What we learnedshipping this to production.
Long-form engineering guides on inference, retrieval, agents, evals, and the failure modes teams only meet after launch. Written by the engineers who hit them, with the architecture and the numbers.
13 guides · 225 minutes of reading
At a glance
Choose a guide for the system you are building. Each guide explains implementation choices and the checks needed to assess them.
- Who this is for
- Engineers and technical leads working on AI system design, quality, and operation.
- Topics
- Retrieval
- AI agents
- Evaluation
- Inference
12 guides
- AI Infrastructure14 min readHow LLM inference worksA practical guide to the systems work behind fast, affordable LLM serving.12 Jun 2026Read the guide →
- AI Applications28 min readWhy RAG systems fail in productionMost RAG failures look like model failures until teams measure retrieval, context assembly, and generation separately.12 Jun 2026Read the guide →
- Agentic Systems16 min readHow to build production AI agentsUseful agents are not magic workers. They are constrained systems that decide, act, verify, and stop.12 Jun 2026Read the guide →
- Strategy & Governance24 min readWhy enterprise AI pilots failMost enterprise AI pilots fail for predictable, well-documented reasons. This guide walks through what the evidence says and the decisions that separate the few that reach production value from the rest.12 Jun 2026Read the guide →
- Agentic Systems10 min readWhen your AI product needs write accessA practical guide to choosing the first action an AI product can perform inside an authority boundary that engineering, product, and security can review.13 Aug 2026Read the guide →
- Agentic Systems11 min readHow to scope AI agent permissionsHow to bind one agent action to a principal, task, target, policy, credential, approval, expected state, and recovery path.13 Aug 2026Read the guide →
- Reliability & Evaluation11 min readWhy AI agents report unfinished work as doneA production guide to expected-state contracts, independent verification, idempotency, reconciliation, retry safety, partial failure, and causal audit evidence.13 Aug 2026Read the guide →
- Strategy & Governance11 min readHow to roll out AI agent autonomy in five levelsHow to move one agent action from read-only to bounded execution through explicit authority levels, exit evidence, demotion rules, and shared release ownership.13 Aug 2026Read the guide →
- AI Applications15 min readRAG vs fine-tuning vs agentic searchMost teams pick a technique and then look for the problem. Start from the failure you can observe in your logs and the choice is usually forced.1 Sep 2026Read the guide →
- Reliability & Evaluation27 min readHow to build LLM evalsYou cannot write the rubric first. Criteria emerge from reading your own failures, and everything downstream depends on doing that step properly.1 Sep 2026Read the guide →
- Reliability & Evaluation22 min readHow to build an audit trail for AI decisionsA tool trace can show what ran without explaining the accepted business decision. This guide defines the record an AI workflow needs when a case is challenged or its evidence changes.9 Sep 2026Read the guide →
- Strategy & Governance18 min readEmbedded AI engineer vs hire vs agencyEmbed a production-proven engineer when you need AI depth in your repo without a permanent headcount bet. Hire full-time when AI is a multi-year competency you can manage. Use an agency for a bounded deliverable with acceptance tests, and not for ongoing ownership inside your systems.22 Sep 2026Read the guide →
Every one of these is a problem we hit in production first.
The concepts these guides assume.
AI Applications
Retrieval-Augmented Generation (RAG)EmbeddingsChunkingVector Search & Vector DatabasesHybrid SearchRerankingLate Interaction & ColBERTGraphRAGContext WindowPrompt InjectionEvalsHallucination & GroundingFine-Tuning (SFT)LoRA & QLoRAFew-Shot & In-Context LearningStructured Output & Constrained DecodingConstrained DecodingPlan-and-ExecuteReAct (Reason + Act)LLM-as-a-JudgePrompt CachingDistillationSynthetic Training DataModel Routing & CascadingKnowledge Cutoff
Reliability & Evaluation
AI AgentTool / Function CallingEvalsLLM Observability & TracingGuardrailsComputer Use & GUI AgentsStructured Output & Constrained DecodingLLM-as-a-JudgeHallucination & GroundingRetrieval-Augmented Generation (RAG)RerankingHybrid SearchBenchmark ContaminationSynthetic Training DataPrompt InjectionFine-Tuning (SFT)Temperature & SamplingChunkingGroundedness (Faithfulness)
Strategy & Governance
EvalsHallucination & GroundingGuardrailsLLM Observability & TracingRetrieval-Augmented Generation (RAG)AI AgentPrompt InjectionModel Routing & CascadingLLM-as-a-JudgeRed-Teaming & JailbreaksFine-Tuning (SFT)Tool / Function CallingPrompt CachingData PoisoningStructured Output & Constrained DecodingModel Context Protocol (MCP)
Let's build something that ships.
Tell us what you're building. We'll tell you whether you need an engineer embedded or the whole build led, what's achievable, and where the real bottlenecks are.