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.
Ai
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.
LLM Guardrails in 2026 — Input Filtering, Output Validation, and Safety Nets
Production guardrail patterns: input filters, output validators, prompt injection defenses, PII redaction, and how to compose guardrails without killing latency.
Embedding Databases in 2026 — pgvector, Qdrant, Weaviate, Milvus, Pinecone
Picking a vector store: pgvector for most apps, Qdrant for self-host at scale, Pinecone for managed simplicity, Milvus for billion-row workloads, Vectorize for edge.