LLM Infrastructure
LLM applications depend on more than a model API. They need reliable ingestion, context preparation, tool interfaces, and runtime choices. This collection covers the infrastructure required to move from a prototype to maintainable AI-powered software.
10 guidesLatest 26 Jul 2026
Start here
- 1The Modern Document Processing StackTurn user files into Markdown that models and embedding pipelines can read.LLM Infrastructure
- 2How to Build an MCP ServerExpose your data and actions to LLM clients through a standard protocol.AI Agents, LLM Infrastructure
- 3Decoding Methods for LLMsLearn what temperature, top k and top p do to the output distribution.LLM Infrastructure, Structured Generation
- 4Sliding-Window Prefix Caches Need Physical ProofCheck that a prefix cache hit is still valid when the serving engine reuses KV state.LLM Infrastructure
- 5Treat Reasoning Controls Like API MigrationsTest a reasoning change as a protocol migration before it reaches production traffic.AI Agents, LLM Infrastructure
More guides
- Your Agent Memory Needs Two ClocksA single timestamp cannot distinguish a late correction from a real-time state change.AI Agents, LLM Infrastructure
- Delete System-Prompt Rules One Regression at a TimeTie every prompt workaround to a failure case, then test its removal when the model changes.AI Agents, LLM Infrastructure
- The Tool List Is Not an Authorization BoundaryRemoving a tool from the model's list doesn't revoke the credentials behind it, so check permissions when the call executes.AI Agents, LLM Infrastructure
- Speculative Agents Need a Commit BoundarySpeculative execution cuts agent latency only when prediction and commitment are separate operations.AI Agents, LLM Infrastructure
- Loop / Harness EngineeringAgent loops turn prompts into one part of a production harness: tools, state, checks, approvals, and exit conditions.AI Agents, LLM Infrastructure