8 min read
Cheap → Expensive LLM Routing: How to Cut AI Costs by 70%
Learn how to implement a routing layer to dispatch LLM requests to the cheapest capable model, reducing costs by up to 70% without sacrificing quality.
Blog
Systems. Software. Clear Thinking.
17 min
Debugging AI Systems (Your First Real Struggle)
When something goes wrong in a multi-component AI system, where do you start? Tracing prompt-to-output, identifying failure source, structured logging, and the systematic method that beats guessing every time.
16 min
Intro to AI System Design
Pipelines vs agents, orchestration patterns, when orchestration is the wrong abstraction — the architectural decisions that determine whether your AI system is maintainable or a debugging nightmare.
16 min
From Toy to Production: What Breaks First
The systems that work perfectly in staging fail in production for reasons that are never about the model. Rate limits, inconsistent outputs at scale, state management, and graceful degradation — what actually breaks and how to engineer around it.
15 min
Cost & Latency: The First Production Pain
Token usage analysis, model selection, caching strategies, and the math that decides whether your AI feature is economically viable at scale.
16 min
Evaluating LLM Outputs (Your First Eval System)
Building the evaluation infrastructure that lets you know if your AI system is actually working — test datasets, scoring criteria, automation, and a continuous loop that catches regressions before users do.
17 min
Building Your First Real RAG System
Chunking strategies, top-k tuning, context window management, and the noise problem — how to move from a retrieval pipeline that sometimes works to one that works reliably.
16 min
Intro to RAG: Making AI Use Your Data
Embeddings, vector databases, the store-retrieve-inject pipeline, and the first real failure mode: irrelevant retrieval. What RAG is, why you need it, and how to build a version that actually works.
15 min
Why Your LLM App Hallucinates (and How to Reduce It)
Hallucination is not a bug in the model — it is an intrinsic property of probabilistic text generation. Here is what causes it, what your reliability layer cannot catch, and what you actually build to mitigate it.
16 min
Making LLM Output Reliable
JSON mode, schema enforcement, validation pipelines, and retry strategies — the complete reliability layer that sits between the model and your downstream systems.
17 min
Prompt Engineering That Actually Matters
The gap between a prompt that works sometimes and one that works reliably. Structured prompt design, system vs user roles, output schemas, and using examples — with concrete before/after comparisons on the same task.
16 min
First LLM App: From API Call to Working Feature
Building a real text summarizer API from scratch — handling latency, malformed responses, retries, and the gap between 'it works locally' and a feature you can actually ship.
18 min
What AI Engineering Actually Means (and What It Is Not)
A complete mental model for reasoning about AI systems in production — covering architecture, reliability, evaluation, and the layers most engineers skip when they call it done after the first API response.