Practical AI engineering, architecture decisions, lessons from production — written by the engineers who build the systems.
Long context models are impressive, but they don't eliminate the need for retrieval. Here's a practical breakdown of whe…
⚙️The debate between fine-tuning and prompt engineering is often framed as binary. It's not. We share the framework we use…
🧠Working memory, episodic memory, semantic memory, procedural memory — what they are, how to implement them, and real exa…
Long context models are impressive, but they don't eliminate the need for retrieval. Here's a practical breakd…
⚙️The debate between fine-tuning and prompt engineering is often framed as binary. It's not. We share the framew…
🧠Working memory, episodic memory, semantic memory, procedural memory — what they are, how to implement them, an…
📊MMLU and HumanEval tell you almost nothing about how a model will perform in your product. We walk through the…
⚡Speculative decoding can give you 2–4× throughput gains — but it requires careful setup. We cover draft model …
🛡️Prompt injection is the SQL injection of the AI era. We document the attack vectors we've encountered and the …
🕸️Pure vector search fails on multi-hop questions. Pure graph traversal is too rigid. We share our architecture …
💰A step-by-step walkthrough of how we reduced a client's inference costs by 96% over 6 months — through model s…
🤖There's no single right way to orchestrate multiple AI agents. We break down three architectures we've deploye…