Articles

Searchable, citable, durable content on LLMs and AI agents. 147 articles, page 12 / 15.

• Speculative Decoding in Production: Reducing LLM Decode Latency with Draft-Verify

  A systematic guide to Speculative Decoding's Draft-Verify mechanism, covering vLLM and TensorRT-LLM integration, metric tuning, applicability boundaries, and production deployment checks to reduce decode latency and avoid overhead from low acceptance rates.

  07/01/2026 LLM / Inference Optimization / Speculative Decoding / LLM Serving / Engineering Practice

• KV Cache Quantization in Production: Reducing Long-Context Inference Memory with FP8/INT8

  A systematic guide to KV cache quantization for long-context LLM inference: value, principles, vLLM/TensorRT-LLM implementation, evaluation metrics, and production risks to help teams safely reduce memory and latency costs.

  07/01/2026 LLM / Inference Optimization / KV Cache / Quantization / LLMOps / vLLM / TensorRT-LLM

• Prefill-Decode Disaggregation in Production: Separating Prefill and Decode to Reduce TTFT/TPOT Conflicts

  A systematic guide to disaggregating LLM prefill and decode phases. Covers the root cause of TTFT vs TPOT conflicts in long-context scenarios, KV cache transfer, dynamic routing, resource allocation, monitoring metrics, and a production launch checklist.

  07/01/2026 LLM / Inference Acceleration / LLM Serving / KV Cache / Engineering Practice

• Structured Outputs in Production: Making JSON Schema Truly Usable with Constrained Decoding

  A systematic guide to implementing structured outputs in LLM engineering, covering JSON Schema, constrained decoding, validation and retry, performance overhead, monitoring metrics, and common pitfalls to reduce parse failures and business risks.

  07/01/2026 LLMs / Structured Outputs / JSON Schema / Constrained Decoding / Engineering Practices

• Agent Tool Calling Security: Defending Against Prompt Injection and Tool Poisoning with Permission Boundaries

  A systematic guide to securing LLM Agent tool calls against Prompt Injection, Tool Poisoning, and privilege escalation. Covers trust boundaries, permission boundaries, and execution boundaries with practical implementations including policy gates, human approval, sandboxing, and full-stack monitoring.

  07/01/2026 LLM Agent / Prompt Injection / Tool Calling / AI Security / MCP

• LLM-as-a-Judge in Production: How to Calibrate Automated Evaluation Without Treating the Model as an Oracle

  A systematic guide to deploying LLM-as-a-Judge for evaluating LLM applications in production, covering dataset construction, rubric design, bias calibration, metric monitoring, and human-in-the-loop review.

  07/01/2026 LLM / LLM Evaluation / LLM-as-a-Judge / Engineering Practice / LLMOps

• Continuous Batching in Production: Keeping LLM Services GPU-Saturated Under High Concurrency

  A systematic guide to continuous batching for LLM serving: core principles, scheduling strategies, KV Cache constraints, engineering deployment steps, and monitoring metrics. Covers the full path from proof-of-concept to production, helping teams boost throughput while controlling tail latency in high-concurrency scenarios.

  07/01/2026 LLM Serving / Continuous Batching / vLLM / KV Cache / Performance Engineering / Inference Optimization / GPU Scheduling

• Speculative Decoding in Production: Reducing LLM Latency with Draft Models

  A systematic guide to Speculative Decoding for LLM inference: principles, engineering trade-offs, model selection, monitoring metrics, and common pitfalls. Learn when and how to deploy draft models for latency reduction.

  06/30/2026 LLM / Inference Optimization / Speculative Decoding / LLMOps / Performance Engineering

• Structured Output in Production: Using Constrained Decoding to Make LLMs Return Reliable JSON

  A practical guide to structured output and constrained decoding for LLMs. Learn how to use JSON Schema, strict mode, post-hoc validation, retry strategies, and production monitoring to ensure stable, parseable structured results for information extraction, agent state management, and automated workflows, reducing online risks at the source.

  06/30/2026 LLM / Structured Output / Constrained Decoding / JSON Schema / LLM Engineering

• Context Engineering in Production: Treating the Agent Context Window as a Resource to Schedule

  Context engineering isn't about cramming data into a window. It's about dynamically selecting instructions, memory, tool results, and retrieval chunks around a task goal, keeping agents reliable, controllable, and cost-effective in long-running tasks.

  06/30/2026 LLM Agent / Context Engineering / AI Engineering Practices / Agent Memory