production-ai
7 free lessons tagged production-ai across AI. Each one is a short sequence of focused steps with narration and a five-question quiz at the end — take them in any order, no signup required.
The Options Nobody Compares, and Changing Your Mind
The fine-tuning versus retrieval framing hides several options that are often better than either. This lesson covers long context and why it does not replace retrieval, prompt caching as a cost lever, agentic retrieval, continued pretraining, and how to revisit a decision that was right when you made it.
Building Both: LoRA, Data, and the Retrieval Pipeline
The decision is only half the work. This lesson covers what building each actually involves: parameter-efficient fine-tuning with LoRA and why it made the technique accessible, the training data problem that stalls most projects, the retrieval pipeline end to end, and how to combine them into one system.
What Each Technique Actually Changes
Fine-tuning and retrieval are usually presented as alternatives when they change different things. This lesson separates them precisely: fine-tuning adjusts weights and teaches behaviour, retrieval supplies context and teaches nothing, and the question that decides between them is whether your problem is knowledge or form.
Hybrid Search, Tuning, and Running It in Production
Dense vectors miss exact terms, so production retrieval combines them with lexical search. This lesson covers hybrid retrieval and reciprocal rank fusion, a tuning method that starts from a stated recall target, capacity planning, the operational failures that catch teams, and how to choose between running your own index and buying a service.
Abstention: Building a System That Can Say It Does Not Know
The most valuable behaviour an AI system can have is refusing to answer when it should not. This lesson covers calibration and why stated confidence is unreliable, selective prediction and the coverage-accuracy trade, conformal methods that give guarantees, designing abstention users accept, and measuring all of it in production.
Building the Control Layer: Rails, Classifiers, and Containment
Guardrails are a layered control system around a model that cannot police itself. This lesson covers the rail types, rules versus classifiers versus model-based judges, the tools that implement them, the latency and false-positive budget that constrains every design, and why architectural containment beats filtering.
RAG Evaluation in Production: Metrics, Tools, and Cadence
Learn how to systematically evaluate Retrieval-Augmented Generation systems in production using RAGAS, TruLens, and Phoenix — covering golden sets, retrieval drift, embedding drift, and cost-aware eval scheduling.