A 2026 text-to-SQL benchmark methodology: execution accuracy, schema linking, latency, and cost across model tiers - plus where generated SQL goes wrong.
How LLM prompt caching works in 2026: provider-side vs self-hosted KV reuse, cache-aware prompt design, hit-rate economics, and where it quietly breaks.
A 2026 architecture guide to semantic caching for LLM apps: embedding similarity lookup, cache invalidation, hit-rate tuning, and where it quietly breaks.
A production 2026 pattern for LLM output validation: constrained decoding, JSON-schema structured outputs, guardrails, and self-repair loops that actually hold.
Patterns to make LLM tool calls deterministic in production — JSON schema enforcement, validators, retries, and when constraint decoding actually pays off.
Emergent abilities in LLMs — what truly emerges with scale, what is a benchmark mirage, and what the 2026 evidence shows about emergence vs measurement.
Deep-dive into GraphRAG architecture patterns — knowledge graph construction, community detection, graph-enhanced retrieval, and when GraphRAG outperforms naive vector RAG. Benchmarks and trade-offs.