12 posts
Context engineering in 2026: how to select and structure LLM context to avoid the lost-in-the-middle effect, token budget inflation, and hallucinations.
How to manage the lifecycle of LLM applications in 2026: versioning prompts and models together, evaluation gate before every deploy, and rollback in under a minute.
LoRA and QLoRA fine-tuning in 2026: hardware requirements, dataset size, workflow from data to adapter deployment, and realistic cost ranges. Check if it's the right decision.
LLM-as-a-judge in 2026: when automated quality assessment works, what systematic biases it introduces, and how to calibrate the judge before trusting it with production decisions.
LLM FinOps for 2026: track cost per token, function, and user, uncover hidden expenses, and choose caching, routing, and budgets that actually cut the bill.
LLM red teaming in 2026: how to build an attack catalog, score vulnerabilities, and close them in a continuous regression testing loop before someone else does.
Why token-by-token streaming reduces perceived latency and how to correctly build an SSE pipeline with backpressure, guardrails, and observability in 2026.
How to choose an LLM model for the task in 2026: task-model matrix, size-cost-latency trade-offs, and a router directing work to the cheapest viable model.
Data leaks via LLM in 2026: attack vectors and defense — PII masking before the model, guardrails, self-hosting, zero-retention, and audit. Decide what you need.
OWASP LLM Top 10 outlines 10 vulnerability classes in large language models. How each manifests in production systems and how to build layered defenses.
LLMs detect patterns humans wouldn’t spot in a month. But without guardrails, explainability, and human-gate, hypotheses instead of accelerating work generate verification debt.
Small AI model vs large LLM: when a specialized 7B outperforms a general GPT-4-class model, the cost difference, and how to choose correctly for your business.
