling, and integrating batches with online apps.
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LLM Deployment Patterns in 2026 — Inference Servers, Routing, and Production Architectures
Practical LLM deployment: vLLM / TGI for self-hosted, hybrid (API + local), routing layers, autoscaling GPUs, fallbacks, and serving cost economics.
Prompt Engineering in 2026 — What Still Works, What Doesn't, and What Changed
Modern prompt engineering: instruction clarity, structured prompts, few-shot vs zero-shot, role tags, and the patterns that survive model upgrades.
LLM Agent Frameworks in 2026 — LangGraph, CrewAI, and the Bare-Metal Alternative
Honest agent framework comparison: LangGraph for stateful workflows, CrewAI for multi-agent, OpenAI Agents SDK, and where 200 lines of Python beats them all.
Agent Memory Systems in 2026 — Episodic, Semantic, and the Patterns That Stick
Practical agent memory: working memory in the prompt, episodic memory in append-only stores, semantic memory in vector DBs, and how to compose them.
LLM Context Windows in 2026 — Long Context, Cache, and the Limits of 'Just Add More'
Practical long-context: when more context helps vs hurts, the lost-in-the-middle problem, caching strategies, retrieval as the better default, and 1M-context economics.
Multimodal LLMs in 2026 — Vision, Audio, and What's Actually Useful
Practical multimodal: vision-aware document understanding, audio transcription + reasoning, image-from-text, video understanding, and where multimodal pays off.
Evaluating RAG Systems in 2026 — Retrieval Quality, Faithfulness, and the Metrics That Matter
How to actually evaluate RAG: retrieval recall and MRR, answer faithfulness and relevance, golden datasets, automated eval pipelines, and Ragas.
LLM Observability in 2026 — Tracing, Evals, and the Things You Can't Skip
Practical LLM observability: tracing every call, eval harnesses, regression detection, prompt versioning, and how to debug the model in production.
LLM Cost Optimization in 2026 — From Bills That Hurt to Bills That Don't
Practical LLM cost cuts: prompt caching, model routing, batch APIs, structured output, fine-tunes for high-volume narrow tasks, and cache hierarchies.