LLM JSON Mode: A Structured-Output Benchmark (2026)
A 2026 benchmark of LLM JSON mode and constrained decoding: throughput, latency, and accuracy across grammar-based methods, with reproducible methodology.
Text-to-SQL LLM Benchmark: Accuracy and Latency (2026)
A 2026 text-to-SQL benchmark methodology: execution accuracy, schema linking, latency, and cost across model tiers - plus where generated SQL goes wrong.
LLM Prompt Caching: Architecture and Economics (2026)
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.
Semantic Caching for LLM Applications: Architecture (2026)
A 2026 architecture guide to semantic caching for LLM apps: embedding similarity lookup, cache invalidation, hit-rate tuning, and where it quietly breaks.
Long-Running Governed AI Agents: Architecture (2026)
Architecture patterns for long-running, governed AI agents in 2026: durable execution, checkpointing, guardrails, and human-in-the-loop control.
LLM Output Validation: Structured Outputs & Guardrails
A production 2026 pattern for LLM output validation: constrained decoding, JSON-schema structured outputs, guardrails, and self-repair loops that actually hold.
LLM Tool Calling Determinism: Production Patterns That Work (2026)
Patterns to make LLM tool calls deterministic in production — JSON schema enforcement, validators, retries, and when constraint decoding actually pays off.
LLM Tokenization Deep Dive: BPE, SentencePiece, Tiktoken (2026)
How LLM tokenizers really work — BPE, SentencePiece, Tiktoken, vocab design, multilingual gotchas, and why your token count drives your bill.
Emergent Abilities in LLMs: What Scales, What’s a Mirage (2026)
Emergent abilities in LLMs — what truly emerges with scale, what is a benchmark mirage, and what the 2026 evidence shows about emergence vs measurement.
GraphRAG Architecture Patterns: Building Knowledge-Graph-Enhanced Retrieval for Enterprise LLM Applications
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.