Tag: LLM Engineering

9 articles

• LLM Model Format Conversion in Production: Avoiding Silent Degradation with Tensor Mapping, QKV Reordering, and Layer-Wise Validation

  A systematic guide to parameter mapping, QKV reordering, shard indexing, layer-wise numerical validation, and rollback gates when converting LLM weights between Hugging Face, Megatron, and TensorRT-LLM. Build a repeatable, auditable release pipeline to prevent silent degradation, output drift, and parallel slicing errors.

  07/22/2026 LLM Engineering / Model Conversion / Megatron / TensorRT-LLM / Safetensors

• LLM Unicode Output Governance in Production: Incremental UTF-8 Decoding, Grapheme Clusters, and NFC Boundary Buffering to Prevent Garbled Text and Truncation

  A systematic guide for streaming LLM applications covering three layers of boundaries—network bytes, API deltas, and user-visible characters—with solutions for incremental UTF-8 decoding, extended grapheme cluster buffering, NFC normalization, and replay testing to avoid garbled text, emoji splitting, and index mismatches.

  07/13/2026 LLM Engineering / Unicode / Streaming / Text Processing / Character Encoding / Grapheme Cluster

• LLM Tokenizer Version Governance in Production: Preventing Cost Surprises with Compatibility Fingerprints, Token Drift Replay, and Budget Gates

  Tokenizer changes can silently shift token counts, truncation boundaries, and costs for the same prompt. This article provides practical solutions including compatibility fingerprints, golden corpus replay, dual counting, and budget gates to help teams identify risks before model upgrades and safely roll out changes.

  07/11/2026 LLM Engineering / Tokenizer / Model Migration / Cost Governance

• LLM Video Input Pipeline in Production: Frame Sampling, Timestamp Alignment, and Media Token Budgeting for Multimodal Cost Control

  Video understanding is more than just passing extra images. This article systematically explains how to build a production-grade LLM video input pipeline, covering file admission, decoding probe, frame sampling, timestamp alignment, media token budgeting, caching, quality evaluation, and common pitfalls.

  07/10/2026 Multimodal LLM / Video Understanding / Media Token / LLM Engineering / Cost Optimization / Frame Sampling / Timestamp Alignment

• LLM Batch API Offline Inference Production Guide: Reducing Costs with JSONL Queues, custom_id, and Result Backfill

  This article explains how to transform non-real-time LLM tasks into a recoverable Batch API offline pipeline, covering JSONL queues, custom_id, status polling, error backfill, result reconciliation, cost budgeting, and a go-live checklist to help teams reduce costs and improve engineering reliability for large-scale LLM tasks.

  07/07/2026 LLM Batch API / Offline Inference / Cost Optimization / LLM Engineering

• Voice Agent in Production: Stabilizing Speech LLM Experiences with Streaming ASR, VAD, and Segmented Context

  A deep dive into the real-time pipeline for speech LLM applications from an engineering perspective, covering how to coordinate streaming ASR, VAD, segmented context, LLM inference, and TTS to reduce end-to-end latency and maintain semantic boundaries in long conversations.

  07/02/2026 Voice Agent / Streaming ASR / VAD / LLM Engineering / Realtime AI / Speech LLM

• 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

• Structured Outputs in Production: Stabilizing JSON Schema Generation with Constrained Decoding

  A systematic guide to Structured Outputs and Constrained Decoding: core principles, schema design, server integration, performance, error handling, and common pitfalls for reliable LLM JSON generation.

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

• PagedAttention in Action: Why KV Cache Scheduling Matters More Than Compute for LLM Serving

  A deep dive into KV Cache memory fragmentation, PagedAttention, continuous batching, and prefix caching — and how they boost throughput and reduce latency in LLM inference.

  06/29/2026 LLM Inference / PagedAttention / KV Cache / vLLM / LLM Engineering